Locality-Load-Prediction Aware Multi-Objective Task Scheduling in the Heterogeneous Cloud Environment

Balagoni, Yadaiah; Rao, R. Srinivasa

doi:10.17485/ijst/2017/v10i9/106576

Cited by 8 publications

(13 citation statements)

References 44 publications

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“…Task scheduling policy in a heterogeneous cloud environment was investigated in Balagoni and Rao. 21 In cloud computing, resources and workloads are geographically distributed. In this phenomenon, it is very difficult to correctly match the VMs with different workloads.…”

Section: Related Workmentioning

confidence: 99%

“…An extensive literature exists on customer satisfaction and revenue maximization. To check the supremacy of this work, we compared this work with other studies 15,16,[18][19][20][21][22][23] and Nazanin et al 24 For the comparative analysis, criteria given in Table 6 is used to assess the working of previous studies. Table 7shows the detailed comparison study of previous works.…”

Section: Comparative Analysismentioning

confidence: 99%

“…Resources provisioning strategies have a big share in performance and cost. Task scheduling policy in a heterogeneous cloud environment was investigated in Balagoni and Rao 21 . In cloud computing, resources and workloads are geographically distributed.…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Smart workload migration on external cloud service providers to minimize delay, running time, and transfer cost

Badshah

Ghani

Irshad³

et al. 2020

Int J Communication

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Summary With limited resources, overutilization is a challenging issue in the emerging system of smart devices, resulting in higher costs, poor performance and low prices due to service level agreement (SLA) violations. These factors extremely annoy the customers as well as providers. External resources are hired to overcome the resources scalability challenges; however, external resources cause higher delay, running time, and transfer cost. The lift in delay explicitly means more cost and more customers dissatisfaction. The delay and transfer cost increases with geographical distance and overutilization. In this article, a mechanism is proposed to make efficient migration decisions to external cloud service providers (CSPs) to minimize the delay, running time, and transfer cost by searching an optimum data center (DC), where the resources may be taken with optimum conditions. The Cloud Analyst is extended to simulate the proposed framework. Results were calculated for three different phenomena, where hiring the resources from underutilized DC decreases the running time to 0.237 ms; however, the response time and transfer cost increases to 499 ms and 0.228 $, respectively; similarly, getting the resources of nearest DC drops the delay time and transfer cost to 80 ms and 0.065 $ respectively; however, the execution time increases to 0.500 ms. The proposed framework optimized the delay time, execution time and transfer cost to 50 ms, 0.237 ms, and 0.065 $, respectively.

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Section: Related Workmentioning

confidence: 99%

Section: Comparative Analysismentioning

confidence: 99%

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Smart workload migration on external cloud service providers to minimize delay, running time, and transfer cost

Badshah

Ghani

Irshad³

et al. 2020

Int J Communication

View full text Add to dashboard Cite

show abstract

“…Balagoni and Rao [27] worked on a task‐scheduling policy in the heterogeneous cloud environment. In cloud computing, resources and workloads are geographically distributed.…”

Section: Related Workmentioning

confidence: 99%

Optimising infrastructure as a service provider revenue through customer satisfaction and efficient resource provisioning in cloud computing

et al. 2019

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“…There are some factors explored by many researches for Hadoop MapReduce optimization. Some researchers focused on the scheduling techniques to improve the overall job execution time of MapReduce jobs [5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Replication Effect over Hadoop MapReduce Performance using Regression Analysis

Shabbir¹,

Bakar²,

Radzi³

2018

IJCA

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Hadoop MapReduce is the community accepted platform that deals with the gigantic data in an efficient and cost-effective manner. To cope up with ever growing datasets and shrinking time to analyze them, Hadoop MapReduce leveraged parallelize computations on large distributed clusters consisting of many machines. Careful consideration of the factors affecting the Hadoop MapReduce can enhance its performance. Many researches has been done for improving the total job execution time of MapReduce by optimizing different parameters. The replication factor is still unexplored for its effect on the MapReduce job completion time. This paper focuses on the evaluation of data replication factor on MapReduce job completion time using regression analysis. The performance of the Hadoop MapReduce job in terms of total job completion time is monitored experimentally by changing different values of replication. The evaluation results evidently shows the dependence of the job completion time on the replication factor. The dependence of total job completion time on the replication has been verified both analytically and experimentally.

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Locality-Load-Prediction Aware Multi-Objective Task Scheduling in the Heterogeneous Cloud Environment

Cited by 8 publications

References 44 publications

Smart workload migration on external cloud service providers to minimize delay, running time, and transfer cost

Smart workload migration on external cloud service providers to minimize delay, running time, and transfer cost

Optimising infrastructure as a service provider revenue through customer satisfaction and efficient resource provisioning in cloud computing

Replication Effect over Hadoop MapReduce Performance using Regression Analysis

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