Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/241740
Title: Energy Efficient Task Scheduling Algorithms for Cloud Computing Data Center
Researcher: Umesh A. S.
Guide(s): Kumar Parveen
Keywords: Engineering and Technology,Computer Science,Computer Science Information Systems, Cloud computing, Server consolidation, Task scheduling, Consumption
University: Nims University Rajasthan
Completed Date: 2019
Abstract: Cloud computing is a technology that provides a platform for the sharing of newlineresources such as software, infrastructure, application and other information. It newlinebrings a revolution in Information Technology industry by offering on-demand of newlineresources. Clouds are basically virtualized datacenters and applications offered as newlineservices. Data center (Server infrastructure) hosts hundreds or thousands of servers newlinewhich comprised of software and hardware to respond the client request. A large newlineamount of energy requires performing the operation. A data center with 500*100 newlineservers consumes around 9Megawatt to perform operation. Energy consumption is a newlinekey concern in data center. Energy consumption by Google is 2,675,898 MWh in newline2011. Cloud Computing is facing lot of challenges like Security of Data, newlineConsumption of energy, Server Consolidation, etc. The research work focuses on the newlinestudy of task scheduling management in a cloud environment. newlineThe main goal is to improve the performance (resource utilization and redeem the newlineconsumption of energy) in data centers. Energy-efficient scheduling of workloads newlinehelps to redeem the consumption of energy in data centers, thus helps in better usage newlineof resource. This is further reducing operational costs and provides benefits to the newlineclients and also to cloud service provider. In this abstract of thesis, the task newlinescheduling in data centers have been compared. Cloudsim a toolkit for modeling and newlinesimulation of cloud computing environment has been used to implement and newlinedemonstrate the experimental results. The results aimed at analyzing the energy newlineconsumed in data centers and shows that by having reduced the consumption of newlineenergy the cloud productivity can be improved. newlineKeywords: Cloud computing, Server consolidation, Task scheduling, Consumption newline
Pagination: 1-10, 1-81
URI: http://hdl.handle.net/10603/241740
Appears in Departments:Department of Computer Science and Engineering



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