Task Offloading and Resource Allocation for Container-enabled Mobile Edge Computing

A growing number of mobile services demand intensive computation resources and high energy consumption. Traditional cloud-based task offloading incurs excessive transmission delay. With mobile edge computing, we can offload tasks to the nearby edge servers, thus mitigating long data transmission latency. Current task offloading and resource allocation approaches ignore the extra overhead brought by container instance startup. This naturally leads to a long overall latency and high energy consumption. Motivated by the limitations of existing literature, we address task offloading and resource allocation problem considering container instance startup time. We first examine the startup latency of containers started from different types of images, and present its impact on overall performance. Then, we formulate the problem as optimization problem. The problem is hard to address because mutual effects of offloading decision, resource allocation decision and container instance startup contribute to the overall performance. We resort to Gibbs sampling to decouple offloading decision and resource allocation. Finally, taking into account all potential conditions brought up by container instance startup, an optimal computation resource allocation and uplink power assignment scheme are derived through bisection, and Karush-Kuhn-Tucher (KKT) conditions. Comprehensive evaluations are conducted to verify the performance of the proposed approach, and demonstrate its superiority over current notable solutions.

Automated summary

The growing demand for mobile services requires efficient task offloading and resource allocation strategies taking into account the impact of container instance startup time. This study proposes a novel approach that addresses these challenges and improves overall performance significantly over existing solutions.