Abstract: In this paper, we propose an efficient non-linear task workload prediction mechanism incorporated with a fair scheduling algorithm
for task allocation and resource management in Gridcomputing. Workload prediction is accomplished in a Grid middleware approach
using a non-linear model expressed as a series of finite known functional components using concepts of functional analysis. The coefficient
of functional components are obtained using a training set of appropriate samples, the pairs of which are estimated based on
a runtime estimation model relied on a least squares approximation scheme. The advantages of the proposed non-linear task workload
prediction scheme is that (i) it is not constrained by analysis of source code (analytical methods), which is practically impossible to be
implemented in complicated real-life applications or (ii) it does not exploit the variations of the workload statistics as the statistical
approaches does. The predicted task workload is then exploited by a novel scheduling algorithm, enabling a fair Quality of Service oriented
resource management so that some tasks are not favored against others. The algorithm is based on estimating the adjusted fair
completion times of the tasks for task order selection and on an earliest completion time strategy for the grid resource assignment. Experimental
results and comparisons with traditional scheduling approaches as implemented in the framework of European Union funded
research projects GRIA and GRIDLAB grid infrastructures have revealed the outperformance of the proposed method.

Abstract: The existence of good probabilistic models for the job
arrival process and the delay components introduced at
the different stages of job processing in a Grid
environment is important for the improved understanding
of the computing concept envisioned by the Grid. In this
study, we present a thorough analysis of the job arrival
process in the EGEE infrastructure and the time
durations a job spends at different states in the EGEE
environment. We define four delay components of the
total job delay and model each component separately.
We observe that the job inter-arrival times at the Grid
level can be adequately modeled by a rounded
exponential distribution, while the total job delay (from
the time it is generated until the time it completes
execution) is dominated by the Computing Element’s
queuing and Worker Node’s execution times.

Abstract: The existence of good probabilistic models for the job
arrival process and job characteristics is important for
the improved understanding of grid systems and the
prediction of their performance. In this study, we
present a thorough analysis of the job inter-arrival
times, the waiting times at the queues, the execution
times, and the data sizes exchanged at the
kallisto.hellasgrid.gr cluster, which is part of the
EGEE Grid infrastructure. By computing the Hurst
parameter of the inter-arrival times we find that the
job arrival process exhibits self-similarity/long-range
dependence. We also propose simple and intuitive
models for the job arrival process and the job
execution times. The models proposed were validated
and were found to be in very good agreement with our
empirical measurements.

Abstract: We propose QoS-aware scheduling algorithms for Grid Networks that are capable of optimally or near-optimally
assigning computation and communication tasks to grid resources. The routing and scheduling algorithms to be
presented take as input the resource utilization profiles and the task characteristics and QoS requirements, and
co-allocate resources while accounting for the dependencies between communication and computation tasks.
Keywords: communication and computation utilization profiles, multicost routing and scheduling, gridcomputing.

Abstract: Efficient task scheduling is fundamental for the success of the Grids,
since it directly affects the Quality of Service (QoS) offered to the users. Efficient
scheduling policies should be evaluated based not only on performance
metrics that are of interest to the infrastructure side, such as the Grid resources
utilization efficiency, but also on user satisfaction metrics, such as the percentage
of tasks served by the Grid without violating their QoS requirements. In this
paper, we propose a scheduling algorithm for tasks with strict timing requirements,
given in the form of a desired start and finish time. Our algorithm aims
at minimizing the violations of the time constraints, while at the same time
minimizing the number of processors used. The proposed scheduling method
exploits concepts derived from spectral clustering, and groups together for assignment
to a computing resource the tasks so to a) minimize the time overlapping
of the tasks assigned to a given processor and b) maximize the degree of
time overlapping among tasks assigned to different processors. Experimental
results show that our proposed strategy outperforms greedy scheduling algorithms
for different values of the task load submitted.

Abstract: Efficient task scheduling is fundamental for the success of the Grids,
since it directly affects the Quality of Service (QoS) offered to the users. Efficient
scheduling policies should be evaluated based not only on performance
metrics that are of interest to the infrastructure side, such as the Grid resources
utilization efficiency, but also on user satisfaction metrics, such as the percentage
of tasks served by the Grid without violating their QoS requirements. In this
paper, we propose a scheduling algorithm for tasks with strict timing requirements,
given in the form of a desired start and finish time. Our algorithm aims
at minimizing the violations of the time constraints, while at the same time
minimizing the number of processors used. The proposed scheduling method
exploits concepts derived from spectral clustering, and groups together for assignment
to a computing resource the tasks so to a) minimize the time overlapping
of the tasks assigned to a given processor and b) maximize the degree of
time overlapping among tasks assigned to different processors. Experimental
results show that our proposed strategy outperforms greedy scheduling algorithms
for different values of the task load submitted.

Abstract: The existence of good probabilistic models
for the job arrival process and the delay components
introduced at different stages of job processing in a
Grid environment is important for the improved
understanding of the Gridcomputing concept. In this
study, we present a thorough analysis of the job
arrival process in the EGEE infrastructure and of the
time durations a job spends at different states in the
EGEE environment. We define four delay compo-
nents of the total job delay and model each compo-
nent separately. We observe that the job inter-arrival
times at the Grid level can be adequately modelled by
a rounded exponential distribution, while the total job
delay (from the time it is generated until the time it
completes execution) is dominated by the computing
element’s register and queuing times and the worker
node’s execution times. Further, we evaluate the
efficiency of the EGEE environment by comparing
the job total delay performance with that of a hypothetical ideal super-cluster and conclude that we
would obtain similar performance if we submitted the
same workload to a super-cluster of size equal to 34%
of the total average number of CPUs participating in
the EGEE infrastructure. We also analyze the job
inter-arrival times, the CE’s queuing times, the WN’s
execution times, and the data sizes exchanged at the
kallisto.hellasgrid.gr cluster, which is node in the
EGEE infrastructure. In contrast to the Grid level, we
find that at the cluster level the job arrival process
exhibits self-similarity/long-range dependence. Final-
ly, we propose simple and intuitive models for the job
arrival process and the execution times at the cluster
level.

Abstract: Today we are experiencing a major reconsideration of the computing
paradigm, as witnessed by the abundance and increasing frequency
of use of terms such as {\em ambient intelligence}, {\em ubiquitous computing}, {\em disappearing computer}, {\em grid
computer}, {\em global computing} and {\em mobile ad-hoc
networks}. Systems that can be described with such terms are of a
dynamic, with no clear physical boundary, nature and it seems that
it is impossible (or, at least, difficult) to define sharply a
number of important properties holding with certainty as well as
holding throughout the whole lifetime of the system.
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One such system property, which is important for the viability of
a system, is {\em trust}. Our departure point is the assumption
that it seems very difficult to define static system properties
related to trust and expect that they hold eternally in the
rapidly changing systems falling under the new computing paradigm.
One should, rather, attempt to define trust in terms of properties
that hold with some limiting probability as the the system grows
and try to establish conditions that ensure that ``good''
properties hold {\em almost certainly}. Based on this viewpoint,
in this paper we provide a new framework for defining trust
through formally definable properties that hold, almost certainly,
in the limit in randomly growing combinatorial structures that
model ``shapeless'' computing systems (e.g. ad-hoc networks),
drawing on results that establish the threshold behavior of
predicates written in the first and second order logic.