MuSIC: Mobility-Aware Optimal Service Allocation in Mobile Cloud Computing

This paper exploits the observation that using tiered clouds, i.e. clouds at multiple levels (local and public) can increase the performance and scalability of mobile applications. User Mobility introduces new complexities in enabling an optimal decomposition of tasks that can execute cooperatively on mobile clients and the tiered cloud architecture while considering multiple QoS goals such application delay, device power consumption and user cost/price. In this paper, we propose a novel framework to model mobile applications as a location-time workflows (LTW) of tasks, here user mobility patterns are translated to a mobile service usage patterns. We show that an optimal mapping of LTWs to tiered mobile cloud resources is an NP-hard problem. We propose an efficient heuristic algorithm called MuSIC that is able to perform well (78% of optimal, 30% better than simple strategies), and scale well to a large number of users while ensuring high application QoS. We evaluate MuSIC and the 2-tier mobile cloud approach via implementation (on real world clouds) and extensive simulations using rich mobile applications like intensive signal processing and video streaming applications. Our experimental and simulation results indicate that MuSIC supports scalable operation (100+ concurrent users executing complex workflows) while improving QoS. We observe about 25% lower delays and power (under fixed price constraints) and about 35% decrease in price (considering fixed delay) in comparison to only using the public cloud. Our studies also show that MuSIC performs quite well under different mobility patterns, e.g. random waypoint, Manhattan models and is resilient to errors/uncertainty in prediction of mobile user location-time workflows.