Abstract: In the maximum cover problem, we are given a collection of
sets over a ground set of elements and a positive integer w, and we are
asked to compute a collection of at most w sets whose union contains the
maximum number of elements from the ground set. This is a fundamental
combinatorial optimization problem with applications to resource allo-
cation. We study the simplest APX-hard variant of the problem where
all sets are of size at most 3 and we present a 6=5-approximation algo-
rithm, improving the previously best known approximation guarantee.
Our algorithm is based on the idea of ¯rst computing a large packing of
disjoint sets of size 3 and then augmenting it by performing simple local
improvements.

Abstract: In the maximum cover problem, we are given a collection of
sets over a ground set of elements and a positive integer w, and we are
asked to compute a collection of at most w sets whose union contains the
maximum number of elements from the ground set. This is a fundamental
combinatorial optimization problem with applications to resource allo-
cation. We study the simplest APX-hard variant of the problem where
all sets are of size at most 3 and we present a 6=5-approximation algo-
rithm, improving the previously best known approximation guarantee.
Our algorithm is based on the idea of ¯rst computing a large packing of
disjoint sets of size 3 and then augmenting it by performing simple local
improvements.

Abstract: Core networks of the future will have a
translucent and eventually transparent optical
structure. Ultra-high-speed end-to-end connectiv-
ity with high quality of service and high reliability
will be realized through the exploitation of opti-
mized protocols and lightpath routing algorithms.
These algorithms will complement a flexible con-
trol and management plane integrated in the
proposed solution. Physical layer impairments
and optical performance are monitored and
incorporated in impairment-aware lightpath rout-
ing algorithms. These algorithms will be integrat-
ed into a novel dynamic network planning tool
that will consider dynamic traffic characteristics,
a reconfigurable optical layer, and varying physi-
cal impairment and component characteristics.
The network planning tool along with extended
control planes will make it possible to realize the
vision of optical transparency. This article pre-
sents a novel framework that addresses dynamic
cross-layer network planning and optimization
while considering the development of a future
transport network infrastructure.

Abstract: We here present the Forward Planning Situated Protocol (FPSP), for scalable, energy efficient and fault tolerant data propagation in situated wireless sensor networks. To deal with the increased complexity of such deeply networked sensor systems, instead of emphasizing on a particular aspect of the services provided, i.e. either for low-energy periodic, or low-latency event-driven, or high-success query-based sensing, FPSP uses two novel mechanisms that allow the network operator to adjust the performance of the protocol in terms of energy, latency and success rate on a per-task basis. We emphasize on distributedness, direct or indirect interactions among relatively simple agents, flexibility and robustness.
The protocol operates by employing a series of plan & forward phases through which devices self-organize into forwarding groups that propagate data over discovered paths. FPSP performs a limited number of long range, high power data transmissions to collect information regarding the neighboring devices. The acquired information, allows to plan a (parameterizable long by {\"e}) sequence of short range, low power transmissions between nearby particles, based on certain optimization criteria. All particles that decide to respond (based on local criteria) to these long range transmissions enter the forwarding phase during which information is propagated via the acquired plan. Clearly, the duration of the forwarding phases is characterized by the parameter {\"e}, the transmission medium and the processing speed of the devices. In fact the parameter {\"e} provides a mechanism to adjust the protocol performance in terms of the latency--energy trade-off. By reducing {\"e} the latency is reduced at the cost of spending extra energy, while by increasing {\"e}, the energy dissipation is reduced but the latency is increased.
To control the success rate--energy trade-off, particles react locally on environment and context changes by using a set of rules that are based on response thresholds that relate individual-level plasticity with network-level resiliency, motivated by the nature-inspired method for dividing labor, a metaphor of social insect behavior for solving problems [1]. Each particle has an individual response threshold {\`E} that is related to the "local" density (as observed by the particle, [2]); particles engage in propagation of events when the level of the task-associated stimuli exceeds their thresholds. Let s be the intensity of a stimulus associated with a particular sensing task, set by the human authorities. We adopt the response function T_{{\`e}}(s) = s^{n}over s^{n} + {\`e}^{n}, the probability of performing the task as a function of s, where n > 1 determines the steepness of the threshold. Thus, when {\`e} is small (i.e. the network is sparse) then the response probability increases; when s increases (i.e. for critical sensing tasks) the response probability increases as well.
This role-based approach where a selective number of devices do the high cost planning and the rest of the network operates in a low cost state leads to systems that have increased energy efficiency and high fault-tolerance since these long range planning phases allow to bypass obstacles (where no sensors are available) or faulty sensors (that have been disabled due to power failure or other natural events).

Abstract: .We present a new methodology for computing approximate
Nash equilibria for two-person non-cooperative games based upon certain
extensions and specializations of an existing optimization approach pre-
viously used for the derivation of xed approximations for this problem.
In particular, the general two-person problem is reduced to an inde-
nite quadratic programming problem of special structure involving the
n x n adjacency matrix of an induced simple graph specied by the in-
put data of the game, where n is the number of players' strategies.

Abstract: We design and implement a multicost impairment- aware routing and wavelength assignment algorithm for online traffic. In transparent optical networks the quality of a transmission degrades due to physical layer impairments. To serve a connection, the proposed algorithm finds a path and a free wavelength (a lightpath) that has acceptable signal quality performance by estimating a quality of transmission measure, called the Q factor. We take into account channel utilization in the network, which changes as new connections are established or released, in order to calculate the noise variances that correspond to physical impairments on the links. These, along with the time invariant eye impairment penalties of all candidate network paths, form the inputs to the algorithm. The multicost algorithm finds a set of so called non-dominated Q paths from the given source to the given destination. Various objective functions are then evaluated in order to choose the optimal lightpath to serve the connection. The proposed algorithm combines the strength of multicost optimization with low execution time, making it appropriate for serving online connections.

Abstract: Clustering is a crucial network design approach to enable large-scale wireless sensor networks (WSNs) deployments. A large variety of clustering approaches has been presented focusing on different performance metrics. Such protocols usually aim at minimizing communication overhead, evenly distributing roles among the participating nodes, as well as controlling the network topology. Simulations on such protocols are performed using theoretical models that are based on unrealistic assumptions like the unit disk graph communication model, ideal wireless communication channels and perfect energy consumption estimations. With these assumptions taken for granted, theoretical models claim various performance milestones that cannot be achieved in realistic conditions. In this paper, we design a new clustering protocol that adapts to the changes in the environment and the needs and goals of the user applications. We address the issues that hinder its performance due to the real environment conditions and provide a deployable protocol. The implementation, integration and experimentation of this new protocol and it's optimizations, were performed using the \textsf{WISEBED} framework. We apply our protocol in multiple indoors wireless sensor testbeds with multiple experimental scenarios to showcase scalability and trade-offs between network properties and configurable protocol parameters. By analysis of the real world experimental output, we present results that depict a more realistic view of the clustering problem, regarding adapting to environmental conditions and the quality of topology control. Our study clearly demonstrates the applicability of our approach and the benefits it offers to both research \& development communities.

Abstract: Optical network design problems fall in the broad
category of network optimization problems. We give a short
introduction on network optimization and general algorithmic
techniques that can be used to solve complex and difficult
network design problems. We apply these techniques to address
the static Routing and Wavelength Assignment problem that is
related to planning phase of a WDM optical network. We present
simulation result to evaluate the performance of the proposed
algorithmic solution.

