Abstract

Multi-access edge computing is a promising technology for solving delay sensitive, computationally intensive applications. It aids users with low-latency access by deploying computing and storage resources close to the user side. However, as the computing resources of edge servers are relatively limited, directly offloading tasks to neighboring edge servers can easily overload them. Tasks that arrive at the overloaded edge server will be blocked due to latency exceeding its threshold. To make better use of edge servers in the metro optical network to support delay sensitive and computationally intensive tasks, we divide the tasks into two categories and model them. Partial offloading tasks can be distributed to multiple edge servers for cooperative computing, while binary offloading tasks can be offloaded to a lightly loaded edge server as a whole. In this paper, we put forward the problem of task optimal allocation. Furthermore, we design and implement a heuristic algorithm for cooperative offloading of dependency-aware tasks based on genetic algorithms. The algorithm jointly optimizes the allocation of routing and computing resources. Simulation results show that our proposed algorithm has superior performance.

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