Open AccessAnalysis of Heterogeneous Device Characteristics in Round Robin Based Load Balancing Algorithm with Closest Data Center as Service Broker Policy in Cloud
Author(s) -
Dr Kumar PJ,
Dr N. Sivakumar,
Dr J. Prabhu,
Prof. P.S. Ramesh,
Prof. Suganya P
Publication year - 2019
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.i8273.078919
Subject(s) - load balancing (electrical power) , computer science , cloud computing , data center , weighted round robin , virtual machine , response time , distributed computing , scheduling (production processes) , algorithm , round robin scheduling , quality of service , dynamic priority scheduling , computer network , operating system , engineering , operations management , geometry , mathematics , grid
Load balancing is an important aspect in cloud to share load among different virtual machines running on various physical nodes. The user response time which is an important performance metric is being highly influenced by the efficient load balancing algorithm for cloud data centers. Virtual machines which are part of the cloud data centers consist of various types of physical devices. The user response time is affected significantly by the capacity of physical devices that exist as part of the data centers. Several load balancing algorithms exist in the literature to allocate task effectively on various virtual machines running in data centers. We investigate the performance of round robin based load balancing algorithm with closest data center as service broker policy in cloud data centers. We have performed a simulation with data centers that consist of devices with different physical characteristics such as memory, storage, bandwidth, processor speed and scheduling policy using Round Robin load balancing algorithm with closest data centers as service broker policy. We present the merits of heterogeneous device characteristics in reducing the user response time and the data center request service time. We used Cloud Analyst, an open source simulation tool for cloud computing environment