Abstract: Implementation of a commercial application to a
grid infrastructure introduces new challenges in managing the
quality-of-service (QoS) requirements, most stem from the fact
that negotiation on QoS between the user and the service provider
should strictly be satisfied. An interesting commercial application
with a wide impact on a variety of fields, which can benefit from
the computational grid technologies, is three–dimensional (3-D)
rendering. In order to implement, however, 3-D rendering to a
grid infrastructure, we should develop appropriate scheduling
and resource allocation mechanisms so that the negotiated (QoS)
requirements are met. Efficient scheduling schemes require
modeling and prediction of rendering workload. In this paper
workload prediction is addressed based on a combined fuzzy
classification and neural network model. Initially, appropriate
descriptors are extracted to represent the synthetic world. The
descriptors are obtained by parsing RIB formatted files, which
provides a general structure for describing computer-generated
images. Fuzzy classification is used for organizing rendering
descriptor so that a reliable representation is accomplished which
increases the prediction accuracy. Neural network performs
workload prediction by modeling the nonlinear input-output
relationship between rendering descriptors and the respective
computational complexity. To increase prediction accuracy, a
constructive algorithm is adopted in this paper to train the neural
network so that network weights and size are simultaneously
estimated. Then, a grid scheduler scheme is proposed to estimate
the queuing order that the tasks should be executed and the
most appopriate processor assignment so that the demanded
QoS are satisfied as much as possible. A fair scheduling policy is
considered as the most appropriate. Experimental results on a real
grid infrastructure are presented to illustrate the efficiency of the
proposed workload prediction — scheduling algorithm compared
to other approaches presented in the literature.
Abstract: The management of Grid resources requires scheduling of both computation and communication tasks at various levels. In this study, we consider the two constituent sub-problems of Grid scheduling, namely: (i) the scheduling of computation tasks to processing resources and (ii) the routing and scheduling of the data movement in a Grid network. Regarding computation tasks, we examine two typical online taskscheduling algorithms that employ advance reservations and perform full network simulation experiments to measure their performance when implemented in a centralized or distributed manner. Similarly, for communication tasks, we compare two routing and data scheduling algorithms that are implemented in a centralized or a distributed manner. We examine the effect network propagation delay has on the performance of these algorithms. Our simulation results indicate that a distributed architecture with an exhaustive resource utilization update strategy yields better average end-to-end delay performance than a centralized architecture.
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 Grid computing. 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: Divisible load scenarios occur in modern media server applications since most multimedia applications typically require access to continuous and discrete data. A high performance Continuous Media (CM) server greatly depends on the ability of its disk IO subsystem to serve both types of workloads efficiently. Disk scheduling algorithms for mixed media workloads, although they play a central role in this task, have been overlooked by related research efforts. These algorithms must satisfy several stringent performance goals, such as achieving low response time and ensuring fairness, for the discrete-data workload, while at the same time guaranteeing the uninterrupted delivery of continuous data, for the continuous-data workload. The focus of this paper is on disk scheduling algorithms for mixed media workloads in a multimedia information server. We propose novel algorithms, present a taxonomy of relevant algorithms, and study their performance through experimentation. Our results show that our algorithms offer drastic improvements in discrete request average response times, are fair, serve continuous requests without interruptions, and that the disk technology trends are such that the expected performance benefits can be even greater in the future.
Abstract: In this work we examine a taskscheduling and data migration problem for Grid Networks, which we refer to as the Data Consolidation (DC) problem. DC arises when a task needs for its execution two or more pieces of data, possibly scattered throughout the Grid Network. In such a case, the scheduler and the data manager must select the data replicas to be used and the site where these will accumulate for the task to be executed. The policies for selecting the data replicas and the data consolidating site comprise the Data Consolidation problem. We propose and experimentally evaluate a number of DC techniques. Our simulation results brace our belief that DC is an important technique for Data Grids since it can substantially improve task delay, network load and other performance related parameters.
Abstract: We examine a taskscheduling and data migration problem for grid networks, which we refer to as the Data Consolidation (DC) problem. DC arises when a task concurrently requests multiple pieces of data, possibly scattered throughout the grid network, that have to be present at a selected site before the task˘s execution starts. In such a case, the scheduler and the data manager must select (i) the data replicas to be used, (ii) the site where these data will be gathered for the task to be executed, and (iii) the routing paths to be followed; this is assuming that the selected datasets are transferred concurrently to the execution site. The algorithms or policies for selecting the data replicas, the data consolidating site and the corresponding paths comprise a Data Consolidation scheme. We propose and experimentally evaluate several DC schemes of polynomial number of operations that attempt to estimate the cost of the concurrent data transfers, to avoid congestion that may appear due to these transfers and to provide fault tolerance. Our simulation results strengthen our belief that DC is an important problem that needs to be addressed in the design of data grids, and can lead, if performed efficiently, to significant benefits in terms of task delay, network load and other performance parameters.
Abstract: In this work we study the problem of schedulingtasks with dependencies in multiprocessor architectures where processors have different speeds.
