Energy Efficient Resource Allocation in Green Clouds

Abstract

Cloud Computing is a business model that is being widely-adopted by enterprises and organizations. It has become the preferred computing platform for deploying applications and services owing to its inherent characteristics, including scalability, pay-per-use, rapid elasticity, cost saving, self-service, and broad network access. These characteristics of Cloud computing have played a major role in its widespread adoption. To accommodate the growing demand for computational resources, Cloud market players Amazon, Microsoft, Google, GoGrid, Flexiant, etc. have set up large sized Data Centers (DCs). These large scale DCs consume huge amount of energy. Further, with the proliferation of Cloud computing, more and more Cloud based applications with varying resource demands and diverse Quality of Service (QoS) requirements are coming up, which require dynamic allocation, reconfiguration, and reallocation of resources. All these requirements necessitate the development of energy-efficient and QoS aware resource allocation techniques that not only reduce energy consumption but also satisfy QoS requirements of the end users. Reducing energy consumption while providing QoS is the biggest challenge that the Cloud service providers are confronting with. Therefore, to achieve the set objectives of energy efficient resource allocation, an extensive literature review on resource allocation in Cloud computing has been done. The state of the art techniques in the area of power management and resource allocation in Clouds have been explored. The comprehensive study of energy-efficient resource allocation techniques in Clouds has been carried out to identify their inherent limitations. From the literature survey, it is apparent that the biggest challenge confronting Cloud service providers is related to energy consumption and QoS aware resource allocation. To address the energy and QoS related challenges of Cloud, a green Cloud framework, named ``SERVmegh', has been proposed for efficient and robust management of resources.