An Evolutionary Multi-Objective Approach for Resource Scheduling in Mobile Cloud Computing

Dasari, Nagaraju; Saritha, V.

doi:10.22266/ijies2017.0228.02

Cited by 4 publications

(7 citation statements)

References 17 publications

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“…In all the cases AWT of our proposed approach is found to be less than existing approaches [17,20]. The reason behind is the effective resource selection and task scheduling approach on the basis of cost and task workload respectively.…”

Section: Case 3-simulation With Tasks = Resources (M=1)mentioning

confidence: 83%

“…For the simulation of proposed resource allocation model, GridSim toolkit is used and results are compared with existing approaches [17] [20]. The results are compared for the reduction of AWT of tasks.…”

Section: Simulation and Resultsmentioning

confidence: 99%

“…A genetic algorithm based resource scheduling is presented for mobile cloud computing by [2,20]. The crossover and mutation is used to generate new population.…”

Section: Related Workmentioning

confidence: 99%

“…The Fig. 2 shows the AWT of Existing approaches [17,20] and proposed approach versus number of tasks. The number of tasks varies from 6-100.…”

Section: Tec=(7×18)+(5×24)+(3×27)+(22×33)+(15×36)+ (13×30) = 1983 $mentioning

confidence: 99%

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An Improved Resource Allocation Model for Grid Computing Environment

Shukla¹,

Kumar²,

Singh³

2019

IJIES

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Grid is collection of heterogeneous resources which are highly dynamic and unpredictable in nature. Various resource allocation techniques are used for effective use of resources. Tasks are submitted with constraints (cost, deadline, etc.) to improve the performance of distributed environment. Based on resource availability, several tasks select the same resource to achieve minimum execution cost. Therefore, an effective scheduling is required to execute tasks on the same resource to reduce average waiting time of tasks. In this paper, an extensive survey of resource allocation techniques is done based on several parameters. A resource allocation and task scheduling mechanism is proposed to optimize cost and average waiting time of heterogeneous tasks. The uniqueness of the proposed model is that it makes task execution schedule and sends calculated data to resource manager before actual task execution for reducing average waiting time of tasks. Further, we validate the effectiveness of the proposed model using GridSim by considering random values of task and resources. Experimental results show that the proposed model provides up to 37% reduction in average waiting time than existing approaches under various environment conditions.

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Section: Case 3-simulation With Tasks = Resources (M=1)mentioning

confidence: 83%

Section: Simulation and Resultsmentioning

confidence: 99%

“…A genetic algorithm based resource scheduling is presented for mobile cloud computing by [2,20]. The crossover and mutation is used to generate new population.…”

Section: Related Workmentioning

confidence: 99%

“…The Fig. 2 shows the AWT of Existing approaches [17,20] and proposed approach versus number of tasks. The number of tasks varies from 6-100.…”

Section: Tec=(7×18)+(5×24)+(3×27)+(22×33)+(15×36)+ (13×30) = 1983 $mentioning

confidence: 99%

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An Improved Resource Allocation Model for Grid Computing Environment

Shukla¹,

Kumar²,

Singh³

2019

IJIES

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“…Nagaraju, D. and Saritha, V [17] presented the multi objective genetic algorithm for mobile cloud (MOGAMCC) environment. In the direction of devicing the MOGAMCC, the cloudsim toolkit was protracted with the MOGA and its task planning approach governs the optimal scheduling policy.…”

Section: Literature Reviewmentioning

confidence: 99%

Multi Objective Task Scheduling Using Modified Ant Colony Optimization in Cloud Computing

Reddy¹,

Phanikumar²

2018

IJIES

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Abstract:Cloud computing is the development of distributed computing, parallel computing, and grid computing, or defined as a commercial implementation of such computer science concepts. One of the main issues in a cloud computing environment is Task scheduling (TS). In Cloud task scheduling, many Non deterministic Polynomial time-hard optimization problem, and many meta-heuristic (MH) algorithms have been proposed to solve it. A task scheduler should adapt its scheduling strategy to changing environment and variable tasks. This paper amends a cloud task scheduling policy based on Modified Ant Colony Optimization (MACO) algorithm. The main contribution of recommended method is to minimize makespan and to perform Multi Objective Task Scheduling (MOTS) process by assigning pheromone amount relative to corresponding virtual machine efficiency. MACO algorithm improves the performance of task scheduling by reducing makespan and degree of imbalance comparatively lower than a basic ACO algorithm by its multi-objective and deliberate nature. Experimental outcomes have shown that proposed MACO to have makespan 350 milliseconds and average utilization of 0.51 for a set of 100 tasks.

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