Combining coded computation and reinforcement learning to improve edge computing in heterogeneous clusters

The emergence of Internet of Things (IoT) technology has led to extensive connections among various devices which leads to a production of a large amount of heterogeneous data. While, cloud computing is a suitable and efficient processing model for storing and processing this large data, the demand for real-time and delay-sensitive applications is increasing rapidly. Using only cloud computing cannot address this problem properly, because the network bandwidth is limited. Therefore, edge processing as a new processing model and a cloud computing supplement is proposed which is based on a distributed processing architecture. In the proposed reinforcement learning and distributed coding computing (RLCDC) method we harness the DDPG algorithm to manage resources. Additionally, maximum distance separable (MDS) codes are also used to deal with the remaining processors, and to remove the transmitted packets, as well as increasing the network's operational capacity and throughput.