Abstract: For a number of optimization problems on random graphs
and hypergraphs, e.g., k-colorings, there is a very big gap between the
largest average degree for which known polynomial-time algorithms can
find solutions, and the largest average degree for which solutions provably
exist. We study this phenomenon by examining how sets of solutions
evolve as edges are added.We prove in a precise mathematical sense that,
for each problem studied, the barrier faced by algorithms corresponds
to a phase transition in the problems solution-space geometry. Roughly
speaking, at some problem-specific critical density, the set of solutions
shatters and goes from being a single giant ball to exponentially many,
well-separated, tiny pieces. All known polynomial-time algorithms work
in the ball regime, but stop as soon as the shattering occurs. Besides
giving a geometric view of the solution space of random instances our
results provide novel constructions of one-way functions.

Abstract: About this book
This state-of-the-art survey features papers that were selected after an open call following the International Dagstuhl Seminar on Algorithmic Methods for Railway Optimization held in Dagstuhl Castle, Germany, in June 2004. The second part of the volume constitutes the refereed proceedings of the 4th International Workshop on Algorithmic Methods and Models for Optimization of Railways held in Bergen, Norway, in September 2004.
The volume covers algorithmic methods for analyzing and solving problems arising in railway optimizations, with a special focus on the interplay between railway and other public transportation systems. Beside algorithmics and mathematical optimization, the relevance of formal models and the influence of applications on problem modeling are also considered. In addition, the papers address experimental studies and useful prototype implementations.
The 17 full papers presented here were carefully reviewed and selected from numerous submissions and are organized into topical sections covering network and line planning, timetabling and timetable information, rolling stock and crew scheduling, and real-time operations.

Abstract: We conduct an experimental study for the timetabling problem in a public railway network under disruptions. We investigate three bicriteria optimization problems introduced recently that model the robustness of a timetable towards delays. We experimentally evaluated these models against various waiting time rules at stations. Our results constitute the rst proofs-of-concept for these new robust timetabling approaches.

Abstract: In this contribution instances of a problem introduced by the differential cryptanalysis of Feistel cryptosystems are formulated as optimization tasks. The performance of Evolutionary Computation methods on these tasks is studied for a representative Feistel cryptosystem, the Data Encryption Standard. The results indicate that the proposed methodology is efficient in handling this type of problems and furthermore, that its effectiveness depends mainly on the construction of the objective function. This approach is applicable to all Feistel cryptosystems that are amenable to differential cryptanalysis.

Abstract: We consider approval voting elections in which each voter votes for a (possibly empty) set of candidates and the outcome consists of a set of k candidates for some parameter k, e.g., committee elections. We are interested in the minimax approval voting rule in which the outcome represents a compromise among the voters, in the sense that the maximum distance between the preference of any voter and the outcome is as small as possible. This voting rule has two main drawbacks. First, computing an outcome that minimizes the maximum distance is computationally hard. Furthermore, any algorithm that always returns such an outcome provides
incentives to voters to misreport their true preferences.
In order to circumvent these drawbacks, we consider approximation algorithms, i.e., algorithms that produce an outcome that approximates the minimax distance for any given instance. Such algorithms can be considered as alternative voting rules. We present a polynomial-time 2-approximation algorithm that uses a natural linear programming relaxation for the underlying optimization problem and deterministically
rounds the fractional solution in order to compute the outcome; this result improves upon the previously best known algorithm that has an approximation ratio of 3. We are furthermore interested in approximation algorithms that are resistant to manipulation by (coalitions of) voters, i.e., algorithms that do not motivate voters to misreport their true preferences in order to improve their distance from the outcome. We complement previous results in the literature with new upper and lower bounds on strategyproof and group-strategyproof algorithms.

Abstract: We study the problem of scheduling permanent jobs on un-
related machines when the objective is to minimize the Lp
norm of the machine loads. The problem is known as load
balancing under the Lp norm. We present an improved up-
per bound for the greedy algorithm through simple analy-
sis; this bound is also shown to be best possible within the
class of deterministic online algorithms for the problem. We
also address the question whether randomization helps on-
line load balancing under Lp norms on unrelated machines;
this is a challenging question which is open for more than a
decade even for the L2 norm. We provide a positive answer
to this question by presenting the ¯rst randomized online
algorithms which outperform deterministic ones under any
(integral) Lp norm for p = 2; :::; 137. Our algorithms es-
sentially compute in an online manner a fractional solution
to the problem and use the fractional values to make ran-
dom choices. The local optimization criterion used at each
step is novel and rather counterintuitive: the values of the
fractional variables for each job correspond to °ows at an ap-
proximate Wardrop equilibrium for an appropriately de¯ned
non-atomic congestion game. As corollaries of our analysis
and by exploiting the relation between the Lp norm and the
makespan of machine loads, we obtain new competitive algo-
rithms for online makespan minimization, making progress
in another longstanding open problem.

Abstract: We study the problem of scheduling permanent jobs on un-
related machines when the objective is to minimize the Lp
norm of the machine loads. The problem is known as load
balancing under the Lp norm. We present an improved up-
per bound for the greedy algorithm through simple analy-
sis; this bound is also shown to be best possible within the
class of deterministic online algorithms for the problem. We
also address the question whether randomization helps on-
line load balancing under Lp norms on unrelated machines;
this is a challenging question which is open for more than a
decade even for the L2 norm. We provide a positive answer
to this question by presenting the ¯rst randomized online
algorithms which outperform deterministic ones under any
(integral) Lp norm for p = 2; :::; 137. Our algorithms es-
sentially compute in an online manner a fractional solution
to the problem and use the fractional values to make ran-
dom choices. The local optimization criterion used at each
step is novel and rather counterintuitive: the values of the
fractional variables for each job correspond to °ows at an ap-
proximate Wardrop equilibrium for an appropriately de¯ned
non-atomic congestion game. As corollaries of our analysis
and by exploiting the relation between the Lp norm and the
makespan of machine loads, we obtain new competitive algo-
rithms for online makespan minimization, making progress
in another longstanding open problem.

Abstract: Counting in general, and estimating the cardinality of (multi-) sets in particular, is highly desirable for a large variety of applications, representing a foundational block for the efficient deployment and access of emerging internet-scale information systems. Examples of such applications range from optimizing query access plans in internet-scale databases, to evaluating the significance (rank/score) of various data items in information retrieval applications. The key constraints that any acceptable solution must satisfy are: (i) efficiency: the number of nodes that need be contacted for counting purposes must be small in order to enjoy small latency and bandwidth requirements; (ii) scalability, seemingly contradicting the efficiency goal: arbitrarily large numbers of nodes nay need to add elements to a (multi-) set, which dictates the need for a highly distributed solution, avoiding server-based scalability, bottleneck, and availability problems; (iii) access and storage load balancing: counting and related overhead chores should be distributed fairly to the nodes of the network; (iv) accuracy: tunable, robust (in the presence of dynamics and failures) and highly accurate cardinality estimation; (v) simplicity and ease of integration: special, solution-specific indexing structures should be avoided. In this paper, first we contribute a highly-distributed, scalable, efficient, and accurate (multi-) set cardinality estimator. Subsequently, we show how to use our solution to build and maintain histograms, which have been a basic building block for query optimization for centralized databases, facilitating their porting into the realm of internet-scale data networks.

