Abstract
Cloud computing is an internet based computing technology that provide on demand computing for end users. Normally, data centers allocation for application on statically based. But today so many data centers have a problem how to reduce energy consumption? Due to increase use of cloud services and infrastructure by various cloud providers, uses of energy day by day increase that's why energy consumption increase lots. Large numbers of data centers that consume lots of energy which increase the level of co2. Hence there is need for green computing and for energy efficient management of cloud data center resources besides meeting the QoS constraints. Within a data center, maximum energy is consumed by cooling systems and ICT Infrastructure, particularly, the servers. So it is of utmost importance to optimize energy utilization in datac enter servers. In order to achieve this, we can leverage the power of virtualization which is intrinsic in Cloud Computing. Virtualization opens the doors for VM consolidation by allowing dynamic migration of virtual machines across physical machines. In this study, the problem of VM consolidation has been formulated as a mathematical optimization problem.
Highlights
Cloud technology aims to provide pay per use service, reliable, Specified resources and Quality of Service (QoS) services for the consumers and end users
Using several fundamental models cloud providers offers various services for the users according to their requirements:
In this algorithm each Virtual Machines (VMs) is allocated to a host for which the increase in power consumption caused by the allocation is the smallest
Summary
Cloud technology aims to provide pay per use service, reliable, Specified resources and QoS services for the consumers and end users. A number of challenges are being faced with respect to implementing energy efficient cloud computing These are (Mell and Grance, 2014): Energy aware dynamic resource allocation, QoS- based Resource Selection and Provisioning, Optimization of Virtual Network Topologies, Autonomic Optimization of Thermal states and Cooling System Operation, Efficient Consolidation of VMs for Managing Heterogeneous Work- loads. The problem of energy-efficient server utilization has been formulated as a mathematical optimization problem
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