We present the preemptive algorithm "Save-Energy" that given a schedule of tasks it post processes it to improve the energy efficiency without any deterioration of the makespan. In terms of time efficiency, we show that preemptive scheduling in an asymmetric system can achieve the same or better optimal makespan than in a symmetric system. Motivited by real multiprocessor systems, we investigate architectures that exhibit limited asymmetry: there are two essentially different speeds. Interestingly, this special case has not been studied in the field of parallel computing and scheduling theory; only the general case was studied where processors have K essentially different speeds. We present the non-preemptive algorithm "Remnants'' that achieves almost optimal makespan. We provide a refined analysis of a recent scheduling method. Based on this analysis, we specialize the scheduling policy and provide an algorithm of (3 + o(1)) expected approximation factor. Note that this improves the previous best factor (6 for two speeds). We believe that our work will convince researchers to revisit this well studied scheduling problem for these simple, yet realistic, asymmetric multiprocessor architectures.
Abstract: We propose a fair scheduling algorithm for Computational Grids, called Fair Execution Time Estimation (FETE) algorithm. FETE assigns a task to the computation resource that minimizes what we call its fair execution time estimation. The fair execution time of a task on a certain resource is an estimation of the time by which a task will be executed on the resource, assuming it gets a fair share of the resource’s computational power. Though space-shared scheduling is used in practice, the estimates of the fair execution times are obtained assuming that a time-sharing discipline is used. We experimentally evaluate the proposed algorithm and observe that it outperforms other known scheduling algorithms. We also propose a version of FETE, called Simple FETE (SFETE), which requires no a-priori knowledge of the tasks workload and in most cases has similar performance to that of FETE.
Abstract: In this paper we present a multicost algorithm for the joint
time scheduling of the communication and computation
resources that will be used by a task. The proposed
algorithm selects the computation resource to execute the
task, determines the path to route the input data, and finds
the starting times for the data transmission and the task
execution, performing advance reservations. We initially
present an optimal scheme of non-polynomial complexity
and by appropriately pruning the set of candidate paths we
also give a heuristic algorithm of polynomial complexity. We
evaluate the performance of our algorithm and compare it to
that of algorithms that handle only the computation or
communication part of the problem separately. We show that
in a Grid network where the tasks are CPU- and dataintensive
important performance benefits can be obtained by
jointly optimizing the use of the communication and
computation resources.
Abstract: A key problem in Grid networks is how to efficiently manage the available infrastructure, in order to
satisfy user requirements and maximize resource utilization. This is in large part influenced by the
algorithms responsible for the routing of data and the scheduling of tasks. In this paper,wepresent several
multi-cost algorithms for the joint scheduling of the communication and computation resources that
will be used by a Grid task. We propose a multi-cost scheme of polynomial complexity that performs
immediate reservations and selects the computation resource to execute the task and determines the
path to route the input data. Furthermore, we introduce multi-cost algorithms that perform advance
reservations and thus also find the starting times for the data transmission and the task execution. We
initially present an optimal scheme of non-polynomial complexity and by appropriately pruning the set
of candidate paths we also give a heuristic algorithm of polynomial complexity. Our performance results
indicate that in a Grid network in which tasks are either CPU- or data-intensive (or both), it is beneficial
for the scheduling algorithm to jointly consider the computational and communication problems. A
comparison between immediate and advance reservation schemes shows the trade-offs with respect to
task blocking probability, end-to-end delay and the complexity of the algorithms.
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: Grids offer a transparent interface to geographically scattered computation, communication, storage and
other resources. In this chapter we propose and evaluate QoS-aware and fair scheduling algorithms for
Grid Networks, which are capable of optimally or near-optimally assigning tasks to resources, while taking
into consideration the task characteristics and QoS requirements. We categorize Grid tasks according to
whether or not they demand hard performance guarantees. Tasks with one or more hard requirements are
referred to as Guaranteed Service (GS) tasks, while tasks with no hard requirements are referred to as Best
Effort (BE) tasks. For GS tasks, we propose scheduling algorithms that provide deadline or computational
power guarantees, or offer fair degradation in the QoS such tasks receive in case of congestion. Regarding
BE tasks our objective is to allocate resources in a fair way, where fairness is interpreted in the max-min fair
share sense. Though, we mainly address scheduling problems on computation resources, we also look at
the joint scheduling of communication and computation resources and propose routing and scheduling
algorithms aiming at co-allocating both resource type so as to satisfy their respective QoS requirements.
Abstract: We propose information aggregation as a method for summarizing the resource-related information, used by the task scheduler. Through this method the information of a set of resources can be uniformly represented, reducing at the same time the amount of information transferred in a Grid network. A number of techniques are described for aggregating the information of the resources belonging to a hierarchical Grid domain. This information includes the cpu and storage capacities at a site, the number of tasks queued, and other resource-related parameters. The quality of the aggregation scheme affects the efficiency of the scheduler{\^a}€™s decisions. We use as a metric of aggregation efficiency the Stretch Factor (SF), defined as the ratio of the task delay when the task is scheduled using complete resource information over the task delay when an aggregation scheme is used. The simulation experiments performed show that the proposed aggregation schemes achieve large information reduction, while enabling good taskscheduling decisions as indicated by the SF achieved.
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, grid
computing.
Abstract: In this work we study the problem of schedulingtasks with dependencies in multiprocessor architectures where processors have different speeds. We examine the energy-efficiency and time efficiency of scheduling in an asymmetric system.
Abstract: Efficient taskscheduling 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 taskscheduling 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.