Abstract: Evaluating target tracking protocols for wireless sensor networks that can localize multiple mobile devices, can be a very challenging task. Such protocols usually aim at minimizing communication overhead, data processing for the participating nodes, as well as delivering adequate tracking information of the mobile targets in a timely manner. Simulations on such protocols are performed using theoretical models that are based on unrealistic assumptions like the unit disk graph communication model, ideal network localization and perfect distance estimations. With these assumptions taken for granted, theoretical models claim various performance milestones that cannot be achieved in realistic conditions. In this paper we design a new localization protocol, where mobile assets can be tracked passively via software agents. We address the issues that hinder its performance due to the real environment conditions and provide a deployable protocol. The implementation, integration and experimentation of this new protocol and it's optimizations, were performed using the WISEBED framework. We apply our protocol in multiple indoors wireless sensor testbeds with multiple experimental scenarios to showcase scalability and trade-offs between network properties and configurable protocol parameters. By analysis of the real world experimental output, we present results that depict a more realistic view of the target tracking problem, regarding power consumption and the quality of tracking information. Finally we also conduct some very focused simulations to assess the scalability of our protocol in very large networks and multiple mobile assets.

Abstract: Wireless sensor networks are comprised of a vast number of devices, situated in an area of interest that self organize in a structureless network, in order to monitor/record/measure an environmental variable or phenomenon and subsequently to disseminate the data to the control center.
Here we present research focused on the development, simulation and evaluation of energy efficient algorithms, our basic goal is to minimize the energy consumption. Despite technology advances, the problem of energy use optimization remains valid since current and emerging hardware solutions fail to solve it.
We aim to reduce communication cost, by introducing novel techniques that facilitate the development of new algorithms. We investigated techniques of distributed adaptation of the operations of a protocol by using information available locally on every node, thus through local choices we improve overall performance. We propose techniques for collecting and exploiting limited local knowledge of the network conditions. In an energy efficient manner, we collect additional information which is used to achieve improvements such as forming energy efficient, low latency and fault tolerant paths to route data. We investigate techniques for managing mobility in networks where movement is a characteristic of the control center as well as the sensors. We examine methods for traversing and covering the network field based on probabilistic movement that uses local criteria to favor certain areas.
The algorithms we develop based on these techniques operate a) at low level managing devices, b) on the routing layer and c) network wide, achieving macroscopic behavior through local interactions. The algorithms are applied in network cases that differ in density, node distribution, available energy and also in fundamentally different models, such as under faults, with incremental node deployment and mobile nodes. In all these settings our techniques achieve significant gains, thus distinguishing their value as tools of algorithmic design.

Abstract: Top-k query processing is a fundamental building block for efficient ranking in a large number of applications. Efficiency is a central issue, especially for distributed settings, when the data is spread across different nodes in a network. This paper introduces novel optimization methods for top-k aggregation queries in such distributed environments. The optimizations can be applied to all algorithms that fall into the frameworks of the prior TPUT and KLEE methods. The optimizations address three degrees of freedom: 1) hierarchically grouping input lists into top-k operator trees and optimizing the tree structure, 2) computing data-adaptive scan depths for different input sources, and 3) data-adaptive sampling of a small subset of input sources in scenarios with hundreds or thousands of query-relevant network nodes. All optimizations are based on a statistical cost model that utilizes local synopses, e.g., in the form of histograms, efficiently computed convolutions, and estimators based on order statistics. The paper presents comprehensive experiments, with three different real-life datasets and using the ns-2 network simulator for a packet-level simulation of a large Internet-style network.

Abstract: We address the issue of measuring distribution fairness in Internet-scale networks. This problem has several interesting instances encountered in different applications, ranging from assessing the distribution of load between network nodes for load balancing purposes, to measuring node utilization for optimal resource exploitation, and to guiding autonomous decisions of nodes in networks built with market-based economic principles. Although some metrics have been proposed, particularly for assessing load balancing algorithms, they fall short. We first study the appropriateness of various known and previously proposed statistical metrics for measuring distribution fairness. We put forward a number of required characteristics for appropriate metrics. We propose and comparatively study the appropriateness of the Gini coefficient (G) for this task. Our study reveals as most appropriate the metrics of G, the fairness index (FI), and the coefficient of variation (CV) in this order. Second, we develop six distributed sampling algorithms to estimate metrics online efficiently, accurately, and scalably. One of these algorithms (2-PRWS) is based on two effective optimizations of a basic algorithm, and the other two (the sequential sampling algorithm, LBS-HL, and the clustered sampling one, EBSS) are novel, developed especially to estimate G. Third, we show how these metrics, and especially G, can be readily utilized online by higher-level algorithms, which can now know when to best intervene to correct unfair distributions (in particular, load imbalances). We conclude with a comprehensive experimentation which comparatively evaluates both the various proposed estimation algorithms and the three most appropriate metrics (G, CV, andFI). Specifically, the evaluation quantifies the efficiency (in terms of number of the messages and a latency indicator), precision, and accuracy achieved by the proposed algorithms when estimating the competing fairness metrics. The central conclusion is that the proposed metric, G, can be estimated with a small number of messages and latency, regardless of the skew of the underlying distribution.

Abstract: Through recent technology advances in the eld of wireless energy transmission, Wireless Rechargeable Sensor Networks
(WRSN) have emerged. In this new paradigm for
WSNs a mobile entity called Mobile Charger (MC) traverses
the network and replenishes the dissipated energy of sensors.
In this work we rst provide a formal denition of the charging
dispatch decision problem and prove its computational
hardness. We then investigate how to optimize the tradeo
s of several critical aspects of the charging process such
as a) the trajectory of the charger, b) the dierent charging
policies and c) the impact of the ratio of the energy
the MC may deliver to the sensors over the total available
energy in the network. In the light of these optimizations,
we then study the impact of the charging process to the
network lifetime for three characteristic underlying routing
protocols; a greedy protocol, a clustering protocol and an
energy balancing protocol. Finally, we propose a Mobile
Charging Protocol that locally adapts the circular trajectory
of the MC to the energy dissipation rate of each sub-region
of the network. We compare this protocol against several
MC trajectories for all three routing families by a detailed
experimental evaluation. The derived ndings demonstrate
signicant performance gains, both with respect to the no
charger case as well as the dierent charging alternatives; in
particular, the performance improvements include the network
lifetime, as well as connectivity, coverage and energy
balance properties.

Abstract: We consider two approaches that model timetable information in public transportation systems
as shortest-path problems in weighted graphs. In the time-expanded approach, every event at
a station, e.g., the departure of a train, is modeled as a node in the graph, while in the timedependent
approach the graph contains only one node per station. Both approaches have been
recently considered for (a simplified version of) the earliest arrival problem, but little is known
about their relative performance. Thus far, there are only theoretical arguments in favor of the
time-dependent approach. In this paper, we provide the first extensive experimental comparison of
the two approaches. Using several real-world data sets, we evaluate the performance of the basic
models and of several new extensions towards realistic modeling. Furthermore, new insights on
solving bicriteria optimization problems in both models are presented. The time-expanded approach
turns out to be more robust for modeling more complex scenarios, whereas the time-dependent
approach shows a clearly better performance.

Abstract: We consider two approaches that model timetable information in public transportation systems
as shortest-path problems in weighted graphs. In the time-expanded approach, every event at
a station, e.g., the departure of a train, is modeled as a node in the graph, while in the timedependent
approach the graph contains only one node per station. Both approaches have been
recently considered for (a simplified version of) the earliest arrival problem, but little is known
about their relative performance. Thus far, there are only theoretical arguments in favor of the
time-dependent approach. In this paper, we provide the first extensive experimental comparison of
the two approaches. Using several real-world data sets, we evaluate the performance of the basic
models and of several new extensions towards realistic modeling. Furthermore, new insights on
solving bicriteria optimization problems in both models are presented. The time-expanded approach
turns out to be more robust for modeling more complex scenarios, whereas the time-dependent
approach shows a clearly better performance.

