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.
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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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