TAME: an Efficient Task Allocation Algorithm for Integrated Mobile Gaming

We consider an integrated mobile gaming platform, in which the mobile device (e.g., smartphone) of a player can offload some game tasks toward a server as well as some neighboring mobile devices. The advantages of such a platform are manyfold: it can lead to an improved game experience, to a better use of energy resources, and, while offloading tasks to other mobile users, to the exploitation of the unused computing and storage resources of the mobile equipments, thus reducing the bandwidth and computing costs of the overall system. In this context, we formulate the problem of offloading the game computational tasks as an optimization problem that minimizes the maximum energy consumption across a set of mobile devices, under the constraints of a maximum response time and a limited availability of computation, communication and storage resources. In light of the problem complexity, we then propose a heuristic, called TAME, which is shown to closely approximate the optimal solution in all scenarios we considered. TAME also outperforms state-of-the-art algorithms under both synthetic and real scenarios, which have been devised based on a realistic and detailed energy consumption model for computation and communication resources. Our results, although tailored to mobile gaming, could be extended to other applications where it may be beneficial to offload computational and storage tasks through device-to-device communications, as enabled by Wi-Fi, Bluetooth, or the upcoming 5G technology

Joint Service Placement and Request Routing in Multi-cell Mobile Edge Computing Networks

The proliferation of innovative mobile services such as augmented reality, networked gaming, and autonomous driving has spurred a growing need for low-latency access to computing resources that cannot be met solely by existing centralized cloud systems. Mobile Edge Computing (MEC) is expected to be an effective solution to meet the demand for low-latency services by enabling the execution of computing tasks at the network-periphery, in proximity to end-users. While a number of recent studies have addressed the problem of determining the execution of service tasks and the routing of user requests to corresponding edge servers, the focus has primarily been on the efficient utilization of computing resources, neglecting the fact that non-trivial amounts of data need to be stored to enable service execution, and that many emerging services exhibit asymmetric bandwidth requirements. To fill this gap, we study the joint optimization of service placement and request routing in MEC-enabled multi-cell networks with multidimensional (storage-computation-communication) constraints. We show that this problem generalizes several problems in literature and propose an algorithm that achieves close-to-optimal performance using randomized rounding. Evaluation results demonstrate that our approach can effectively utilize the available resources to maximize the number of requests served by low-latency edge cloud servers.Comment: IEEE Infocom 201