Abstract: In this work we study energy efficient routing strategies
for wireless ad-hoc networks. In this kind of networks,
energy is a scarce resource and its conservation
and efficient use is a major issue. Our strategy follows
the multi-cost routing approach, according to which a
cost vector of various parameters is assigned to each
link. The parameters of interest are the number of hops
on a path, and the residual energy and the transmission
power of the nodes on the path. These parameters
are combined in various optimization functions,
corresponding to different routing algorithms, for selecting
the optimal path. We evaluate the routing algorithms
proposed in a number of scenarios, with respect
to energy consumption, throughput and other performance
parameters of interest. From the experiments
conducted we conclude that routing algorithms that take
into account energy related parameters, increase the
lifetime of the network, while achieving better performance
than other approaches, such as minimum hop
routing.

Abstract: In this work, we propose an energy-efficient multicasting algorithm
for wireless networks for the case where the transmission
powers of the nodes are fixed. Our algorithm is
based on the multicost approach and selects an optimal
energy-efficient set of nodes for multicasting, taking into account:
i) the node residual energies, ii) the transmission
powers used by the nodes, and iii) the set of nodes covered.
Our algorithm is optimal, in the sense that it can
optimize any desired function of the total power consumed
by the multicasting task and the minimum of the current
residual energies of the nodes, provided that the optimization
function is monotonic in each of these parameters. Our
optimal algorithm has non-polynomial complexity, thus, we
propose a relaxation producing a near-optimal solution in
polynomial time. The performance results obtained show
that the proposed algorithms outperform established solutions
for energy-aware multicasting, with respect to both
energy consumption and network lifetime. Moreover, it is
shown that the near-optimal multicost algorithm obtains
most of the performance benefits of the optimal multicost
algorithm at a smaller computational overhead.

Abstract: We consider the following distributed optimization problem: three agents i =
1; 2; 3 are each presented with a load drawn independently from the same known prior distribution.
Then each agent decides on which of two available bins to put her load. Each bin has
capacity �, and the objective is to find a distributed protocol that minimizes the probability
that an overflow occurs (or, equivalently, maximizes the winning probability).
In this work, we focus on the cases of full information and local information, depending on
whether each agent knows the loads of both other agents or not. Furthermore, we distinguish
between the cases where the agents are allowed to follow different decision rules (eponymous
model) or not (anonymous model ). We assume no communication among agents.
First, we present optimal protocols for the full information case, for both the anonymous and
the eponymous model.
For the local information, anonymous case, we show that the winning probability is upper
bounded by 0.622 in the case where the input loads are drawn from the uniform distribution.
Motivated by [3], we present a general method for computing the optimal single-threshold protocol
for any continuous distribution, and we apply this method to the case of the exponential
distribution.
Finally, we show how to compute, in exponential time, an optimal protocol for the local
information, eponymous model for the case where the input loads are drawn from a discretevalued,
bounded distribution.

Abstract: One of the major problems algorithm designers usually face is to know in advance whether a proposed optimization algorithm is going to behave as planned, and if not, what changes are to be made to the way new solutions are examined so that the algorithm performs nicely. In this work we develop a methodology for differentiating good neighborhoods from bad ones. As a case study we consider the structure of the space of assignments for random 3-SAT formulas and we compare two neighborhoods, a simple and a more refined one that we already know the corresponding algorithm behaves extremely well. We give evidence that it is possible to tell in advance what neighborhood structure will give rise to a good search algorithm and we show how our methodology could have been used to discover some recent results on the structure of the SAT space of solutions. We use as a tool Go with the winners, an optimization heuristic that uses many particles that independently search the space of all possible solutions. By gathering statistics, we compare the combinatorial characteristics of the different neighborhoods and we show that there are certain features that make a neighborhood better than another, thus giving rise to good search algorithms.

Abstract: We consider the offline version of the routing and
wavelength assignment (RWA) problem in transparent all-optical networks. In such networks and in the absence of regenerators, the signal quality of transmission degrades due to physical layer
impairments. We initially present an algorithm for solving the static RWA problem based on an LP relaxation formulation that tends to yield integer solutions. To account for signal degradation due to physical impairments, we model the effects of the path length, the path hop count, and the interference among ligthpaths by imposing additional (soft) constraints on RWA. The objective of the resulting optimization problem is not only to serve the
connection requests using the available wavelengths, but also to minimize the total accumulated signal degradation on the selected lightpaths. Our simulation studies indicate that the proposed RWA algorithms select the lightpaths for the requested connections so as to avoid impairment generating sources, thus dramatically reducing the overall physical-layer blocking when compared to RWA algorithms that do not account for impairments.

Abstract: We consider the online impairment-aware routing
and wavelength assignment (IA-RWA) problem in transparent
WDM networks. To serve a new connection, the online algorithm,
in addition to finding a route and a free wavelength (a lightpath),
has to guarantee its transmission quality, which is affected by
physical-layer impairments. Due to interference effects, the establishment
of the new lightpath affects and is affected by the other
lightpaths. We present two multicost algorithms that account
for the actual current interference among lightpaths, as well as
for other physical effects, performing a cross-layer optimization
between the network and physical layers. In multicost routing,
a vector of cost parameters is assigned to each link, from which
the cost vectors of the paths are calculated. The first algorithm
utilizes cost vectors consisting of impairment-generating source
parameters, so as to be generic and applicable to different physical
settings. These parameters are combined into a scalar cost
that indirectly evaluates the quality of candidate lightpaths. The
second algorithm uses specific physical-layer models to define
noise variance-related cost parameters, so as to directly calculate
the -factor of candidate lightpaths. The algorithms find a set of
so-called nondominated paths to serve the connection in the sense
that no path is better in the set with respect to all cost parameters.
To select the lightpath, we propose various optimization functions
that correspond to different IA-RWA algorithms. The proposed
algorithms combine the strength of multicost optimization with
low execution times, making them appropriate for serving online
connections

Abstract: In this work we study the combination of multicost
routing and adjustable transmission power in wireless
ad hoc networks, so as to obtain dynamic energy- and
interference-efficient routes to optimize network performance.
In multi-cost routing, a vector of cost parameters is
assigned to each network link, from which the cost vectors
of candidate paths are calculated. Only at the end these
parameters are combined in various optimization functions,
corresponding to different routing algorithms, for selecting
the optimal path. The multi-cost routing problem is a
generalization of the multi-constrained problem, where no
constraints exist, and is also significantly more powerful
than single-cost routing. Since energy is an important
limitation of wireless communications, the cost parameters
considered are the number of hops, the interference caused,
the residual energy and the transmission power of the
nodes on the path; other parameters could also be included,
as desired. We assume that nodes can use power control to
adjust their transmission power to the desired level. The
experiments conducted show that the combination of multicost
routing and adjustable transmission power can lead to
reduced interference and energy consumption, improving
network performance and lifetime.

