Toward an Optimal Latency-Energy Dynamic Offloading Scheme for Collaborative Cloud Networks

Abstract

Growing technologies like virtualization and artificial intelligence have become more popular nowadays because they are more handy and accessible on mobile devices. But lack of resources for processing these applications at the user end and the limited energy of mobile devices are still significant hurdles. Collaborative edge and cloud computing are one of the solutions to this problem. An optimal offloading strategy is required to balance transmission latency for the cloud and limited resources at edge servers. We have proposed a multi-period deep deterministic policy gradient (MP-DDPG) algorithm to find an optimal offloading policy to the collaborative cloud network including the central cloud server, edge cloud servers, and mobile devices constrained by minimization of computation, transmission delay, and energy consumption. The novelty of this algorithm lies in partitioning the task to offload in multiple time slots and reusing cloud and edge resources in every slot, rather than taking a single offloading decision and running out of remote resources by offloading a single large task. Our results show that MP-DDPG achieves the minimum latency and energy consumption in the collaborative cloud network.

Keywords

• Collaborative cloud computing
• computation offloading
• latency
• energy efficiency
• deep reinforcement learning
• multi-period deep deterministic policy gradient