Energy efficiency models and optimization algoruthm to enhance on-demand resource delivery in a cloud computing environment

Abstract

Online hosed services are what is referred to as Cloud Computing. Access to these services is via the internet. h shifts the traditional IT resource ownership model to renting. Thus, high cost of infrastructure cannot limit the less privileged from experiencing the benefits that this new paradigm brings. Therefore, c loud computing provides flexible services to cloud user in the form o f software, platform and infrastructure as services. The goal behind cloud computing is to provide computing resources on-demand to cloud users efficiently, through making data centers as friendly to the environment as possible, by reducing data center energy consumption and carbon emissions. With the massive growth of high performance computational services and applications, huge investment is required to build large scale data centers with thousands o f centers and computing model. Large scale data centers consume enormous amount s of electrical energy. The computational intensity involved in data center is likely to dramatically increase the difference between the amount of energy required for peak periods and of T-peak periods in a cloud computing data center. In addition to the overwhelming operational cost, the overheating caused by high power consumption will affect the reliability o f machines and hence reduce their lifetime. There fore, in order to make the best u e of precious electricity resources, it is important to know how much energy will be required under a certain circumstance in a data center. Consequently, this dissertation addresses the challenge by developing and energy-efficient model and a defragmentation algorithm. We further develop an efficient energy usage metric to calculate the power consumption along with a Load Balancing Virtual Machine Aware Model for improving delivery of no-demand resource in a cloud-computing environment. The load balancing model supports the reduction of energy consumption and helps to improve quality of service. An experimental design was carried out using cloud analyst as a simulation tool. The results obtained show that the LBVMA model and throttled load balancing algorithm consumed less energy. Also, the quality or service in terms of response time is much better for data centers that have more physical machines. but memory configurations at higher frequencies consume more energy. Additionally, while using the LBVMA model in conjunction with the throttled load balancing algorithm, less energy is consumed. meaning less carbon is produced by the data center.