An Adaptive Firefly Algorithm for Load Balancing in Cloud Computing

Over the past few years, cloud computing has become a popular paradigm that provides computing over the internet. There are umpteen factors that a cloud ecosystem need such as reliability, flexibility, dynamic load balancing etc. With the internet facility, resources are provided dynamically to the end users in an on-demand fashion. Users could be billions in number accessing the cloud. Their need for services have been increasing at an alarming rate. To enhance the performance of the system, resources should be used efficiently. Cloud computing needs to identify different issues and challenges. One of the main issues in cloud computing is Load balancing, in which workload is distributed dynamically to all the nodes. Load balancing not only optimize the resource use, maximize throughput, minimize processing time of datacenters and response time of user base, but also helps in evading the overloading of any single resource. This paper proposes an Adaptive firefly algorithm (ADF) for solving the load balancing problem in cloud computing by performing virtual machine scheduling over datacenters. The results have been compared with Ant Colony Optimization (ACO) algorithm used for load balancing.

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