Abstract: In translucent (or managed reach) WDM optical
networks, regenerators are employed at specific nodes. Some of
the connections in such networks are routed transparently, while
others have to go through a sequence of 3R regenerators that serve
as “refueling stations” to restore their quality of transmission
(QoT). We extend an online multicost algorithm for transparent
networks presented in our previous study [1], to obtain an IA-RWA
algorithm that works in translucent networks and makes use,
when required, of the regenerators present at certain locations
of the network. To characterize a path, the algorithm uses a
multicost formulation with several cost parameters, including the
set of available wavelengths, the length of the path, the number of
regenerators used, and noise variance parameters that account for
the physical layer impairments. Given a new connection request
and the current utilization state of the network, the algorithm calculates
a set of non dominated candidate paths, meaning that any
path in this set is not inferior with respect to all cost parameters
than any other path. This set consists of all the cost-effective (in
terms of the domination relation) and feasible (in terms of QoT)
lightpaths for the given source-destination pair, including all the
possible combinations for the utilization of available regenerators
of the network. An optimization function or policy is then applied
to this set in order to select the optimal lightpath. Different optimization
policies correspond to different IA-RWA algorithms.
We propose and evaluate several optimization policies, such as the
most used wavelength, the best quality of transmission, the least
regeneration usage, or a combination of these rules. Our results
indicate that in a translucent network the employed IA-RWA
algorithm has to consider all problem parameters, namely, the
QoT of the lightpaths, the utilization of wavelengths and the
availability of regenerators, to efficiently serve the online traffic.

Abstract: In this work we consider temporal graphs, i.e. graphs, each edge of which isassigned a set of discrete time-labels drawn from a set of integers. The labelsof an edge indicate the discrete moments in time at which the edge isavailable. We also consider temporal paths in a temporal graph, i.e. pathswhose edges are assigned a strictly increasing sequence of labels. Furthermore,we assume the uniform case (UNI-CASE), in which every edge of a graph isassigned exactly one time label from a set of integers and the time labelsassigned to the edges of the graph are chosen randomly and independently, withthe selection following the uniform distribution. We call uniform randomtemporal graphs the graphs that satisfy the UNI-CASE. We begin by deriving theexpected number of temporal paths of a given length in the uniform randomtemporal clique. We define the term temporal distance of two vertices, which isthe arrival time, i.e. the time-label of the last edge, of the temporal paththat connects those vertices, which has the smallest arrival time amongst alltemporal paths that connect those vertices. We then propose and study twostatistical properties of temporal graphs. One is the maximum expected temporaldistance which is, as the term indicates, the maximum of all expected temporaldistances in the graph. The other one is the temporal diameter which, looselyspeaking, is the expectation of the maximum temporal distance in the graph. Wederive the maximum expected temporal distance of a uniform random temporal stargraph as well as an upper bound on both the maximum expected temporal distanceand the temporal diameter of the normalized version of the uniform randomtemporal clique, in which the largest time-label available equals the number ofvertices. Finally, we provide an algorithm that solves an optimization problemon a specific type of temporal (multi)graphs of two vertices.

Abstract: We propose a class of novel energy-efficient multi-cost routing algorithms for wireless mesh networks, and evaluate their performance. In multi-cost routing, a vector of cost parameters is assigned to each network link, from which the cost vectors of candidate paths are calculated using appropriate operators. In the end these parameters are combined in various optimization functions, corresponding to different routing algorithms, for selecting the optimal path. We evaluate the performance of the proposed energy-aware multi-cost routing algorithms under two models. In the network evacuation model, the network starts with a number of packets that have to be transmitted and an amount of energy per node, and the objective is to serve the packets in the smallest number of steps, or serve as many packets as possible before the energy is depleted. In the dynamic one-to-one communication model, new data packets are generated continuously and nodes are capable of recharging their energy periodically, over an infinite time horizon, and we are interested in the maximum achievable steady-state throughput, the packet delay, and the energy consumption. Our results show that energy-aware multi-cost routing increases the lifetime of the network and achieves better overall network performance than other approaches.

Abstract: In this work we study the combination of
multicost routing and adjustable transmission power
in wireless ad-hoc networks, so as to obtain dynamic
energy and interference-efficient routes to optimize network performance. In multi-cost routing, a vector of
cost parameters is assigned to each network link, from
which the cost vectors of candidate paths are calcu-
lated. Only at the end are these parameters combined in
various optimization functions, corresponding to different routing algorithms, for selecting the optimal path.
The multi-cost routing problem is a generalization of
the multi-constrained problem, where no constraints exist, and is also significantly more powerful than single-
cost routing. Since energy is an important limitation of
wireless communications, the cost parameters consid
ered are the number of hops, the interference caused,
the residual energy and the transmission power of the
nodes on the path; other parameters could also be in
cluded, as desired.We assume that nodes can use power
control to adjust their transmission power to the desired
level. The experiments conducted show that the com
bination of multi-cost routing and adjustable transmis sion power can lead to reduced interference and energy
consumption, improving network performance and life-
time.

Abstract: We provide an improved FPTAS for multiobjective shortest paths,a fundamental (NP_hard) problem in multiobjective optimization,along with a new generic method for obtaining FPTAS to any multiobjective optimization problem with non-linear objectives. We show how these results can be used to obtain better approximate solutions to three related problems that have important applications in QoS routing and in traffic optimization.

Abstract: We provide an improved FPTAS for multiobjective shortest paths—a fundamental (NP-hard) problem in multiobjective optimization—along with a new generic method for obtaining FPTAS to any multiobjective optimization problem with non-linear objectives. We show how these results can be used to obtain better approximate solutions to three related problems, multiobjective constrained [optimal] path and non-additive shortest path, that have important applications in QoS routing and in traffic optimization. We also show how to obtain a FPTAS to a natural generalization of the weighted multicommodity flow problem with elastic demands and values that models several realistic scenarios in transportation and communication networks.

Abstract: The Frequency Assignment Problem (FAP) in radio networks is the problem of assigning frequencies to transmitters exploiting frequency reuse while keeping signal interference to acceptable levels. The FAP is usually modelled by variations of the graph coloring problem. The Radiocoloring (RC) of a graph G(V,E) is an assignment function Φ: V → IN such that ¦Φ(u)-Φ(v)≥ 2, when u; v are neighbors in G, and ¦Φ(u)-Φ(v)≥1 when the minimum distance of u; v in G is two. The discrete number and the range of frequencies used are called order and span, respectively. The optimization versions of the Radiocoloring Problem (RCP) are to minimize the span or the order. In this paper we prove that the min span RCP is NP-complete for planar graphs. Next, we provide an O(nΔ) time algorithm (¦V¦ = n) which obtains a radiocoloring of a planar graph G that approximates the minimum order within a ratio which tends to 2 (where Δ the maximum degree of G). Finally, we provide a fully polynomial randomized approximation scheme (fpras) for the number of valid radiocolorings of a planar graph G with λ colors, in the case λ ≥ 4λ + 50.

Abstract: We consider the offline version of the routing and
wavelength assignment (RWA) problem in transparent all-optical
networks. In such networks and in the absence of regenerators,
the signal quality of transmission degrades due to physical layer
impairments. Because of certain physical effects, routing choices
made for one lightpath affect and are affected by the choices made
for the other lightpaths. This interference among the lightpaths
is particularly difficult to formulate in an offline algorithm since,
in this version of the problem, we start without any established
connections and the utilization of lightpaths are the variables of
the problem.We initially present an algorithm for solving the pure
(without impairments) RWA problem based on a LP-relaxation
formulation that tends to yield integer solutions. Then, we extend
this algorithm and present two impairment-aware (IA) RWA algorithms
that account for the interference among lightpaths in their
formulation. The first algorithm takes the physical layer indirectly
into account by limiting the impairment-generating sources. The
second algorithm uses noise variance-related parameters to directly
account for the most important physical impairments. The
objective of the resulting cross-layer optimization problem is not
only to serve the connections using a small number of wavelengths
(network layer objective), but also to select lightpaths that have
acceptable quality of transmission (physical layer objective).
Simulations experiments using realistic network, physical layer,
and traffic parameters indicate that the proposed algorithms can
solve real problems within acceptable time.

