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Application partitioning that splits the executions into local and remote parts, plays a critical role in high-performance mobile offloading systems. Optimal partitioning will allow mobile devices to obtain the highest benefit from Mobile Cloud Computing (MCC) or Mobile Edge Computing (MEC). Due to unstable resources in the wireless network (network disconnection, bandwidth fluctuation, network latency, etc.) and at the service nodes (different speeds of mobile devices and cloud/edge servers, memory, etc.), static partitioning solutions with fixed bandwidth and speed assumptions are unsuitable for offloading systems. In this paper, we study how to dynamically partition a given application effectively into local and remote parts while reducing the total cost to the degree possible. For general tasks (represented in arbitrary topological consumption graphs), we propose a Min-Cost Offloading Partitioning (MCOP) algorithm that aims at finding the optimal partitioning plan (i.e., to determine which portions of the application must run on the mobile device and which portions on cloud/edge servers) under different cost models and mobile environments. Simulation results show that the MCOP algorithm provides a stable method with low time complexity which significantly reduces execution time and energy consumption by optimally distributing tasks between mobile devices and servers, besides it adapts well to mobile environmental changes.
Wu et al. (Wed,) studied this question.