Task Offloading and Resource Allocation Strategies Among Multiple Edge Servers

Mobile-edge computing has been widely used in the Internet of Things (IoT) field. In mobile-edge computing, users' tasks can be executed on local devices or offloaded to edge servers for processing. However, in the multiedge server collaboration scenario, service coverage of multiple edge servers may overlap with each other. Therefore, users in the overlapped service coverage areas need to determine which server to be offloaded. Inefficient task offloading strategies may result in unbalanced workloads of the edge servers, which causes negative impacts on task completion latency and the total number of served users. To address this problem, we propose a task offloading and resource allocation strategy in a multiedge server collaboration scenario in this article. In more detail, we consider that task demands arrive dynamically and tasks can be processed locally or offloaded to edge servers. We then model the task offloading and resource allocation problem as a partially observable Markov game (POMG) and propose a task offloading and resource allocation strategy based on a reinforcement learning algorithm I-PDQN to ensure that the task latency requirements are met while maximizing the number of served users and minimizing the average task energy consumption. Finally, we evaluate the performance of the strategy proposed in this article against some typical benchmark strategies under different system parameters through experiments. The experimental results show that the I-PDQN-based task offloading and resource allocation strategy can outperform benchmark approaches.