Energy-Minimized Partial Computation Offloading for Delay-Sensitive Applications in Heterogeneous Edge Networks

Mobile Devices (MDs) support various delay-sensitive and computation-intensive applications. Yet they only have limited battery energy and computing resources, thereby failing to totally run all applications. A mobile edge computing (MEC) paradigm has been proposed to provide additional computation, storage, and networking resources for MDs. Servers in MEC are often deployed in both macro base stations (MBSs) and small base stations (SBSs). Thus, it is highly challenging to associate resource-limited MDs to them with high performance, and realize partial computation offloading among them for minimizing total energy consumption of an MEC system. To tackle these challenges, this work proposes a novel computation offloading approach for delay-sensitive applications with multiple separable tasks in hybrid networks including MDs, SBSs, and an MBS. To achieve it, this work formulates total energy consumption minimization as a constrained mixed integer non-linear program. To solve it, this work designs an improved meta-heuristic optimization algorithm called Particle swarm optimization based on Genetic Learning (PGL), which integrates strong local search capacity of a particle swarm optimizer, and genetic operations of a genetic algorithm. PGL jointly optimizes task offloading among MDs, SBSs, and MBS, users' connection to SBSs, MDs' CPU speeds and transmission power, SBSs and MBS, and bandwidth allocation of available channels. Simulations with real-world data collected from Google cluster trace demonstrate that PGL significantly outperforms other existing methods in total energy consumption of an entire system.