Implementation of dynamic resource allocation using Adaptive Fuzzy Multi-Objective Genetic Algorithm for IoT based cloud computing

Abstract

IoT based cloud computing system faces new challenges every day, due to the complex structure of system clusters and high volume of data processed by the systems. The ability of acquiring resources in an elastic manner is considered as the primary rationale for adopting IoT based cloud computing system. Elasticity mainly supports the facility to grow and shrink the virtual resources dynamically according to the requirement of IoT based cloud users. The Resource Allocation proposes Enhanced Genetic Algorithm is proposed to solve these problems, which is used to accomplish better virtual machine allocation across IoT based cloud servers for maintaining vertical elasticity. The resource under utilization problem and high operational cost of IoT based cloud system noticed in the dynamic resource allocation (DRA) technique motivated to propose Resource Allocation Technique. DRA employs the Adaptive Fuzzy Multi-Objective Genetic Algorithm (AFMOGA) for efficient resource allocation using Horizontal Elasticity approach in second contribution. The objectives of the problem are to maximize the Load Distribution and to improve the Resource Utilization, thereby tried to provide the horizontal elasticity for resource allocation in an efficient manner. Further, the proposed technique also reduces the virtual machine migration count. Moreover, the proposed algorithm provides perfect balance between the exploration and exploitation processes by efficiently making use of adaptive genetic operators integrated with the Fuzzy Inference System (FIS). The simulation results shows that the proposed method outperforms compared to the state of art approaches.