Efficient Task Allocation Using Intelligent Bacterial Foraging Optimization (IBFO) Algorithm in Cloud

Abstract

Cloud usage increases with increase in computational demands. The number of tasks for execution in cloud increases which ends up in complexity of scheduling tasks to resources in an energy efficient manner and with reduction of computation time. To resolve this issue Bacterial Foraging Optimization (BFO) algorithm proves to handle energy and time consumption efficiently. Though Bacterial Foraging Optimization (BFO) is one of the widely known and robust algorithm for handling multi-objective optimization problems, the algorithm is basically static and is run for a fixed number of iterations. Due to the inflexibility of the algorithm, there exists a need for the improvement of the existing Bacterial Foraging Optimization (BFO) algorithm. This paper rolls out an improved version of Bacterial Foraging Optimization (BFO) called Intelligent Bacterial Foraging Optimization (IBFO) algorithm that is dynamic based on the problem. Intelligent Bacterial Foraging Optimization (IBFO) algorithm is found to be more efficient than the existing Bacterial Foraging Optimization (BFO) algorithm in task scheduling in cloud environment.