Joint optimization method of intelligent service arrangement and computing-networking resource allocation for MEC

Joint optimization method of intelligent service arrangement and computing-networking resource allocation for MEC

Blog Article

To solve the problems of low efficiency of network service caching and computing-networking resource allocation caused by tasks differentiation, highly dynamic network environment, and decentralized computing-networking resource deployment in edge networks, a decentralized service arrangement and computing offloading model for mobile edge computing was investigated and established.Considering the multidimensional resource constraints, e.g.

, computing power, storage, and bandwidth, with dorisvale station for sale the objective of minimizing task processing latency, the joint optimization of service caching and computing-networking resource allocation was abstracted as a partially observable Markov decision process.Considering the temporal dependency of service request and its coupling relationship with service caching, a long short-term memory network was introduced to capture time-related network state information.Then, based on recurrent multi-agent deep reinforcement learning, a distributed service arrangement and resource allocation algorithm was proposed to autonomously decide service caching and computing-networking resource read more allocation strategies.

Simulation results demonstrate that significant performance improvements in terms of cache hit rate and task processing latency achieved by the proposed algorithm.