Multi Objective Task Scheduling in Cloud Environment Using Nested PSO Framework

Jena, Rabindra Kumar

doi:10.1016/j.procs.2015.07.419

Cited by 132 publications

(51 citation statements)

References 13 publications

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“…The imitation learning operation is defined as (13). , POP POP , we should reset the value of the variable according to the formula (12). After carrying out the imitation learning operation, the greedy operation is performed, and the individuals with better fitness are retained.…”

Section: 1mentioning

confidence: 99%

“…Recently, some research work focuses on this problem and propose several methods, most of these methods are designed based on evolutionary algorithms, as this kind of algorithm has strong heuristic algorithm optimization ability. Some evolutionary algorithms (such as genetic algorithm [6][7][8][9], particle swarm algorithm [10][11][12], ant colony algorithm [13,14], bee colony [15], and cuckoo algorithm [4,16]) has been used to solve the problem of task scheduling in cloud computing. However, above evolutionary algorithms still have some shortcomings, such as slow convergence speed, easy to fall into local optimum, etc.…”

Section: Introductionmentioning

confidence: 99%

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Effective Task Scheduling in Cloud Computing Based on Improved Social Learning Optimization Algorithm

et al. 2017

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Abstract-For the typical optimal problem of task scheduling in cloud computing, this paper proposes a novel resource scheduling algorithm based on Social Learning Optimization Algorithm (SLO). SLO is a new swarm intelligence algorithm which is proposed by simulating the evolution process of human intelligence and has better optimization mechanism and optimization performance. This paper proposes two learning operators for task scheduling in cloud computing after analyzing the characteristics of the problem of task scheduling; then, by introducing the Small Position Value (SPV) method, the two learning operators with continuous nature essence are enabled to solve the problem of task scheduling, and then the improved SLO is employed to solve the problem of cloud resource optimal scheduling. Finally, the performance of improved SLO is compared with existing research work on the CloudSim platform. Experimental results show that the approach proposed in this paper has better global optimization ability and convergence speed.

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Section: 1mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Effective Task Scheduling in Cloud Computing Based on Improved Social Learning Optimization Algorithm

et al. 2017

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“…In their work, authors discussed how the energy in multiprocessor affects with the time relation constraints. Jena et al [16] have focused a theory on task scheduling using a multi objective optimization method for energy consumption with the related of processing time. Da-Ren et al [17] have invented a model of variable voltage depend processor with the discrete time voltage/speed.…”

Section: Related Workmentioning

confidence: 99%

Optimization of Energy Consumption for Task Scheduling on Uni-Processor and Multiprocessor for Step Topology under Distributed Environment

Verma¹,

Saxena²

2017

IJCA

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Distributed computer networking plays a very crucial role in the Business, Industries, Education, Research and Development areas. Many users work on the heterogeneous devices which have different configurations. In distributed network communication takes place from one to one machine, one to many machines or many to one machine. Hence, tasks are migrated from one device to another device which is the important property of the distributed system. Due to rapid increase of the users on the devices connected across the distributed network, the management of the computer networks is a very big and challenging area of research. In the present work, different devices are connected across the step topological networks and an attempt is made to reduce the overall energy consumption when data is flowing from one device to another device. Optimization of energy consumption reduces the overall cost of transfer of data. Multiprocessor and Uni-processor cases are considered in special cases and computed results are represented in the form of tables. A well known Hungarian methodology is used for optimization of the overall energy.

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“…In terms of resources utilization and makespan Hybrid PSO implementation in [9] tried to balance the load across the system and minimize the makespan. In [10], authors presented multi-objective PSO based optimization algorithm for dynamic environment of clouds and optimize energy and processing time. Proposed algorithm provides an optimal balance results for multiple objectives.…”

Section: Particle Swarm Optimization (Pso)mentioning

confidence: 99%