Optimal Computation Load Balancing for Integrated Service Caching and Offloading Systems in Hierarchical Cloud Architecture

Mobile services such as Augmented Reality (AR) and online gaming require high computing and storage resources with real-time data exchange with servers. However, mobile devices are resource-constrained and cloud computing incurs additional network latency which makes them dissatisfactory to use such mobile services. To tackle this challenge, service caching has been emerging as a critical technology that provides services by leasing resources from mobile edge computing (MEC) servers located close to the users. Unlike content caching, which stores data only for delivery, service caching utilizes both computing and storage resources, and thereby should be jointly considered with code offloading which offloads the processing workloads of mobile devices to servers. Therefore, the conventional caching strategy that caches the most popular services may not be the best choice, and the intertwined nature of service caching and code offloading policies makes the system operation complex. In this paper, we formulate an average cost (energy consumption, offloading payment) minimization problem of mobile devices constrained by service queue stability in a three-layer (mobile/MEC/cloud) network architecture. We apply a statistic-based dynamic optimization framework to derive an online-fashioned interTWined Interplay between Storage and Transmission, namely TWIST algorithm that jointly makes decisions of service caching, CPU clock frequency of mobile devices, and code offloading. Next, via theoretical analysis, we show the trade-off relationships between the average cost of mobile devices and service delay. Finally, extensive simulations demonstrate that TWIST can save up to 51% of cost for the same delay of 2.41sec. compared to the existing algorithms.