A hybrid algorithm for efficient task scheduling in cloud computing environment

Thanka, M. Roshni; Maheswari, P. Uma; Edwin, E. Bijolin

doi:10.1504/ijris.2019.099850

Cited by 7 publications

(4 citation statements)

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“…This kind of algorithm has attracted more and more attention because of its simple principle and the advantages of quickly solving the target. With the continuous improvement and development of optimization algorithms, many swarm intelligence algorithms have been widely used, among these the most widely used algorithms mainly include ant colony, particle swarm, and artificial fish swarm methods and genetic algorithm ( Figure 2 ) [ 13 ].…”

Section: Research On Optimal Scheduling Algorithmmentioning

confidence: 99%

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[Retracted] Income Gap Equalization Based on PSO Algorithm in Edge Computing Environment

Zhao

Liu

2022

Journal of Environmental and Public Health

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In order to explore the current income gap, a method based on the PSO algorithm in the edge computing environment is proposed. PSO calculates and simulates bird flock foraging activities, the Frank Heppner biological group model, and the three rules in bird activities. After studying the activities of these natural creatures, abstract problems are quantified and similar models established. The Gini coefficient is calculated by using grouped data, and the grouping basis is also innovative. The quantile grouping method is adopted, which can effectively solve the difference between the concentration index and the Gini coefficient, and the Gini coefficients of each year can be added up to finally get the Gini coefficient of the stock income. Experimental results show that the Gini coefficient of traffic income in 2017 and 2018 had dropped significantly, but the variation of the Gini coefficient of stock income (Delta CG) was still greater than 0. Obviously, the adjustment speed of the Gini coefficient of stock income was lagging behind, as was the Gini coefficient of traffic income. We found that after 1986, the facilitation effect was greater than the dilution effect, and the facilitation effect continued to push up the stock income gap, which indicated more income flow to the high-income group, with the income flow gap showing an upward trend and the upward trend becoming more and more obvious. It has been proved that the PSO algorithm can effectively identify the income gap in the edge computing environment, and the corresponding policy suggestions are given.

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Section: Research On Optimal Scheduling Algorithmmentioning

confidence: 99%

“…From this, we can obtain the Gini coefficient calculation formulas (13)(14)(15) under the continuous distribution. After identity transformation, the following calculation formulas can be obtained:…”

Section: Calculation Of Gini Coefficientmentioning

confidence: 99%

[Retracted] Income Gap Equalization Based on PSO Algorithm in Edge Computing Environment

Zhao

Liu

2022

Journal of Environmental and Public Health

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“…For future work, the authors planned to enhance the proposed algorithm by using heuristic algorithms characteristics in dynamic task scheduling algorithms with artificial intelligence technologies. (Thanka, et al, 2019) have advanced a hybrid task scheduling algorithm in cloud computing environment based on Artificial Bee colony strategy and Particle Swarm optimization called it ABPS algorithm. The main purpose of ABPS task scheduling algorithm was to minimize makespan, cost, and maximize resource utilization and balance the load.…”

Section: Quality Of Service (Qos) Based Algorithmsmentioning

confidence: 99%

Classification and Performance Study of Task Scheduling Algorithms in Cloud Computing Environment

2022

IKM

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Cloud computing is becoming very common in recent years and is growing rapidly due to its attractive benefits and features such as resource pooling, accessibility, availability, scalability, reliability, cost saving, security, flexibility, on-demand services, pay-per-use services, use from anywhere, quality of service, resilience, etc. With this rapid growth of cloud computing, there may exist too many users that require services or need to execute their tasks simultaneously by resources provided by service providers. To get these services with the best performance, and minimum cost, response time, makespan, effective use of resources, etc. an intelligent and efficient task scheduling technique is required and considered as one of the main and essential issues in the cloud computing environment. It is necessary for allocating tasks to the proper cloud resources and optimizing the overall system performance. To this end, researchers put huge efforts to develop several classes of scheduling algorithms to be suitable for the various computing environments and to satisfy the needs of the various types of individuals and organizations. This research article provides a classification of proposed scheduling strategies and developed algorithms in cloud computing environment along with the evaluation of their performance. A comparison of the performance of these algorithms with existing ones is also given. Additionally, the future research work in the reviewed articles (if available) is also pointed out. This research work includes a review of 88 task scheduling algorithms in cloud computing environment distributed over the seven scheduling classes suggested in this study. Each article deals with a novel scheduling technique and the performance improvement it introduces compared with previously existing task scheduling algorithms.

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“…Using the suggested algorithm, incoming jobs are allocated to the VMs to improve cloud computing's task scheduling by decreasing the makespan. Likewise, an artificial bee and particle swarm algorithm was suggested by Thanka et al [25] for task scheduling. To minimize the makespan, Pradhan and Bisoy [26] presented the modified particle swarm optimization algorithm.…”

Section: Introductionmentioning

confidence: 99%

Cloud computing: an efficient load balancing and scheduling of task method using a hybrid optimization algorithm

Ravinder,

Vurukonda

2023

IJEECS

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<p>The cloud computing trend on the internet is vital as it allows data and applications to be managed over the internet instead of requiring personal devices. The job of users is scheduled in the resources of the cloud in order to improve performance. Schedu ling tasks is an non - deterministic polynomial (NP) - hard problem, as it may have multiple solutions. Various researchers have proposed different load balancing and job scheduling algorithms to optimize the scheduling process in cloud environments, each with disadvantages. Therefore, this research proposes a novel hybrid load balancing and scheduling of tasks by the whale optimization algo rithm (WOA) and seagull optimization algorithm (SOA) in the cloud. This hybrid proposed whale - seagull optimization algorithm (WSOA) optimizes task scheduling in the cloud b y reducing processing time, response, and execution time, maximizing central processing unit (CPU) utilization, memory utilization, throughput, reliability, and balancing the load. The algorithm is simulated using the CloudSim toolkit package. As compared with existing approaches, simulation results showed better performance in terms of response time, processing time, execution time, CPU utilization, memory utilization, throughput, and reliability and is analyzed by comparing with the harries hawks optimiza tion (HHO), hybrid dragonfly and firefly algorithm (ADA), spider monkey algorithm (SMA) and bird swarm optimization (BSO).</p>

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A hybrid algorithm for efficient task scheduling in cloud computing environment

Cited by 7 publications

References 0 publications

[Retracted] Income Gap Equalization Based on PSO Algorithm in Edge Computing Environment

[Retracted] Income Gap Equalization Based on PSO Algorithm in Edge Computing Environment

Classification and Performance Study of Task Scheduling Algorithms in Cloud Computing Environment

Cloud computing: an efficient load balancing and scheduling of task method using a hybrid optimization algorithm

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