Intuitionistic fuzzy clustering and natural exponential inertia weight with Artificial Bee Colony based scheduling approach for mobile grid

Research in mobile grid computing is inspired by the broad benefits of intensive scientific and enterprise applications by the scientific community in grid and mobile computing. The primary aim of grid computing is to combine the power of wide-ranging resources that are very diverse in nature and provide users with essential services to users in the future. Resources are complex and varying in a mobile grid system and the availability is limited for a period of time. With resource mobility in mind, classification is the most challenging and primitive challenge in the mobile grid environment. The previous paradigm outlined the Quantum based approach on Gravitational search Algorithm (QBGSA) for scheduling the task (job) on a mobile computing grid. In any case, it has problem with computational difficulty and execution time. To solve this problem, the proposed system designed an Intuitionistic Fuzzy C-Means (IFCM) clustering and Natural Exponential Inertia Weight with Artificial Bee Colony (NEIWABC) based scheduling approaches. In this proposed research work, grid server has all the information about mobile grid networks. Once it receiving the task request from user, it verifies the available resource providers. The nodes are clustered by using Intuitionistic Fuzzy C-Means (IFCM) clustering algorithm and Cluster Head (CH) selection is done using mobile capacity such as storage capacity, computational power. Then the task request is promoting to the selected CH in the cluster. Then the CH schedules the jobs to the appropriate mobiles to respond user. The task scheduling is performed by using Natural Exponential Inertia Weight with Artificial Bee Colony (NEIWABC) algorithm. The results of this experiment depicts that the proposed system attains good efficiency on comparison with previous system.