Abstract: In future transparent optical networks, it is
important to consider the impact of physical impairments in the
routing and wavelengths assignment process, to achieve efficient
connection provisioning. In this paper, we use classical multi-
objective optimization (MOO) strategies and particularly genetic
algorithms to jointly solve the impairment aware RWA (IA-
RWA) problem. Fiber impairments are indirectly considered
through the insertion of the path length and the number of
common hops in the optimization process. It is shown that
blocking is greatly improved, while the obtained solutions truly
converge towards the Pareto front that constitutes the set of
global optimum solutions. We have evaluated our findings, using
an Q estimator tool, that calculates the signal quality of each path
analytically.
Index Terms RWA, Genetic Algorithm, All-Optical
Networks, Multi Objective Optimization.

Abstract: We propose and evaluate an impairment-aware multi-parametric routing and wavelength assignment algorithm for online traffic in transparent optical networks. In such networks the signal quality of transmission degrades due to physical layer impairments. In the multiparametric approach, a vector of cost parameters is assigned to each link, from which the cost vectors of candidate lightpaths are calculated. In the proposed scheme the cost vector includes impairment generating source parameters, such as the path length, the number of hops, the number of crosstalk sources and other inter-lightpath interfering parameters, so as to indirectly account for the physical layer effects. For a requested connection the algorithm calculates a set of candidate lightpaths, whose quality of transmission is validated using a function that combines the impairment generating parameters. For selecting the lightpath we propose and evaluate various optimization functions that correspond to different IA-RWA algorithms. Our performance results indicate that the proposed algorithms utilize efficiently the available resources and minimize the total accumulated signal degradation on the selected lightpaths, while having low execution times.

Abstract: In this paper we propose an energy-efficient broadcast algorithm for wireless networks for the case where the transmission powers of the nodes are fixed. Our algorithm is based on the multicost approach and selects an optimal energy-efficient set of nodes for broadcasting, taking into account: i) the node residual energies, ii) the transmission powers used by the nodes, and iii) the set of nodes that are covered by a specific schedule. Our algorithm is optimal, in the sense that it can optimize any desired function of the total power consumed by the broadcasting task and the minimum of the current residual energies of the nodes, provided that the optimization function is monotonic in each of these parameters. Our algorithm has non-polynomial complexity, thus, we propose a relaxation producing a near-optimal solution in polynomial time. Using simulations we show that the proposed algorithms outperform other established solutions for energy-aware broadcasting with respect to both energy consumption and network lifetime. Moreover, it is shown that the near-optimal multicost algorithm obtains most of the performance benefits of the optimal multicost algorithm at a smaller computational overhead.

Abstract: We consider in this paper the problem of scheduling a set of independent
parallel tasks (jobs) with respect to two criteria, namely,
the makespan (time of the last finishing job) and the minsum (average
completion time). There exist several algorithms with a good
performance guaranty for one of these criteria. We are interested
here in studying the optimization of both criteria simultaneously.
The numerical values are given for the moldable task model, where
the execution time of a task depends on the number of processors
alloted to it. The main result of this paper is to derive explicitly
a family of algorithms guaranteed for both the minsum and the
makespan. The performance guaranty of these algorithms is better
than the best algorithms known so far. The Guaranty curve
of the family is the set of all points (x; y) such that there is an
algorithm with guarantees x on makespan and y on the minsum.
When the ratio on the minsum increases, the curve tends to the
best ratio known for the makespan for moldable tasks (3=2). One
extremal point of the curves is a (3;6)-approximation algorithm.
Finally a randomized version is given, which improves this results
to (3;4.08).

Abstract: In this work we focus on the energy efficiency challenge in wireless sensor networks, from both an on-line perspective (related to routing), as well as a network design perspective (related to tracking). We investigate a few representative, important aspects of energy efficiency: a) the robust and fast data propagation b) the problem of balancing the energy
dissipation among all sensors in the network and c) the problem of efficiently tracking moving
entities in sensor networks. Our work here is a methodological survey of selected results that
have alre dy appeared in the related literature.
In particular, we investigate important issues of energy optimization, like minimizing the total
energy dissipation, minimizing the number of transmissions as well as balancing the energy
load to prolong the system¢s lifetime. We review characteristic protocols and techniques in the recent literature, including probabilistic forwarding and local optimization methods. We study the problem of localizing and tracking multiple moving targets from a network design perspective i.e. towards estimating the least possible number of sensors, their positions and operation characteristics needed to efficiently perform the tracking task. To avoid an expensive massive deployment, we try to take advantage of possible coverage overlaps over space and time, by introducing a novel combinatorial model that captures such overlaps. Under this model, we abstract the tracking network design problem by a covering combinatorial problem and then design and analyze an efficient approximate method for sensor placement
and operation.

Abstract: We employ here the Probabilistic Method, a way of reasoning which shows existence of combinatorial structures and properties to prove refute conjectures. The radiocoloring problem (RCP) is the problem of assigning frequencies to the transmitters of a network so that transmitters of distance one get frequencies that di#er by at least two and any two transmitters of distance one get frequencies that di#er by at least one. The objective of an assignment may be to minimize the number of frequencies used (order) or the range of them (span). Here, we study the optimization version of RCP where the objective is to minimize the order. In graph theory terms the problem is modelled by a variation of the vertex graph coloring problem. We investigate upper bounds for the minimum number of colors needed in a radiocoloring assignment of a graph G. We first provide an upper bound for the minimum number of colors needed to radiocolor a graph G of girth at most 7. Then, we study whether the minimum order of a radiocoloring assignment is determined by local conditions, i.e. by the minimum order radiocoloring assignment of some small subgraphs of it. We state a related conjecture which is analogous to a theorem of Molloy and Reed for the vertex coloring problem [12]. We then investigate whether the conjecture contradicts a Theorem of Molloy and Reed for the vertex coloring when applied on the graph G 2

Abstract: The Frequency Assignment Problem (FAP) in radio networks is the problem of assigning frequencies to transmitters, by exploiting frequency reuse while keeping signal interference to acceptable levels. The FAP is usually modelled by variations of the graph coloring problem. A Radiocoloring (RC) of a graph G(V,E) is an assignment function View the MathML source such that |{\"O}(u)-{\"O}(v)|greater-or-equal, slanted2, when u,v are neighbors in G, and |{\"O}(u)-{\"O}(v)|greater-or-equal, slanted1 when the distance of u,v in G is two. The number of discrete frequencies and the range of frequencies used are called order and span, respectively. The optimization versions of the Radiocoloring Problem (RCP) are to minimize the span or the order. In this paper we prove that the radiocoloring problem for general graphs is hard to approximate (unless NP=ZPP) within a factor of n1/2-{\aa} (for any View the MathML source), where n is the number of vertices of the graph. However, when restricted to some special cases of graphs, the problem becomes easier. We prove that the min span RCP is NP-complete for planar graphs. Next, we provide an O(n{\"A}) time algorithm (|V|=n) which obtains a radiocoloring of a planar graph G that approximates the minimum order within a ratio which tends to 2 (where {\"A} the maximum degree of G). Finally, we provide a fully polynomial randomized approximation scheme (fpras) for the number of valid radiocolorings of a planar graph G with {\"e} colors, in the case where {\"e}greater-or-equal, slanted4{\"A}+50.

