A Resilient and Robust Edge-Cloud Network System Supporting CPS

Tanmoy Sen, Haiying Shen, Walid Saad, Thinh T. Doan

Abstract: Many outages of cloud computing services are caused by the natural disasters or common causes such as software failure, hardware failure, cyber attacks, and power outage. As a result, it is critical to develop resilient and robust cyber-physical system (CPS) to support continuity of service for smart and connected communities (S&CC). In spite of considerate research efforts on virtual machine (VM) migration for enhancing failure-resilience of datacenters, one issue still needs to be effectively addressed: how to determine the best destination for VM migration to avoid the influence from a given failure and enable edge nodes to connect to the VMs continuously. In this paper, we aim to handle these important issues to build a resilient and robust edge-cloud (or fog) network system supporting CPS for S&CC. We propose a machine learning based method for VM migration destination determination for the VMs in the predicted failure domains. We also propose a continuous edge-cloud connection method in wireless network component that enables edge nodes to continuously connect with the VMs through intermediate edge nodes when they cannot directly connect to their original VMs due to VM migration. Our experimental results show our proposed system reduces the number of job failures by 1.8 times and reduces the total job completion time by 48% for completed jobs compared to the case without our system.