CPU Performance Coefficient (CPU-PC): A Novel Performance Metric Based on Real-time CPU Resource Provisioning in Time-shared Cloud Environments

T. Mastelic, J. Jasarevic, I. Brandic:
"CPU Performance Coefficient (CPU-PC): A Novel Performance Metric Based on Real-time CPU Resource Provisioning in Time-shared Cloud Environments";
Vortrag: 2014 IEEE 6th International Conference on Cloud Computing Technology and Science (CLOUDCOM 2014), Singapore, Singapore; 15.12.2014 - 18.12.2014; in:"Proceedings of the 2014 IEEE 6th International Conference on Cloud Computing Technology and Science, CloudCom 2014", IEEE Computer Society, (2014), S. ##.

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Abstract:


The Cloud represents an emerging paradigm that provides on-demand computing resources, such as CPU. The resources are customized in quantity through various virtual machine (VM) flavours, which are deployed on top of timeshared infrastructure, where a single server can host several VMs. However, their Quality of Service (QoS) is limited and boils down to the VM availability, which does not provide any performance guarantees for the shared underlying resources. Consequently, the providers usually over-provision their resources trying to increase utilization, while the customers can suffer from poor performance due to increased concurrency.
In this paper, we introduce CPU Performance Coefficient (CPU-PC), a novel performance metric used for measuring the real-time quality of CPU provisioning in virtualized environments. The metric isolates an impact of the provisioned CPU on the performance of the customerĀ“s application, hence allowing the provider to measure the quality of provisioned resources and manage them accordingly. Additionally, we provide a measurement of the proposed metric for the customer as well, thus enabling the latter to monitor the quality of rented resources. As evaluation, we utilize three real world applications used in existing Cloud services, and correlate the CPU-PC metric with the response time of the applications. An R-squared correlation of over 0.9557 indicates the applicability of our approach in the real world.