Implementation of Template based Datacenter Broker Policy for Cloud to Improve the QoS
Seema Chowhan1, Ajay Kumar2, Shailiaja Shirwaikar3
1Seema Chowhan *, Department of Computer Science, Baburaoji Gholap College Sangvi, Pune, India.
2Ajay Kumar, Department of Computer Application, Jayawant Institute, Pune, India.
3Shailiaja Shirwaikar, Department of Computer Science, Savitribai Phule Pune University, Pune, India.
Manuscript received on January 02, 2020. | Revised Manuscript received on January 15, 2020. | Manuscript published on January 30, 2020. | PP: 2590-2595 | Volume-8 Issue-5, January 2020. | Retrieval Number: E6295018520/2020©BEIESP | DOI: 10.35940/ijrte.E6295.018520
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Cloud computing is abstraction of web based services for businesses having dynamic requirements of resources. The cloud offers noteworthy benefits to business world by providing hardware set up, software and management of the system software. It puts in together elastic resources including hardware, software in virtual platform dynamically to meet computing need. The Quality of service parameters (QoS) comprises different parameters like, performance, availability and reliability, response time, throughput and bandwidth etc. There is need for proper monitoring, management of these parameters for effective cloud services and to maintain customer base. An efficient DCB policy reduce the overall execution time of the requested Cloudlets (Jobs/Tasks). An important policy of the Datacenter Broker (DCB) is binding of Cloudlets with an available VMs. For efficient load balancing there is need of proper allocation of cloudlets to the appropriate available VMs as per application requirement. In present study, we proposed a Template Based Resource Provisioning algorithm for effective resource utilization and allocation of Cloudlets to the available VMs in a Datacenter. The proposed method takes into consideration application requirement with its size (Cloudlet length) along with power and capacity of VMs. Experimental are performed using CloudSim under different workload scenario and results are obtained for comparison. The proposed algorithm creates performance supremacy over existing DCB algorithm for high workload.
Keywords: Quality of Service, Virtual Machines, Datacenter, Datacenter Broker.
Scope of the Article: Natural Language Processing and Machine Translation.