Energy Constrained Resource Scheduling for Cloud Environment

  R.Selvi,  S.Russia,  V.K.Anitha

Cloud Computing is used to access computing resources owned and operated by a third-party provider. It is internet-based computing to share resources, software and information. The rapid growth in demand for computational power has led to a shift to the cloud computing model established by large-scale virtualized data centers. Such data centers consume enormous amounts of electrical energy. However, to support green computing, cloud providers also need to minimize the cloud infrastructure energy consumption while conducting the service delivery. In existing system, dynamic server consolidation through live migration is an efficient way towards energy conservation in cloud data centers. It keeps the number of power-on systems as low as possible which is achieved by implementing Power Aware Best fit Decreasing (PABFD) algorithm with the help of Minimum Migration Time(MMT). The main drawback is that when the systems are in power off state will also increases power consumption when starts immediately.In proposed system, Power management strategies have been proposed for enterprise servers based on Dynamic Voltage and Frequency Scaling (DVFS). DVFS allows the server to transition the processor from high-power states to low-power states. The processors are assigned to deep sleep to reduce energy consumption. In deep sleep the server can be configured to use Direct Memory Access (DMA) to place incoming packets into memory buffers for processing in the active state. Request grouping will group received requests into batches and put the processor into sleep between the batches. The Virtual grouping scheme is enhanced to manage resources with load balancing mechanism. The system is improved with optimization mechanism to manage relative response time. Resource levels and application requirements are integrated in the allocation process. The system is adopted to support Dynamic Random Access Memory (DRAM) and Dual in-line Memory Module (DIMM) components.In proposed system, Power management strategies have been proposed for enterprise servers based on Dynamic Voltage and Frequency Scaling (DVFS). DVFS allows the server to transition the processor from high-power states to low-power states. The processors are assigned to deep sleep to reduce energy consumption. In deep sleep the server can be configured to use Direct Memory Access (DMA) to place incoming packets into memory buffers for processing in the active state. Request grouping will group received requests into batches and put the processor into sleep between the batches. The Virtual grouping scheme is enhanced to manage resources with load balancing mechanism. The system is improved with optimization mechanism to manage relative response time. Resource levels and application requirements are integrated in the allocation process. The system is adopted to support Dynamic Random Access Memory (DRAM) components.

Keywords- Energy Management, Virtual machines,overhead, request grouping, servers, data center,server consolidation, virtual grouping

Unique Identification Number - IJEDR1502078
Page Number(s) - 417-421
Pubished in - Volume 3 | Issue 2 | May 2015
DOI (Digital Object Identifier) -   
Publisher - IJEDR (ISSN - 2321-9939)

  R.Selvi,  S.Russia,  V.K.Anitha,   "Energy Constrained Resource Scheduling for Cloud Environment", International Journal of Engineering Development and Research (IJEDR), ISSN:2321-9939, Volume.3, Issue 2, pp.417-421, May 2015, Available at :http://www.ijedr.org/papers/IJEDR1502078.pdf