Multiprocessor independent tasks scheduling using a novel heuristic PSO algorithm

Omidi, Ali; Rahmani, Amir Masoud

doi:10.1109/iccsit.2009.5234707

Cited by 15 publications

(14 citation statements)

References 12 publications

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“…Many famous metaheuristic, such as GA [19,30,39], AIS [31,44,46], ACO [5], PSO [34], SA [26], TS [36], and VNS [10] have been successfully applied to a broad range of scheduling problems. Nevertheless, to trade off better performance in terms of schedule quality, non-deterministic algorithms always have much higher computational cost.…”

Section: Related Workmentioning

confidence: 99%

“…In attempts to obtain schedules of better quality, many well-known metaheuristics, including genetic algorithm (GA) [1,6,8,21,33,37,42], artificial immune system (AIS) [44], ant colony optimization (ACO) [5], particle swarm optimization (PSO) [34], simulated annealing (SA) [26], tabu search (TS) [36], and variable neighborhood search (VNS) [32], have been adopted. Generally, metaheuristics can be divided into trajectory methods (also named local search heuristics) and population-based methods.…”

Section: Introductionmentioning

confidence: 99%

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A heuristic-based hybrid genetic-variable neighborhood search algorithm for task scheduling in heterogeneous multiprocessor system

Wen

Yang

2011

Information Sciences

103

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A heuristic-based hybrid genetic-variable neighborhood search algorithm for task scheduling in heterogeneous multiprocessor system

Wen

Yang

2011

Information Sciences

103

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“…To acquire a procedure having higher value, several famous meta-heuristics mechanisms like Simulated Annealing (SA) [8], Genetic Algorithm (GA) [6,12,14] Ant Colony Optimization (ACO) [16], TABU Search, Artificial Immune System [19], VNS and a popular Particle Swarm Optimization (PSO) [13] have been adopted. In general, meta-heuristics may be split into path like curve (also called nonglobal exploration logical technique) and based on chromosome mechanisms.…”

Section: Introductionmentioning

confidence: 99%

Genetic-variable neighborhood search with thread replication for mobile cloud computing

Singh

2016

International Journal of Parallel, Emergent and Distributed Sys

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The resource-intensive resettlement procedure and inherent restrictions of the wireless medium obstruct the understanding of faultless implementation in mobile cloud computing (MCC) surroundings. In MCC, transfer of thread execution to high end servers allows the processing of those jobs which demand more resources on the mobile equipment. Hence, implementing the application with stumpy cost, least overhead and non-obtrusive relocation is a demanding research area in MCC. Many scheduling mechanisms have been proposed so far to balance the load between the given set of mobile servers, but it is found to be NP-Hard problem. Therefore, evolutionary techniques are required to balance the load among mobile servers. Genetic Algorithm (GA) has been verified to be fine by jumble up the key values, but it becomes unsuccessful to strengthen the exploration in the rising areas. In Variable Neighborhood Search (VNS), local exploration technique is implemented continually to evaluate optimistic outcomes in neighborhood to attain local best solution. But VNS is still prone to premature convergence traps only because of limited search capability. Therefore hybridization with non-global finding techniques may conquer the limitations and guide the dominant search mechanisms to some extent. The GA along with VNS using thread replication (GVNSTR) is implemented to set stability of non-local searching and local utilization for an evolutionary processing period and get the optimized solution.

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“…The results infer that the PSO performs better than the GA. Omidi and Rahmani [11] used PSO for task scheduling in multiprocessor systems as an important step for efficient utilization of resources. They considered independent tasks on homogeneous multiprocessor systems.…”

Section: Review Stagementioning

confidence: 99%

“…Finally, the inertia factor W can be fixed or varied with a decreasing value as the algorithm proceeds [11] or it may be restarted as in [5]. PSO has been applied to solve the problem of task partitioning for homogeneous and heterogeneous multiprocessors [5], [10], [11].…”

Section: International Journal Of Computer Theory and Engineering Vomentioning

confidence: 99%

Enhanced PSO Approach for Real Time Systems Scheduling

Awadalla¹,

Elewi²

2016

IJCTE

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Abstract-Systems as asymmetric multiprocessor platforms are considered power-efficient multiprocessor architectures, efficient task partitioning (assignment) and play a crucial role in achieving more energy efficiency at these multiprocessor platforms. This paper addresses the problem of energy-aware static partitioning of periodic real time tasks on heterogeneous multiprocessor platforms. A modified Particle Swarm Optimization variant based on min-min and priority assignment algorithms for task partitioning is proposed. The proposed approach aims to minimize the overall energy consumption, meanwhile avoid deadline violations. An energy-aware cost function is proposed to be considered in the proposed approach. Extensive simulated experiments and comparisons with related approaches have been conducted and the achieved results demonstrate that the proposed partitioning scheme significantly outperforms in terms of the number of executed iterations to accomplish a specific task in addition to the energy savings.Index Terms-Task partitioning, task assignment, heterogeneous multiprocessors, particle swarm optimization, min-min, priority assignment algorithm.

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Multiprocessor independent tasks scheduling using a novel heuristic PSO algorithm

Cited by 15 publications

References 12 publications

A heuristic-based hybrid genetic-variable neighborhood search algorithm for task scheduling in heterogeneous multiprocessor system

A heuristic-based hybrid genetic-variable neighborhood search algorithm for task scheduling in heterogeneous multiprocessor system

Genetic-variable neighborhood search with thread replication for mobile cloud computing

Enhanced PSO Approach for Real Time Systems Scheduling

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