A Load Balancing Analysis of Cloud Base Application with different Service Broker Policies

provisioning and resource optimization are the key issues in cloud computing. To balance the load in across virtual machine load balancing algorithms are classified into two categories i.e. static, dynamic. For homogeneous and stable environment we prefer static load balancing algorithms. For heterogeneous, dynamic environment we prefer dynamic load balancing algorithms. Load balancing may take place in the public, private or hybrid cloud. In this paper, we focus on a load balancing policy i.e. Closest data Center with different no of virtual machines. The evaluation metrics is the response time and data center processing time. Cloud Environment is simulated for the scenario of "Internet banking" of an international bank in simulation toolkit CloudAnalyst. Using these two evaluation metrics we identify that for real deployment of such customers application what should be a threshold value of key parameters which are supported by the Cluster of users across the Globe. KeywordsCloudlet, Clouds, DVFS, VM, CPU;

[1]  Kousik Dasgupta,et al.  Load Balancing in Cloud Computing using Stochastic Hill Climbing-A Soft Computing Approach , 2012 .

[2]  Shantenu Jha,et al.  Efficient Runtime Environment for Coupled Multi-physics Simulations: Dynamic Resource Allocation and Load-Balancing , 2010, 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing.

[3]  Randy H. Katz,et al.  Above the Clouds: A Berkeley View of Cloud Computing , 2009 .

[4]  Marios D. Dikaiakos,et al.  Cloud Computing: Distributed Internet Computing for IT and Scientific Research , 2009, IEEE Internet Computing.

[5]  Rajkumar Buyya,et al.  CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms , 2011, Softw. Pract. Exp..

[6]  Mladen A. Vouk,et al.  Cloud computing — Issues, research and implementations , 2008, ITI 2008 - 30th International Conference on Information Technology Interfaces.

[7]  Rajkumar Buyya,et al.  Cloud Computing Principles and Paradigms , 2011 .

[8]  Mladen A. Vouk,et al.  Cloud Computing – Issues, Research and Implementations , 2008, CIT 2008.

[9]  Ratan Mishra,et al.  Ant colony Optimization: A Solution of Load balancing in Cloud , 2012 .

[10]  Saroj Hiranwal,et al.  Adaptive Round Robin Scheduling using Shortest Burst Approach Based on Smart Time Slice , 2012 .

[11]  Dan Wang,et al.  Cloud Task Scheduling Based on Load Balancing Ant Colony Optimization , 2011, 2011 Sixth Annual Chinagrid Conference.

[12]  Kousik Dasgupta,et al.  A Genetic Algorithm (GA) based Load Balancing Strategy for Cloud Computing , 2013 .

[13]  Lei Wu,et al.  An Intelligent Load Balancing Algorithm Towards Efficient Cloud Computing , 2011, AI for Data Center Management and Cloud Computing.

[14]  Rajkumar Buyya,et al.  CloudAnalyst: A CloudSim-Based Visual Modeller for Analysing Cloud Computing Environments and Applications , 2010, 2010 24th IEEE International Conference on Advanced Information Networking and Applications.