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Performance and Energy-based Cost Prediction Modelling of Virtual Machines in Cloud Computing Environments

Research Area:  Cloud Computing


   Cloud Computing has transformed the way in which enterprises and individuals are utilizing the Information Technology (IT) by offering on-demand services such as applications, platforms and infrastructures for their customers with reasonable prices based on their usage (e.g., pay-as-you-go model). However, the wide adoption of Cloud Computing and the growing number of Cloud customers have increased the overall operational costs for Cloud providers, especially with the increasing cost of energy consumed to operate Cloud services. Consequently, Cloud providers consider energy consumption as one of the most important cost factors to be maintained within their infrastructures.
   This thesis introduces a Cloud system architecture along with a novel Cost Modeller component that aims to enable the awareness of energy consumption, performance variation and cost in a Cloud environment. To fulfill this aim, an energy-based cost model is firstly developed to attribute the PM’s energy consumption to VMs and measures the actual resource usage, power consumption and the total cost for each VM. An energy-based cost prediction framework is then introduced to predict workload, power consumption and estimate the total cost of the VMs during service operation based on historical workload data. Finally, a performance and energy-based cost prediction framework is introduced to combine VMs consolidation and resource provisioning in order to design cost-effective strategies while taking into consideration the trade-off among cost, energy efficiency and performance variation of Cloud services. The evaluation of the proposed research on a Cloud testbed shows that the proposed energy-based cost model is capable of fairly attributing the PMs energy consumption to heterogeneous VMs, thus enabling cost and energy awareness at the VM level. Compared with actual results obtained in the Cloud testbed, the predicted results show that the proposed energy-based cost prediction framework is capable of predicting workload, power consumption and estimating the total cost for heterogeneous VMs based on historical workload patterns.
   The application of the proposed research provides the awareness of energy consumption, performance variation and cost at the virtual level in Cloud environments, which contributes to overcoming the challenge of identifying the most cost-effective strategies for Cloud services. The outcomes of this research can be used and incorporated in reactive and proactive management mechanisms to make enhanced cost decisions supported by performance and energy awareness in order to efficiently manage Cloud resources. This has the potential to contribute to a reduction in energy consumption, and therefore lowering the total cost for Cloud providers while maintaining the service performance.

Name of the Researcher:  Mohammad Aldossary

Name of the Supervisor(s):  

Year of Completion:  2019

University:  University of Leeds

Thesis Link:   Home Page Url