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Data and Communication Networks pp 29–42Cite as

Analysis of Workloads for Cloud Infrastructure Capacity Planning

Part of the Advances in Intelligent Systems and Computing book series (AISC,volume 847)

Abstract

Workload analysis and characterization are the first steps toward effective cloud infrastructure capacity planning. Identifying workload patterns based on resource utilization not only enables informed decisions about mapping of current request to available capacity, but also serves as a meaningful indicator for future resource requirements. Of paramount concern is the optimal utilization of data center server capacity, i.e., the CPU, I/O, and memory. The compute capacity of modern servers can be further harnessed by optimal utilization of individual CPU cores. A precise CPU core-level usage monitoring and provisioning can lead to cumulative benefits of optimal CPU utilization, efficient VM placement, reduced VM migrations, and energy efficiency through lower power consumption. In this paper, we make a preliminary analysis of usage patterns of CPU cores in the case of CPU- and memory-intensive workloads on an experimental cloud setup in our laboratory. The aim is to make a comparative analysis of the utilization of individual CPU cores with that of aggregated CPU usage to explore the feasibility of incorporating a fine-grained usage detail for resource scheduling and VM provisioning. Initial experiments reveal observable differences between the utilization of individual CPU cores and that reported by aggregate CPU usage. Usage difference ranges from 1 to 29% below and between 4 and 20% above the aggregate. Incorporating such finer details can leverage the vast compute capacity of multicore servers and effective power usage.

Keywords

  • Workload analysis
  • Workload characterization
  • Capacity planning
  • Server capacity planning
  • Workload intensity
  • Job inter-arrival patterns
  • Multicore scheduling

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Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, MNNIT Allahabad, Allahabad, UP, 211004, India

    Eva Patel & Dharmender Singh Kushwaha

Authors
  1. Eva Patel
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  2. Dharmender Singh Kushwaha
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Corresponding author

Correspondence to Eva Patel .

Editor information

Editors and Affiliations

  1. University of Canberra, Adelaide, SA, Australia

    Prof. Dr. Lakhmi C. Jain

  2. Aurel Vlaicu University of Arad, Arad, Romania

    Prof. Valentina E. Balas

  3. School of Computing Science and Engineering, Galgotias University, Greater Noida, Uttar Pradesh, India

    Dr. Prashant Johri

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Patel, E., Kushwaha, D.S. (2019). Analysis of Workloads for Cloud Infrastructure Capacity Planning. In: Jain, L., E. Balas, V., Johri, P. (eds) Data and Communication Networks. Advances in Intelligent Systems and Computing, vol 847. Springer, Singapore. https://doi.org/10.1007/978-981-13-2254-9_4

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  • DOI: https://doi.org/10.1007/978-981-13-2254-9_4

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