• Shivani Kumari
• Anita Ganpati

Cloud computing is on-demand availability of computing resources, users can use these resources anywhere, anytime with the help of internet connectivity on pay per usage basis. Users request for the services on cloud is increasing day by day so, for better performance need to balance all the request and answered those request in minimum time. For that, load balancing is required in cloud computing. Load balancing is the method of assigning an equal amount of work among nodes in cloud to improve both resource utilization and job response time, also avoid a condition where some of the nodes are overloaded while others are doing small amount of work. It also ensures that every node in the distributed network does almost equal amount of work at particular amount of time. Main objective of the study is to analyze the performance of different dynamic load balancing algorithms i.e. Ant colony optimization, honey bee, particle swarm optimization and throttled with respect to different service broker policies. The performance metrics are overall data processing time, response time and total cost. For this, Cloud Analyst tool is used. From the findings of the study, it may be concluded that Ant colony optimization performs better with optimize response time service broker policy.

Cloud Computing, Load Balancing, Load Balancing Algorithms, Ant colony optimisation, Honey Bee, Throttled, and Particle Swarm Optimisation, Cloud Analyst.

"Performance Analysis of Dynamic Load Balancing Algorithms with different Service Broker Policies using Cloud Analyst ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 6, page no.758-762, June-2019, Available :http://www.jetir.org/papers/JETIR1906E66.pdf

"Performance Analysis of Dynamic Load Balancing Algorithms with different Service Broker Policies using Cloud Analyst ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 6, page no. pp758-762, June-2019, Available at : http://www.jetir.org/papers/JETIR1906E66.pdf