Opposition-based learning inspired particle swarm optimization (OPSO) scheme for task scheduling problem in cloud computing

Agarwal, Mohit; Srivastava, Gur Mauj Saran

doi:10.1007/s12652-020-02730-4

Cited by 35 publications

(16 citation statements)

References 88 publications

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“…Figure 4. Comparison of algorithm convergence It can be seen from fig.4that the algorithm in this paper starts to converge after 700 generations of evolution, while the algorithms in literature[8][9][10] all start to converge after 850 generations of evolution, which shows that the convergence speed of the algorithm in this paper is better than that in literature. In terms of fitness, the average fitness of the proposed algorithm is consistently better than that of the literature algorithms in the evolution process, which indicates that the proposed algorithm effectively simulates the diversity maintenance and antibody response mechanism of the immune system, and has high local optimization ability.…”

mentioning

confidence: 76%

“…In terms of fitness, the average fitness of the proposed algorithm is consistently better than that of the literature algorithms in the evolution process, which indicates that the proposed algorithm effectively simulates the diversity maintenance and antibody response mechanism of the immune system, and has high local optimization ability. In a word, the grid task scheduling result of this algorithm is better than that of the algorithm in literature[8][9][10].…”

mentioning

confidence: 80%

“…In order to verify the performance of this algorithm, two groups of comparative experiments were carried out with reference [8][9][10]. Using simulation software to create 1 024 grid computing resources and 600 tasks.…”

Section: Comparison Of Algorithm Convergencementioning

confidence: 99%

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Research on cloud computing task scheduling based on calculus mathematical equation

Zou¹

2022

HSET

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Due to the heterogeneity, distribution, autonomy and diversity of services in cloud computing environment, higher requirements are put forward for cloud platform scheduling mechanism, so the research on cloud architecture and its scheduling mechanism has attracted more and more attention from the industry. A cloud computing task scheduling algorithm based on calculus mathematical equation is proposed. Through the double boundary convergence control of the partial differential classification mathematical model, the partial differential classification data model is integrated into the data set, and the fuzzy control of the data is completed through the increment and decrement support vector. The membership function is used to transform the multi-QoS(quality of service) objective constraint problem into a single objective constraint solving problem. Compared with traditional methods, the method proposed in this paper can effectively reduce the deadline baseline violation rate of user task scheduling, and reduce its average task execution time and average task execution cost on the premise of meeting the user task multi-QoS target constraints.

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confidence: 76%

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confidence: 80%

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Research on cloud computing task scheduling based on calculus mathematical equation

Zou¹

2022

HSET

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“…Wang et al [27] in their work presented a load-balancing mechanism based on a hyper-heuristic algorithm to provide a system that distributes tasks in a balanced manner. The authors in [28] proposed a dynamic resource allocation technique that helps in reducing the energy consumption by data centers in a cloud computing environment, whereas Agarwal and Srivastava [29] proposed a task scheduling mechanism in which the initial population of the PSO is generated by an opposition-based learning concept so that diversity in the population can be achieved and results in improvement for the set of performance indicators.…”

Section: Related Workmentioning

confidence: 99%

An Adaptive Genetic Algorithm-Based Load Balancing-Aware Task Scheduling Technique for Cloud Computing

Agarwal¹,

Gupta²

2022

Computers, Materials &Amp; Continua

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Task scheduling in highly elastic and dynamic processing environments such as cloud computing have become the most discussed problem among researchers. Task scheduling algorithms are responsible for the allocation of the tasks among the computing resources for their execution, and an inefficient task scheduling algorithm results in under-or over-utilization of the resources, which in turn leads to degradation of the services. Therefore, in the proposed work, load balancing is considered as an important criterion for task scheduling in a cloud computing environment as it can help in reducing the overhead in the critical decision-oriented process. In this paper, we propose an adaptive genetic algorithm-based load balancing (GALB)aware task scheduling technique that not only results in better utilization of resources but also helps in optimizing the values of key performance indicators such as makespan, performance improvement ratio, and degree of imbalance. The concept of adaptive crossover and mutation is used in this work which results in better adaptation for the fittest individual of the current generation and prevents them from the elimination. CloudSim simulator has been used to carry out the simulations and obtained results establish that the proposed GALB algorithm performs better for all the key indicators and outperforms its peers which are taken into the consideration.

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“…Ewees et al (2021) proposed a multi-objective optimization method based on improved whale optimization algorithm by combining the differential evolution algorithm and the OBL. Agarwal and Srivastava (2021) proposed task scheduling mechanism based on PSO in which OBL technique is used to avoid premature convergence and to accelerate the convergence of standard PSO. Hussien (2021) proposed a novel version of salp swarm algorithm which is depends on OBL strategy.…”

Section: Introductionmentioning

confidence: 99%

Improved elephant herding optimization using opposition-based learning and K-means clustering to solve numerical optimization problems

Wang

2021

J Ambient Intell Human Comput

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Elephant herding optimization (EHO) algorithm which is a new swarm intelligence optimization algorithm was proposed in 2015. Its cores are clan updating operator and separation operator. Although EHO has achieved great success, there are still some shortcomings. For example, EHO generates clans with a fixed number of individuals. The separation operator is only applied to the worst individual of each clan, easily leading to low diversity of the population. In order to improve the performance of EHO, in this paper, we improved EHO and proposed an opposition-based learning (OBL) elephant herding optimization (OBLEHO) algorithm. First, we introduced the OBL strategy into the process of initialization and clan leader updating. Second, we used K-Means clustering to classify individuals with similar positions into the same clan and improved the clan updating operator. Third, we increased the number of separated individuals and introduced the Cauchy mutation operator. Finally, we applied an elitist strategy to the proposed algorithm. Subsequently, we used 29 benchmark functions of CEC 2017 and 5 sets of traveling salesman problem (TSP) to verify the performance of OBLEHO. The experimental results indicated that OBLEHO has great competitiveness in terms of benchmark functions and TSP.

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Opposition-based learning inspired particle swarm optimization (OPSO) scheme for task scheduling problem in cloud computing

Cited by 35 publications

References 88 publications

Research on cloud computing task scheduling based on calculus mathematical equation

Research on cloud computing task scheduling based on calculus mathematical equation

An Adaptive Genetic Algorithm-Based Load Balancing-Aware Task Scheduling Technique for Cloud Computing

Improved elephant herding optimization using opposition-based learning and K-means clustering to solve numerical optimization problems

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