Cluster-M Parallel Programming Paradigm

Eshaghian, M.M.; Shaaban, Muhammad

doi:10.1142/s0129053394000147

Cited by 13 publications

(5 citation statements)

References 4 publications

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“…, P ] and (Δt) = 0.9 for all the nodes of the sites B and D, while for the site C we assume i (Δt) = 0. 8 It is to note that in this load conditions the mapping procedure has chosen once again the most powerful nodes: 4 of C with i (Δt) = 0.6 which are those with a minor load and 16 of E.…”

Section: Figure 2 the Grid Architecturementioning

confidence: 99%

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A Distributed Differential Evolution Approach for Mapping in a Grid Environment

Falco

Scafuri

Tarantino

et al. 2007

15th EUROMICRO International Conference on Parallel, Distributed and Network-Based Processing (PDP'07)

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Increase in intensive applications with different computational requirements, coupled with the unification of remote and diverse resources thanks to advances in the widearea network technologies and the low cost of components, have encouraged the development of grid computing. To exploit the promising potentials of geographically distributed resources, effective and efficient mapping algorithms are fundamental. Since the problem of optimally mapping is NP-complete, the development of evolutionary techniques to find near-optimal solutions is welcome. In this paper a distributed system based on Differential Evolution is designed and implemented to face the mapping problem in a grid environment aiming at reducing the degree of use of the grid resources. This system is tested on some different resource allocation scenarios.

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Section: Figure 2 the Grid Architecturementioning

confidence: 99%

“…computer clusters, cannot work adequately in these situations [3], other approaches have been proposed to cope with different issues of the problem [11,8,4,7].…”

Section: Introductionmentioning

confidence: 99%

A Distributed Differential Evolution Approach for Mapping in a Grid Environment

Falco

Scafuri

Tarantino

et al. 2007

15th EUROMICRO International Conference on Parallel, Distributed and Network-Based Processing (PDP'07)

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“…Task matching and scheduling techniques [6,7,9,15,19,[22][23][24][25][26][27][28][29][30][31][32]35] can be categorized into optimal selection theory-based approaches [9,31], graph-based approaches [6,22], genetic algorithm-based techniques [23,25,30] and other heuristics [6,7,19,29,35]. Most of the work has been carried out to find a nearoptimal solution.…”

Section: Related Workmentioning

confidence: 99%

An Integrated Technique for Task Matching and Scheduling onto Distributed Heterogeneous Computing Systems

Dhodhi¹,

Ahmad

Yatama

et al. 2002

Journal of Parallel and Distributed Computing

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This paper presents a problem-space genetic algorithm (PSGA)-based technique for efficient matching and scheduling of an application program that can be represented by a directed acyclic graph, onto a mixed-machine distributed heterogeneous computing (DHC) system. PSGA is an evolutionary technique that combines the search capability of genetic algorithms with a known fast problem-specific heuristic to provide the best-possible solution to a problem in an efficient manner as compared to other probabilistic techniques. The goal of the algorithm is to reduce the overall completion time through proper task matching, task scheduling, and inter-machine data transfer scheduling in an integrated fashion. The algorithm is based on a new evolutionary technique that embeds a known problem-specific fast heuristic into genetic algorithms (GAs). The algorithm is robust in the sense that it explores a large and complex solution space in smaller CPU time and uses less memory space as compared to traditional GAs. Consequently, the proposed technique schedules an application program with a comparable schedule length in a very short CPU time, as compared to GA-based heuristics. The paper includes a performance comparison showing the viability and effectiveness of the proposed technique through comparison with existing GA-based techniques.

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“…In this paper, the attention is focused only on the mapping phase. Since mapping algorithms for traditional parallel and distributed systems, which usually run on homogeneous and dedicated resources, for example, computer clusters, cannot work adequately in heterogeneous environments [1], other approaches have been proposed to cope with different issues of the problem [8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

A Distributed Bio-Inspired Method for Multisite Grid Mapping

Falco

Cioppa

Scafuri

et al. 2010

Applied Computational Intelligence and Soft Computing

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Computational grids assemble multisite and multiowner resources and represent the most promising solutions for processing distributed computationally intensive applications, each composed by a collection of communicating tasks. The execution of an application on a grid presumes three successive steps: the localization of the available resources together with their characteristics and status; the mapping which selects the resources that, during the estimated running time, better support this execution and, at last, the scheduling of the tasks. These operations are very difficult both because the availability and workload of grid resources change dynamically and because, in many cases, multisite mapping must be adopted to exploit all the possible benefits. As the mapping problem in parallel systems, already known as NP-complete, becomes even harder in distributed heterogeneous environments as in grids, evolutionary techniques can be adopted to find near-optimal solutions. In this paper an effective and efficient multisite mapping, based on a distributed Differential Evolution algorithm, is proposed. The aim is to minimize the time required to complete the execution of the application, selecting from among all the potential ones the solution which reduces the use of the grid resources. The proposed mapper is tested on different scenarios.

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Cluster-M Parallel Programming Paradigm

Cited by 13 publications

References 4 publications

A Distributed Differential Evolution Approach for Mapping in a Grid Environment

A Distributed Differential Evolution Approach for Mapping in a Grid Environment

An Integrated Technique for Task Matching and Scheduling onto Distributed Heterogeneous Computing Systems

A Distributed Bio-Inspired Method for Multisite Grid Mapping

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