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TAR Based Hotspot Prediction in Cloud Data Centres

TAR Based Hotspot Prediction in Cloud Data Centres

Anu Valiyaparambil Raveendran, Elizabeth Sherly Sherly
Copyright: © 2019 |Volume: 11 |Issue: 3 |Pages: 22
ISSN: 1938-0259|EISSN: 1938-0267|EISBN13: 9781522564928|DOI: 10.4018/IJGHPC.2019070101
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MLA

Raveendran, Anu Valiyaparambil, and Elizabeth Sherly Sherly. "TAR Based Hotspot Prediction in Cloud Data Centres." IJGHPC vol.11, no.3 2019: pp.1-22. http://doi.org/10.4018/IJGHPC.2019070101

APA

Raveendran, A. V. & Sherly, E. S. (2019). TAR Based Hotspot Prediction in Cloud Data Centres. International Journal of Grid and High Performance Computing (IJGHPC), 11(3), 1-22. http://doi.org/10.4018/IJGHPC.2019070101

Chicago

Raveendran, Anu Valiyaparambil, and Elizabeth Sherly Sherly. "TAR Based Hotspot Prediction in Cloud Data Centres," International Journal of Grid and High Performance Computing (IJGHPC) 11, no.3: 1-22. http://doi.org/10.4018/IJGHPC.2019070101

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Abstract

In this article, the authors studied hotspots in cloud data centers, which are caused due to a lack of resources to satisfy the peak immediate requests from clients. The nature of resource utilization in cloud data centers are totally dynamic in context and may lead to hotspots. Hotspots are unfavorable situations which cause SLA violations in some scenarios. Here they use trend aware regression (TAR) methods as a load prediction model and perform linear regression analysis to detect the formation of hotspots in physical servers of cloud data centers. This prediction model provides an alarm period for the cloud administrators either to provide enough resources to avoid hotspot situations or perform interference aware virtual machine migration to balance the load on servers. Here they analyzed the physical server resource utilization model in terms of CPU utilization, memory utilization and network bandwidth utilization. In the TAR model, the authors consider the degree of variation between the current points in the prediction window to forecast the future points. The TAR model provides accurate results in its predictions.

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