Exploiting Computation Replication in Multi-User Multi-Server Mobile Edge Computing Networks.

In mobile edge computing (MEC) systems, mobile devices can offload their computation-intensive tasks to edge servers to save energy and shorten latency. However, the extra latency for downloading the computed results back may degrade the benefits of computation offloading if the downlink channel suffers severe fading and interference. In this work, we exploit computation replication in task offloading to reduce the download latency in multi-user multi-server MEC networks. The main idea is to partition the task generated by each user into multiple subtasks, and offload each subtask to a set of MEC servers via the uplink channel for repeated execution. The duplication of computation results on multiple servers thus enables data-sharing based transmission cooperation to send the results back to users. Next, we adopt an asymptotic total latency that accounts the uploading, computing and results downloading phases as the performance metric to capture the tradeoff between the increased computation load and the reduced communication time. We formulate a linear programming problem to optimize the task partition ratios for minimizing the total latency. We show that there exists an optimal degrees of replication (the number of MEC servers to compute the same subtask) and an associated task partition strategy for optimal latency performance. Our finding reveals great advantage of computation replication for latency reduction in multi-server MEC networks where the output data size of each computation task is non-negligible.