A Basic Simulation of ACO Algorithm Under Cloud Computing for Fault Tolerant

Kushwah, Virendra Singh; Goyal, Sandip Kumar

doi:10.1007/978-981-10-1675-2_46

Cited by 7 publications

(4 citation statements)

References 13 publications

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“…Lastly, they have tried to achieve better scalability. Kushwah et al [9] concentrated on conceptualized contemplate on adaptation to internal failure with ACO calculation. Different calculations are pro-posed by others creators however this paper is a coordination of all.…”

Section: Literature Surveymentioning

confidence: 99%

Measuring Throughput for Fault Tolerant Based ACO Algorithm under Cloud Computing: A Comparison Study

Kushwah¹,

Goyal²,

Sharma³

2018

IJET

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Any technical problem can be main cause for any fault. Due to any fault, system would be suffered the work and enhance the system cost in term of money and others. There are many algorithms for fault tolerant in cloud computing and make comparison with fault tolerant based ant colony optimization and which is used to minimize fault during load balancing. In this paper, throughput is measured by such kind of fault tolerant based algorithms and determines that which algorithm is better. It has been compared with ACO. After such comparison, it is clearly determined that ACO has good functionalities to have better throughput. Comparative study is shown by the graphically and finally described that ACO is better than others in context of throughput calculation are. ACO is itself a meta-heuristic algorithm and better optimization technique.

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Section: Literature Surveymentioning

confidence: 99%

Measuring Throughput for Fault Tolerant Based ACO Algorithm under Cloud Computing: A Comparison Study

Kushwah¹,

Goyal²,

Sharma³

2018

IJET

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“…Scheduling resources efficiently is the focus of cloud computing research at present [1]. Many researchers have applied various intelligent algorithms such as the genetic algorithm [2][3], particle swarm optimization (PSO) [4][5], and ant colony optimization (ACO) [6][7] to resource scheduling and achieved good results. In 2014, Meng put forward chicken swarm optimization (CSO) [8], which is a random optimization algorithm based on population.…”

Section: Introductionmentioning

confidence: 99%

Chicken Swarm Optimization in Task Scheduling in Cloud Computing

Li-ru¹

2019

IJPE

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In order to solve the problem of low efficiency in resource scheduling in cloud computing, an improved chicken swarm optimization (CSO) is proposed for task scheduling. Firstly, the concept of opposition-based learning is introduced to initialize the chicken population and improve the global search ability. Secondly, the concepts of the weight value and learning factor in particle swarm optimization (PSO) are introduced to improve the positions of chickens, and the individual positions of chickens are optimized. Thirdly, the overall individual positions of the CSO are optimized by the difference algorithm. Finally, the possible cross-boundary of individual positions in the algorithm is prevented as a whole by boundary processing. In the simulation experiment, the optimized CSO is compared with the basic CSO, PSO, and ant colony optimization (ACO) in terms of completion time, cost, energy consumption, and load balancing, and good results are achieved.

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“…Selecting a task scheduling algorithm with excellent performance can allow cloud users to run stably and efficiently in the cloud computing platform, shorten the completion time required for tasks, and ensure the economic benefits of cloud computing service providers to the greatest extent [1]. References [2][3][4][5][6][7] proposed to use the basic ant colony algorithm, particle swarm optimization algorithm, and genetic algorithm to achieve certain effects in cloud computing resource scheduling. Ebadifard [8] proposed a static task scheduling method based on the particle swarm optimization (PSO) algorithm, where the tasks are assumed to be non-preemptive and independent.…”

Section: Introductionmentioning

confidence: 99%

Scheduling Algorithm for a Task under Cloud Computing

Yao¹

2019

Int J Performability Eng

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Aiming at the problem of low efficiency of task scheduling in cloud computing, this paper first analyzes the task of cloud computing task scheduling. Secondly, the pheromone setting quality function of the ant colony algorithm and the empirical feedback factor of selection probability are improved. The new method is adopted for the colonial calculation method and boundary value processing in the imperial competition algorithm. Finally, the two algorithms are merged to obtain a cloud computing task scheduling algorithm based on the ant colony algorithm-empire competition algorithm. In the simulation experiment, the algorithm demonstrates certain advantages in terms of task execution time, execution cost, and load rate.

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A Basic Simulation of ACO Algorithm Under Cloud Computing for Fault Tolerant

Cited by 7 publications

References 13 publications

Measuring Throughput for Fault Tolerant Based ACO Algorithm under Cloud Computing: A Comparison Study

Measuring Throughput for Fault Tolerant Based ACO Algorithm under Cloud Computing: A Comparison Study

Chicken Swarm Optimization in Task Scheduling in Cloud Computing

Scheduling Algorithm for a Task under Cloud Computing

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