Response Time Minimization of Different Load Balancing Algorithms in Cloud Computing Environment

Jena, Soumya Ranjan; Ahmad, Zulfikhar

doi:10.5120/12064-8144

Cited by 12 publications

(2 citation statements)

References 4 publications

(4 reference statements)

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“…Additionally, Cloud Analyst keeps internet characteristics impacted by the bandwidth issue. Therefore, according to equation(3), the transmission delay depends on the number of user requests transmitted (N) and the whole available bandwidth in the Internet network (Bwtotal) which is mentioned in equation( 4) [61][62][63].…”

Section: Overall Response Timementioning

confidence: 99%

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: Overall Response Timementioning

confidence: 99%

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

Mosayebi¹,

Azmi²

2023

Preprint

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“…If more than one data center is in the same region, then the data center having less cost will be selected. Ram Prasad et al have studied divisible load scheduling theory in cloud computing [2]. Kumar Nishant et al proposed Ant colony optimization to improve the load balance [3].…”

Section: Realted Workmentioning

confidence: 99%

Modifying Broker Policy for Better Distribution of the Load Over Geo-distributed Datacenters

Sheikhani¹,

Ding²,

Talwana³

et al. 2019

IOTCC

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As an increasing number of businesses move toward Cloud based services, issues such as reduce response time, optimize cost, and load balance over data centers are important factor that need to be studied. Selecting the suitable data center to handle the user request is affecting those factors directly. The Broker policy determines which data center should service the request from each user base; so choosing appropriate policy can improve the performance noticeably. One of the benchmarks policies is service proximity-based that routing the request to the data center, which has lowest network latency or minimum transmission delay from a user base. If there are more than one data centers in a region in close proximity, then one of the data centers is selected at random to service the incoming request. However, other factors such as cost, workload, number of virtual machines, processing time etc., are not taken into consideration. Randomly selected data center gives undesirable results in terms of response time, data processing time, cost, and other parameters. this work propose modifying that policy by applying new schedule algorithm that control the load balance. the results showed that the using of this algorithm instead of the random selection would improve the distribution of the workload over the available datacenters noticeably.

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Load Balancing on Cloud Analyst Using First Come First Serve Scheduling Algorithm

Saeed

Javaid

Zubair

et al. 2018

Lecture Notes on Data Engineering and Communications Technologies

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Response Time Minimization of Different Load Balancing Algorithms in Cloud Computing Environment

Cited by 12 publications

References 4 publications

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

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

Modifying Broker Policy for Better Distribution of the Load Over Geo-distributed Datacenters

Load Balancing on Cloud Analyst Using First Come First Serve Scheduling Algorithm

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