Energy Efficient Dynamic Slot Allocation of Map Reduce Tasks for Big Data Applications

Abstract

Big data applications are very large in size in terms of map and reduce tasks and it takes large time to execute those map reduce based big data applications. Map Reduce is a familiar bulk data processing concept for extensive data computing in cloud environment. The existing Energy Conscious Arrangement is implemented with static map and reduce slot allocation, where there is no room for effective resource utilization. In case of static slot allocation, the pre-determination of map and reduce slots does not employ efficient run time performance and the slots can be strictly under-utilized. To deal with it, the proposed work discovers and enhances the resource utilization and execution time. In the proposed work, dynamic slot allocation technique is accomplished with the existing Energy Conscious Arrangement concept. The technique called Energy Efficient Dynamic Slot Allocation (EEDSA) is accomplished by using dynamic slot allocation technique, which ensures efficient resource utilization. It provides way for slots to be reassigned to map tasks or reduce tasks relying on the requirements. Since slots of the node in the cluster do not deploy any specific functionality, there is no constraint on using map slots in place of reduce slots and vice versa. The proposed technique EEDSA is able to show 10% reduction in execution time for various workloads in comparison with existing Energy Conscious Arrangement technique.