Task scheduling is a crucial challenge in cloud computing paradigm as variety of tasks with different runtime processing capacities generated from various heterogeneous devices are coming up to cloud application console which effects system performance in terms of makespan, resource utilization, resource cost. Therefore, traditional scheduling algorithms may not adapt to this paradigm efficiently. Many existing authors developed various task schedulers by using metaheuristic approaches to solve Task scheduling problem(TSP) to get near optimal solutions but still TSP is a highly dynamic challenging scenario as it is a NP hard problem. To tackle this challenge, this paper introduces a multi objective prioritized task scheduler using improved asynchronous advantage actor critic(a3c) algorithm which uses priorities of tasks based on length of tasks, runtime processing capacities and priorities of VMs based on electricity unit cost using multi cloud environment. Scheduling process carried out in two stages. In the first stage, all incoming tasks, VM priorities are calculated at the task manager level and in the second stage, Priorities are fed to (MOPTSA3C) scheduler to generate scheduling decisions to map tasks effectively onto VMs by considering priorities and schedule tasks based on cost, resource utilization, makespan in the available multi cloud environment. Extensive simulations are conducted on Cloudsim toolkit by giving input trace different fabricated data distributions and real time worklogs of HPC2N, NASA datasets to the scheduler. For evaluating the efficacy of proposed MOPTSA3C, it compared against existing techniques i.e. DQN, A2C, MOABCQ. From the results, it is evident that proposed MOPTSA3C outperforms existing algorithms for makespan, resource utilization, resource cost, reliability.