Abstract: The Frequency Assignment Problem (FAP) in radio networks is the problem of assigning frequencies to transmitters, by exploiting frequency reuse while keeping signal interference to acceptable levels. The FAP is usually modelled by variations of the graph coloring problem. A Radiocoloring (RC) of a graph G(V,E) is an assignment function View the MathML source such that |{\"O}(u)-{\"O}(v)|greater-or-equal, slanted2, when u,v are neighbors in G, and |{\"O}(u)-{\"O}(v)|greater-or-equal, slanted1 when the distance of u,v in G is two. The number of discrete frequencies and the range of frequencies used are called order and span, respectively. The optimization versions of the Radiocoloring Problem (RCP) are to minimize the span or the order. In this paper we prove that the radiocoloring problem for general graphs is hard to approximate (unless NP=ZPP) within a factor of n1/2-{\aa} (for any View the MathML source), where n is the number of vertices of the graph. However, when restricted to some special cases of graphs, the problem becomes easier. We prove that the min span RCP is NP-complete for planar graphs. Next, we provide an O(n{\"A}) time algorithm (|V|=n) which obtains a radiocoloring of a planar graph G that approximates the minimum order within a ratio which tends to 2 (where {\"A} the maximum degree of G). Finally, we provide a fully polynomial randomized approximation scheme (fpras) for the number of valid radiocolorings of a planar graph G with {\"e} colors, in the case where {\"e}greater-or-equal, slanted4{\"A}+50.

Abstract: The Frequency Assignment Problem (FAP) in radio networks is the problem of assigning frequencies to transmitters exploiting frequency reuse while keeping signal interference to acceptable levels. The FAP is usually modelled by variations of the graph coloring problem. A Radiocoloring (RC) of a graph G(V,E) is an assignment function View the MathML source such that |{\"E}(u)−{\"E}(v)|greater-or-equal, slanted2, when u,v are neighbors in G, and |{\"E}(u)−{\"E}(v)|greater-or-equal, slanted1 when the distance of u,v in G is two. The discrete number of frequencies used is called order and the range of frequencies used, span. The optimization versions of the Radiocoloring Problem (RCP) are to minimize the span (min span RCP) or the order (min order RCP).
In this paper, we deal with an interesting, yet not examined until now, variation of the radiocoloring problem: that of satisfying frequency assignment requests which exhibit some periodic behavior. In this case, the interference graph (modelling interference between transmitters) is some (infinite) periodic graph. Infinite periodic graphs usually model finite networks that accept periodic (in time, e.g. daily) requests for frequency assignment. Alternatively, they can model very large networks produced by the repetition of a small graph.
A periodic graph G is defined by an infinite two-way sequence of repetitions of the same finite graph Gi(Vi,Ei). The edge set of G is derived by connecting the vertices of each iteration Gi to some of the vertices of the next iteration Gi+1, the same for all Gi. We focus on planar periodic graphs, because in many cases real networks are planar and also because of their independent mathematical interest.
We give two basic results:
• We prove that the min span RCP is PSPACE-complete for periodic planar graphs.
• We provide an O(n({\"A}(Gi)+{\'o})) time algorithm (where|Vi|=n, {\"A}(Gi) is the maximum degree of the graph Gi and {\'o} is the number of edges connecting each Gi to Gi+1), which obtains a radiocoloring of a periodic planar graph G that approximates the minimum span within a ratio which tends to View the MathML source as {\"A}(Gi)+{\'o} tends to infinity.
We remark that, any approximation algorithm for the min span RCP of a finite planar graph G, that achieves a span of at most {\'a}{\"A}(G)+constant, for any {\'a} and where {\"A}(G) is the maximum degree of G, can be used as a subroutine in our algorithm to produce an approximation for min span RCP of asymptotic ratio {\'a} for periodic planar graphs.

Abstract: The Frequency Assignment Problem (FAP) in radio networks is the problem of assigning frequencies to transmitters exploiting frequency reuse while keeping signal interference to acceptable levels. The FAP is usually modelled by variations of the graph coloring problem. The Radiocoloring (RC) of a graph G(V,E) is an assignment function {\"O}: V → IN such that ∣{\"O}(u) - {\"O}(v)∣ ≥2, when u, v are neighbors in G, and ∣{\"O}(u) - {\"O}(v)∣ ≥1 when the distance of u, v in G is two. The range of frequencies used is called span. Here, we consider the optimization version of the Radiocoloring Problem (RCP) of finding a radiocoloring assignment of minimum span, called min span RCP. In this paper, we deal with a variation of RCP: that of satisfying frequency assignment requests with some periodic behavior. In this case, the interference graph is an (infinite) periodic graph. Infinite periodic graphs model finite networks that accept periodic (in time, e.g. daily) requests for frequency assignment. Alternatively, they may model very large networks produced by the repetition of a small graph. A periodic graph G is defined by an infinite two-way sequence of repetitions of the same finite graph G i (V i ,E i ). The edge set of G is derived by connecting the vertices of each iteration G i to some of the vertices of the next iteration G i +1, the same for all G i . The model of periodic graphs considered here is similar to that of periodic graphs in Orlin [13], Marathe et al [10]. We focus on planar periodic graphs, because in many cases real networks are planar and also because of their independent mathematical interest. We give two basic results: - We prove that the min span RCP is PSPACE-complete for periodic planar graphs. - We provide an O(n({\"A}(G i ) + {\'o})) time algorithm, (where ∣V i ∣ = n, {\"A}(G i ) is the maximum degree of the graph G i and {\'o} is the number of edges connecting each G i to G i +1), which obtains a radiocoloring of a periodic planar graph G that approximates the minimum span within a ratio which tends to 2 as {\"A}(Gi) + {\'o} tends to infinity.

Abstract: We consider information aggregation as a method for reducing the information exchanged in a Grid network and used by the resource manager in order to make scheduling decisions. In this way, information is summarized across nodes and sensitive or detailed information can be kept private, while resources are still publicly available for use. We present a general framework for information aggregation, trying to identify issues that relate to aggregation in Grids. In this context, we describe a number of techniques, including single point and intra-domain aggregation, define appropriate grid-specific domination relations and operators for aggregating static and dynamic resource information, and discuss resource selection optimization functions. The quality of an aggregation scheme is measured both by its effects on the efficiency of the scheduler¢s decisions and also by the reduction it brings on the amount of resource information recorded, a tradeoff that we examine in detail. Simulation experiments demonstrate that the proposed schemes achieve significant information reduction, either in the amount of information exchanged, or in the frequency of the updates, while at the same time maintaining most of the value of the original information as expressed by a stretch factor metric we introduce.

Abstract: In this paper, we present and study solutions for the efficient processing of queries over string attributes in a large P2P data network implemented with DHTs. The proposed solutions support queries with equality, prefix, suffix, and containment predicates over string attributes. Currently, no known solution to this problem exists.
We propose and study algorithms for processing such queries and their optimizations. As event-based, Publish/Subscribe information systems are a champion application class where string attribute (continuous) queries are very common, we pay particular attention to this type of data networks, formulating our solution in terms of this environment. A major design decision behind the proposed solution is our intention to provide a solution that is general (DHT-independent), capable of being implemented on top of any particular DHT.

