Efficient Scheduling of Task Graphs to Multiprocessors Using a Combination of Modified Simulated Annealing and List Based Scheduling

Houshmand, Mahboobeh; Soleymanpour, Elaheh; Salami, Hossein; Amerian, Mahya; Deldari, Hossein

doi:10.1109/iitsi.2010.137

Cited by 13 publications

(7 citation statements)

References 14 publications

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“…Contrary to heuristic-based, the meta-heuristic methods search the solution space in a direct manner and produce efficient results on the broad domain problems, but these methods have high time complexity. Meta-heuristic algorithms are also called guided-random search-based methods [18][19][20][21][22][23][24][25][26][27][28][29][30].…”

Section: R Re El La At Te Ed D W Wo or Rk Kmentioning

confidence: 99%

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A Proposed Meta-Heuristic Approach for Cloudlets Scheduling in Cloud Computing Environment

Nasr

El‐Bahnasawy

Attiya

et al. 2019

Menoufia Journal of Electronic Engineering Research

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A A P Pr ro op po os se ed d M Me et ta a--H He eu ur ri is st ti ic c A Ap pp pr ro oa ac ch h f fo or r C Cl lo ou ud dl le et ts s S Sc ch he ed du ul li in ng g i in n C Cl lo ou ud d C Co om mp pu ut ti in ng g E En nv vi ir ro on nm me en nt t A Ab bs st tr ra ac ct tThis paper presents a new hybrid approach, called ACOSA, for cloudlets scheduling to enhance the scheduler behavior in Cloud computing (CC) environment and to overcome the results oscillation problem of the existing meta-heuristic scheduling algorithms. The proposed approach combines both the Ant Colony Optimization (ACO) and Simulated Annealing (SA) algorithm to improve both quality of solutions and time complexity of the scheduling algorithm. The proposed approach is evaluated by using the well-known CloudSim, and the results are compared with the ant colony and simulated annealing separately in terms of schedule length, load balancing, and time complexity. It decreases the schedule length by 29.75% with SA and 12.25% with ACO. The ACOSA provides higher load balancing degree. It improves the balancing degree ratio by 36.36% than SA and 12.13% than ACO algorithms.

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Section: R Re El La At Te Ed D W Wo or Rk Kmentioning

confidence: 99%

“…However, the time complexity is high. Another metaheuristic cloudlet-scheduling algorithm is Simulated Annealing (SA) [24][25][26]. It has smaller time complexity than GA.…”

Section: ____________________________________________________________mentioning

confidence: 99%

A Proposed Meta-Heuristic Approach for Cloudlets Scheduling in Cloud Computing Environment

Nasr

El‐Bahnasawy

Attiya

et al. 2019

Menoufia Journal of Electronic Engineering Research

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“…The duplicated class method CPFD (Critical path fast duplicated) [14] allows node cloning; the resulting node multiplicity copies methods for reducing communication delays. The random methods mostly use genetic or simulated annealing [15,16,17]; they can be very fast and are simple to implement.…”

Section: Task-scheduling Problemmentioning

confidence: 99%

Towards an automated framework for task scheduling

Dubois

Boukadoum

2010

2010 International Conference on Microelectronics

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the ongoing sh.-inkage of semiconductor geometr'ies allows for increasingly higher system-on-chip (SoC) densities, with more and more on-chip pl·ocessol·s. As a result, task scheduling has become an important concern in system design and reseal'ch in this area has pmduced substantial and dive.-sified knowledge. This paper addl'esses the issue of how to effectively repl'esent and use this knowledge in the context of design automation tools. A new methodology based on functional concept analysis is pl'esented that structul'es the available task scheduling knowledge for optimal application to multiprocessOi' SoC design.

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“…The duplicated class method CPFD (Critical path fast duplicated) [11] allows node cloning; the resulting node multiplicity copies methods for reducing communication delays. The random methods mostly use genetic or simulated annealing [8,12,13]; they can be very fast and are simple to implement.…”

Section: Task-scheduling Problemmentioning

confidence: 99%

Association rules learning technique for knowledge mining about scheduling algorithm performance

Dubois

Boukadoum

2011

2011 IEEE 9th International New Circuits and Systems Conference

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With the advent of increasingly higher numbers of processors on-chip, task scheduling has become an important concern in system design, and research in this area has produced substantial and diversified knowledge. As a result, the efficient management and taping of this knowledge has become a concern in itself. This paper addresses the issue of how to effectively extract performance information about a scheduling algorithm in the context of a set of applications, by learning the association rules between the applications' attributes and the algorithms' performance metrics. The new methodology that is presented serves to both increase the designer's knowledge about a particular scheduling algorithm and compare algorithms.

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Efficient Scheduling of Task Graphs to Multiprocessors Using a Combination of Modified Simulated Annealing and List Based Scheduling

Cited by 13 publications

References 14 publications

A Proposed Meta-Heuristic Approach for Cloudlets Scheduling in Cloud Computing Environment

A Proposed Meta-Heuristic Approach for Cloudlets Scheduling in Cloud Computing Environment

Towards an automated framework for task scheduling

Association rules learning technique for knowledge mining about scheduling algorithm performance

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