Reliability-Aware Offloading and Allocation in Multilevel Edge Computing System

Mobile edge computing system provides cloud computing capabilities at the edge of wireless mobile networks, ensuring low latency, highly efficient computing, and improved user experience. At the same time, computationally intensive components are offloaded from mobile devices to edge servers and distributed among the servers. Due to the special constraints (mobile devices' battery capacities, limited computing resources of one single edge server, inevitable edge server failure, etc.), there emerges a following problem. 1) How to guarantee the reliability of the offloaded computing? This problem brings in the following two other problems. 2) How to find the appropriate offloading point in the mobile program such that the computing tasks offloaded to cloud can be maximized, while the transmission energy consumption is minimized? 3) What is the achievable minimum latency tasks allocation strategy among multiple users' mobile devices and multiple edge servers? In this paper, we try to address the aforementioned problems. First, for the appropriate offloading point problem, we consider the offloading valuable basic constraint and propose a task merging strategy based on mobile program component call graphs to minimize the computational complexity of the program partition. Second, we formulate the second problem as a combinatorial optimization problem and transform it into an n-fold integer programming problem by mapping the remaining computing resources to a virtual component. Third, we design a reliable shadow component scheme between multilevel severs for the reliability problem. Finally, we develop a fast algorithm for the mix problem and analyze its performance and conduct experiments to prove the accuracy of our theoretical results.

Automated summary

Mobile edge computing system enhances user experience by providing cloud computing capabilities at the network edge, but faces challenges in ensuring reliability of offloaded computing and optimizing task allocation strategies.