EANAE: a Cloud Job Migration Based Fault Tolerance Services Using Energy-Aware Nash Auction Equilibrium Model

Abstract

Cloud computing providers provision their resource into various kinds of Virtual Machine (VM) instances that are further allocated to the users for a particular time period. In current days, the major challenging task for cloud service providers is to build an efficient VM provisioning and allocation mechanisms. Several existing cloud provisioning mechanisms are based on the traditional cloud pricing policies and cost reservation techniques. These policies lacks the dynamic VM provisioning and cost reservation approaches. Many existing techniques also have the issues of job failure occurrence due to idle energy, complex computation of node prediction, and the network without consideration of success rate. To overwhelm the above existing limitations, the proposed strategy enhances the resource provisioning through the Energy Aware Nash Auction Equilibrium (EANAE) model. Subsequently, the optimal bidding mechanism is offered by the consideration of workload selection in terms of jobs deadline, average CPU time consumption, job submission, and job remaining time. Furthermore, the workflow optimization logics are presented by applying the migrations of jobs across multiple cloud VMs to optimize the cost. A cloud job migration based fault tolerant services have an effective scheduling mechanism, which in term maximize the revenue. The jobs are reallocated for available VM with the optimal cost and time. The experimental result exhibit better response time, migration frequency, execution time, memory utilization, and energy utilization than the existing method.