Multi-heuristic dynamic task allocation using genetic algorithms in a heterogeneous distributed system

Page, Andrew J.; Keane, Thomas; Naughton, Thomas J.

doi:10.1016/j.jpdc.2010.03.011

Cited by 89 publications

(35 citation statements)

References 21 publications

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“…For example, [38] presented a GA-based study of two task allocation models in distributed computing systems. A type of GA has also been capable to map tasks dynamically to processors in a heterogeneous distributed system [39].…”

Section: Related Workmentioning

confidence: 99%

Towards organizing smart collaboration and enhancing teamwork performance: a GA-supported system oriented to mobile learning through cloud-based online course

Sun

Shen

2014

Int. J. Mach. Learn. & Cyber.

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(2014). Towards organizing smart collaboration and enhancing teamwork performance: a GA-supported system oriented to mobile learning through cloud-based online course. International Journal of Machine Learning and Cybernetics, 7 (3), 391-409. Towards organizing smart collaboration and enhancing teamwork performance: a GA-supported system oriented to mobile learning through cloud-based online course AbstractAt present, people's learning styles become diverse, for which mobile learning is a significant trend that enables distributed learners to achieve collaborative learning and allows them to be engaged in virtual teams to work together. In this research, we develop a system to cope with the problem in current mobile learning circumstance, where learners in virtual teams generally lack proper communications, guidance and assistances between each other. Following the theory of Kolb team learning experience, we use our system, Teamwork as a Service (TaaS), to work in conjunction with cloud-based learning management systems. This system targets at organizing a series of learning activities and then forming a learning flow in order to allow learners to participate into smart collaborations. Executing the five web services of TaaS sequentially, learners can have their collaborative learning arranged in a better environment, where they are able to know about one another, be grouped into cloud-based 'Jigsaw Classroom', plan and publish tasks and supervise other learners mutually. In particular, one primary point of enhancing learners' teamwork performance is to offer them computational choice of task allocation. For this reason, we model the social features related to the collaborative learning activities, and introduce a genetic algorithm (GA) approach to group learners into appropriate teams with two different team formation scenarios. The technical details of the operation principle of GA are illustrated thoroughly. Finally, experimental results are presented to prove our approach is workable to facilitate teamwork with considerations of learner's capabilities and preferences. We also demonstrate our implementation details of the newly designed TaaS over the Amazon cloud and discuss the main improvements for collaborative learning brought by TaaS. Abstract At present, people's learning styles become diverse, for which mobile learning is a significant trend that enables distributed learners to achieve collaborative learning and allows them to be engaged in virtual teams to work together. In this research, we develop a system to cope with the problem in current mobile learning circumstance, where learners in virtual teams generally lack proper communications, guidance and assistances between each other. Following the theory of Kolb team learning experience, we use our system, Teamwork as a Service (TaaS), to work in conjunction with cloud-based learning management systems in order to organize a series of learning activities and then form a learning flow. Executing the five web services of TaaS sequentially, learner...

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Section: Related Workmentioning

confidence: 99%

Towards organizing smart collaboration and enhancing teamwork performance: a GA-supported system oriented to mobile learning through cloud-based online course

Sun

Shen

2014

Int. J. Mach. Learn. & Cyber.

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“…In the past yeas, several classical heuristic algorithm have been proposed for solving this problem. Unfortunately, early studies mainly concentrate on system performance, i.e., throughput [21], load-balance [22], response time [16][17][18][19] and etc., instead of energy efficiency. Recently, researchers began to take more efforts on energy saving when scheduling workflow applications.…”

Section: Related Workmentioning

confidence: 99%

Optimizing Data-Accessing Energy Consumption for Workflow Applications in Clouds

He¹,

Liu²

2014

IJFGCN

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“…A task allocation algorithm based on multi-heuristic evolutionary approach for heterogeneous distributed system is presented in [16]. It manoeuvres on batches of unmapped tasks and tasks are dynamic and pre-emptive in nature.…”

Section: Releted Workmentioning

confidence: 99%

“…The scheduling of jobs is primarily concerned for the proper functionality of a grid computing system. Genetic algorithms [3,4,8,9,10,13,14,16] are search heuristics, to find optimal solution job scheduling as compared to other search procedures. It is a method for solving optimization problems and based on process of natural genetics.…”

Section: Introductionmentioning

confidence: 99%

Load Balancing Approach for Scheduling Sequential Task in Grid Computing Environment

Pandey¹,

Verma²,

Tamta³

2013

IJCA

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In grid computing environment, the efficiency of computing node is affected by several factors such as node utilization, allocation of jobs etc. The incoming job is allocated to appropriate node in such a way that the node utilization is maximized and a well-balanced load across all the participating computing nodes that enhances the overall performance of grid computing. This paper presents an amended hybrid approach for scheduling sequential task. The proposed approach uses combination of first-come-firstserved (FCFS) and genetic algorithm (GA). A sliding-window technique is presented to initiate alteration between the FCFS and GA, to offers a rapid task assignment. For GA we initially generate random population and use straightforward encoding. The proposed method is evaluated in the terms of makespan value and node utilization with a varying set of simulation cases and parameters after then it is compared with a well design first-come-first-server (FCFS) and hybrid genetic algorithm (HGA). Experimental results have shown significant improvement compared to the both FCFS and HGA algorithms. The result gives minimized makespan value with increased node utilization for both homogeneous and heterogeneous types of nodes.

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Multi-heuristic dynamic task allocation using genetic algorithms in a heterogeneous distributed system

Cited by 89 publications

References 21 publications

Towards organizing smart collaboration and enhancing teamwork performance: a GA-supported system oriented to mobile learning through cloud-based online course

Towards organizing smart collaboration and enhancing teamwork performance: a GA-supported system oriented to mobile learning through cloud-based online course

Optimizing Data-Accessing Energy Consumption for Workflow Applications in Clouds

Load Balancing Approach for Scheduling Sequential Task in Grid Computing Environment

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