Migration-driven Resilient Disaster Response Edge-Cloud Deployments
Xiaojie Zhang, Saptarshi Debroy · 2019
Cloud based incidence response systems suffer from lack of network connectivity to offload compute intensive mission-critical applications to remote cloud. Thus, the next-generation incidence response solutions are becoming more edge-cloud based where computational resources are available closer to the disaster site. However, frequent and dynamic unpredictabilities or fluctuations generated in such edge-cloud deployments (e.g., using wireless spectrum in unlicensed manner) adversely impact the performance of mission-critical, real-time applications which often demand strict performance guarantees. Such fluctuations cause severe performance degradations to mission-critical applications that use such channels. In this paper, we propose an intelligent yet lightweight application task assignment (end-user device to edge-cloud) and application migration scheme (between edge-cloud resources) that can help mission-critical applications avoid impending fluctuations and improve system resilience. The proposed scheme implements Largest Bandwidth Largest Job-First Fit (LBLJ-FF) algorithm as a Unified Resource Broker (URB) service that optimizes transmission cost and migration overhead. The algorithm is light-weight and fast converging in reacting to sudden fluctuations. We demonstrate the performance of the proposed scheme through a realistic simulation that uses real fluctuation dataset. The results demonstrate the existence of an optimal trade-off point between transmission cost and migration overhead optimizations. The results also show the resilience of the proposed algorithm in improving job completion rate when the edge-cloud system is under intense fluctuation.