Self managed cloud computing and edge computing system using deep reinforcement learning

Abstract

In order to tackle the latency problems in sensitive applications implemented in IoT devices, the edge computing came into existence. On top of that, the idea of a mobile edge computing (MEC) network, in which such computationally heavy activities are computed in many edge servers deployed adjacent to mobile devices, has recently gained popularity. Unlike cloud server, the edge device has a nite computation capacity and it can't handle massive computation tasks. There needs to be a smart task o oading scheme in order to decide whether to execute the task in the local device itself, or o oad it to the edge device and process there or again send it further to the cloud server for processing. Also the algorithm should be able to utilize the available network bandwidth e ciently. The optimization of joint o oading decision and bandwidth allocation in a multi user, multi task, multi server environment can be formulated as a mixed integer non linear programming problem (MINLP) in MEC to preserve energy and maintain quality of service for wireless devices. MINLP is a NP-hard problem and the time complexity to solve it grows exponentially. In this research work, the power of deep learning and reinforcement learning has been applied to solve this MINLP problem under a fraction of a second which makes it suitable for real world usage. Further an end to end integrated edge and cloud computing system has been proposed that switches from one to another whenever required, and leverages the bene ts of both paradigm.