Abstract: In this work we consider temporal networks, i.e. networks defined by a labeling $\lambda$ assigning to each edge of an underlying graph G a set of discrete time-labels. The labels of an edge, which are natural numbers, indicate the discrete time moments at which the edge is available. We focus on path problems of temporal networks. In particular, we consider time-respecting paths, i.e. paths whose edges are assigned by $\lambda$ a strictly increasing sequence of labels. We begin by giving two efficient algorithms for computing shortest time-respecting paths on a temporal network. We then prove that there is a natural analogue of Menger’s theorem holding for arbitrary temporal networks. Finally, we propose two cost minimization parameters for temporal network design. One is the temporality of G, in which the goal is to minimize the maximum number of labels of an edge, and the other is the temporal cost of G, in which the goal is to minimize the total number of labels used. Optimization of these parameters is performed subject to some connectivity constraint. We prove several lower and upper bounds for the temporality and the temporal cost of some very basic graph families such as rings, directed acyclic graphs, and trees.

Abstract: We study the load balancing problem in the context of a set of clients each wishing to run a job on a server selected among a subset of permissible servers for the particular client. We consider two different scenarios. In selfish load balancing, each client is selfish in the sense that it selects to run its job to the server among its permissible servers having the smallest latency given the assignments of the jobs of other clients to servers. In online load balancing, clients appear online and, when a client appears, it has to make an irrevocable decision and assign its job to one of its permissible servers. Here, we assume that the clients aim to optimize some global criterion but in an online fashion. A natural local optimization criterion that can be used by each client when making its decision is to assign its job to that server that gives the minimum increase of the global objective. This gives rise to greedy online solutions. The aim of this paper is to determine how much the quality of load balancing is affected by selfishness and greediness.
We characterize almost completely the impact of selfishness and greediness in load balancing by presenting new and improved, tight or almost tight bounds on the price of anarchy and price of stability of selfish load balancing as well as on the competitiveness of the greedy algorithm for online load balancing when the objective is to minimize the total latency of all clients on servers with linear latency functions.

Abstract: We study the load balancing problem in the context of a set of clients each
wishing to run a job on a server selected among a subset of permissible servers for
the particular client. We consider two different scenarios. In selfish load balancing,
each client is selfish in the sense that it chooses, among its permissible servers, to
run its job on the server having the smallest latency given the assignments of the
jobs of other clients to servers. In online load balancing, clients appear online and,
when a client appears, it has to make an irrevocable decision and assign its job to
one of its permissible servers. Here, we assume that the clients aim to optimize some
global criterion but in an online fashion. A natural local optimization criterion that
can be used by each client when making its decision is to assign its job to that server that gives the minimum increase of the global objective. This gives rise to greedy
online solutions. The aim of this paper is to determine how much the quality of load
balancing is affected by selfishness and greediness.
We characterize almost completely the impact of selfishness and greediness in
load balancing by presenting new and improved, tight or almost tight bounds on the
price of anarchy of selfish load balancing as well as on the competitiveness of the
greedy algorithm for online load balancing when the objective is to minimize the
total latency of all clients on servers with linear latency functions. In addition, we
prove a tight upper bound on the price of stability of linear congestion games.

Abstract: We give an overview of models and efficient algorithms for
optimally solving timetable information problems like “given a departure
and an arrival station as well as a departure time, which is the
connection that arrives as early as possible at the arrival station?” Two
main approaches that transform the problems into shortest path problems
are reviewed, including issues like the modeling of realistic details
(e.g., train transfers) and further optimization criteria (e.g., the number
of transfers). An important topic is also multi-criteria optimization,
where in general all attractive connections with respect to several criteria
shall be determined. Finally, we discuss the performance of the described
algorithms, which is crucial for their application in a real system.

Abstract: We give an overview of models and efficient algorithms for optimally solving timetable information problems like “given a departure and an arrival station as well as a departure time, which is the connection that arrives as early as possible at the arrival station?” Two main approaches that transform the problems into shortest path problems are reviewed, including issues like the modeling of realistic details (e.g., train transfers) and further optimization criteria (e.g., the number of transfers). An important topic is also multi-criteria optimization, where in general all attractive connections with respect to several criteria shall be determined. Finally, we discuss the performance of the described algorithms, which is crucial for their application in a real system.

Abstract: In this work, we introduce the notion of time to some well-known combinatorial optimization problems. In particular, we study problems defined on temporal graphs. A temporal graph D=(V,A) may be viewed as a time-sequence G_1,G_2,...,G_l of static graphs over the same (static) set of nodes V. Each G_t = D(t) = (V,A(t)) is called the instance of D at time t and l is called the lifetime of D. Our main focus is on analogues of traveling salesman problems in temporal graphs. A sequence of time-labeled edges (e.g. a tour) is called temporal if its labels are strictly increasing. We begin by considering the problem of exploring the nodes of a temporal graph as soon as possible. In contrast to the positive results known for the static case, we prove that, it cannot be approximated within cn, for some constant c > 0, in general temporal graphs and within (2 − \varepsilon), for every constant \varepsilon > 0, in the special case in which D(t) is connected for all 1 <= t <= l, both unless P = NP. We then study the temporal analogue of TSP(1,2), abbreviated TTSP(1,2), where, for all 1 <= t <= l, D(t) is a complete weighted graph with edge-costs from {1,2} and the cost of an edge may vary from instance to instance. The goal is to find a minimum cost temporal TSP tour. We give several polynomial-time approximation algorithms for TTSP(1,2). Our best approximation is (1.7 + \varepsilon) for the generic TTSP(1,2) and (13/8 + \varepsilon) for its interesting special case in which the lifetime of the temporal graph is restricted to n. In the way, we also introduce temporal versions of Maximum Matching, Path Packing, Max-TSP, and Minimum Cycle Cover, for which we obtain polynomial-time approximation algorithms and hardness results.

Abstract: The interpolation method, originally developed in statistical physics, transforms distributions of random CSPs to distributions of much simpler problems while bounding the change in a number of associated statistical quantities along the transformation path. After a number of further mathematical developments, it is now known that, in principle, the method can yield rigorous unsatisfiability bounds if one “plugs in an appropriate functional distribution”. A drawback of the method is that identifying appropriate distributions and plugging them in leads to major analytical challenges as the distributions required are, in fact, infinite dimensional objects. We develop a variant of the interpolation method for random CSPs on arbitrary sparse degree distributions which trades accuracy for tractability. In particular, our bounds only require the solution of a 1-dimensional optimization problem (which typically turns out to be very easy) and as such can be used to compute explicit rigorous unsatisfiability bounds.

Abstract: In this work we add a training phase to an Impairment Aware Routing and Wavelength Assignment (IA-RWA) algorithm so as to improve its performance. The initial IA-RWA algorithm is a multi-parametric algorithm where a vector of physical impairment parameters is assigned to each link, from which the impairment vectors of candidate lightpaths are calculated. The important issue here is how to combine these impairment parameters into a scalar that would reflect the true transmission quality of a path. The training phase of the proposed IA-RWA algorithm is based on an optimization approach, called Particle Swarm Optimization (PSO), inspired by animal social behavior. The training phase gives the ability to the algorithm to be aware of the physical impairments even though the optical layer is seen as a black box. Our simulation studies show that the performance of the proposed scheme is close to that of algorithms that have explicit knowledge of the optical layer and the physical impairments.