Efficient Computation Offloading in Mobile Cloud Computing with Nature-Inspired Algorithms

The ubiquitous presence of smart phones and other hand-held computing devices has resulted in a growing feasibility to utilize them as computing resources. However, these mobile devices are constrained in battery and may not possess adequate capability for computationally intensive tasks. Cloud computing allows mobile devices to address their inherent challenges by making it possible to offload computation, completely or partially, to powerful cloud servers. This enables mobile devices to act as compute resources; though, it also results in cost of using cloud servers as well as communication cost involved in offloading. The paper models the computation offloading problem as an optimization problem and makes use of nature-inspired algorithms for deciding whether a task should be executed locally on a mobile device or offloaded to the cloud. The study was performed over four algorithms, namely Genetic Algorithm (GA), Differential Evolution (DE), Particle Swarm Optimization (PSO) and Shuffled Frog Leaping Algorithm (SFLA). Experimental analysis revealed that these algorithms outperform exhaustive search technique by providing a near optimal solution in a reasonable time even for large workflows. Results also establish that GA outperforms DE, PSO and SFLA by around 45%, 65% and 42%, respectively by reducing an application's overall execution cost.