A Survey of Load Balanced Job Scheduling Schemes in Cloud Computing

Nasim, Shamma Shabnam; Mizan, Chowdhury Md.; Chakroborty, Tridib; Ghosh, Sudeep; Karmakar, Suparna

doi:10.1007/978-981-15-6014-9_20

Cited by 4 publications

(2 citation statements)

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“…) have performed from the perspective of RT and DCPT, where x and y are the dimensions in the equations. We take into consideration algorithms named AIS DSBPF and AIS DSBPS that are derivations of AIS DSBP and contain, respectively, equation (7) and equation (8). Affinity is set as the default equation( 6) function in the AIS DSBP algorithm.…”

Section: Comparison Of the Efficiency Of Algorithmsmentioning

confidence: 99%

“…As part of the load-balancing procedure in cloud computing, end users are the ones that request jobs from the cloud, queue them up, and then schedule them. The task is completed after the virtual machine (VM) management assigns VMs to each of the tasks [7]. Since cloud computing is growing swiftly and users want more services and better results, the load balancing management component in cloud computing systems should be able to allocate resources based on user needs [8].…”

Section: Introductionmentioning

confidence: 99%

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Cost-effective clonal selection and AIS-based load balancing in cloud computing environment

Mosayebi¹,

Azmi²

2023

Preprint

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One of the challenges in cloud computing is increasing efficiency, which calls for the application of task scheduling and load balancing. Numerous load-balancing methods that have been suggested for the cloud environment can increase efficiency. The Throttled policy is one of them that aids in evenly distributing user requests among virtual machines. In the first part of this article, we examined the cost of the Clonal selection algorithm with Throttled load balancing Policy and the Clonal selection algorithm with Distributed Service Broker Policy and contrasted them to the clonal selection and other immune systems mechanisms. Additionally, we suggest two additional mechanisms based on the artificial immune system. In the two proposed mechanisms, algorithms are introduced so that by benefiting from load balancing policies when receiving requests from users, in the shortest possible time and the fastest processing of data and with the lowest cost, they respond to users. In the second part, we introduce two other new algorithms that were created as a result of combining an evolutionary algorithm with the artificial immune system mechanisms. Algorithms have been evaluated and compared with other algorithms using the Cloud Analyst simulator in terms of average response time, average data center processing time, total data transfer cost, and total cost. The results attained show how much better the suggested algorithms are.

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Section: Comparison Of the Efficiency Of Algorithmsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%