Constrained Multi-objective Optimization for IoT-enabled Computation Offloading in Collaborative Edge and Cloud Computing

Internet of things (IoT) applications are becoming more resource-hungry and latency-sensitive, which are severely constrained by limited resources of current mobile hardware. Mobile cloud computing (MCC) can provide abundant computation resources, while mobile edge computing (MEC) aims to reduce the transmission latency by offloading complex tasks from IoT devices to nearby edge servers. It is still challenging to satisfy the quality of service (QoS) with different constraints of IoT devices in a collaborative MCC and MEC environment. In this paper, we propose three constrained multi-objective evolutionary algorithms (CMOEAs) for solving IoT-enabled computation offloading problems in collaborative edge and cloud computing networks. First of all, a constrained multi-objective computation offloading model considering time and energy consumption is established in the mobile environment. Inspired by the push and pull search (PPS) framework, three constrained multi-objective evolutionary algorithms are developed by combing the advantages of population-based search algorithms with flexible constraint handling mechanisms. On one hand, three popular and challenging constrained benchmark suites are selected to test the performance of the proposed algorithms by comparing them to the other seven state-of-the-art CMOEAs. On the other hand, a multi-server multi-user multi-task computation offloading experimental scenario with a different number of IoT devices is used to evaluate the performance of three proposed algorithms and other compared algorithms as well as representative offloading schemes. The experimental results of the benchmark suites and computation offloading problems demonstrate the effectiveness and superiority of the proposed algorithms.