EdgeMORE: improving resource allocation with multiple options from tenants

International audienceUnder the paradigm of Edge Computing (EC), a Network Operator (NO) deploys computational resources at the network edge and let third-party Service Providers (SPs) run on top of them, as tenants. Besides the clear advantages for SPs and final users thanks to the vicinity of computation nodes, a NO aims to allocate edge resources in order to increase its own utility, including bandwidth saving, operational cost reduction, QoE for its users, etc. However, while the number of third-party services competing for edge resources is expected to dramatically grow, the resources deployed cannot increase accordingly, due to physical limitations. Therefore, smart strategies are needed to fully exploit the potential of EC, despite its constrains. To this aim, we propose to leverage service adaptability, a dimension that has mainly been neglected so far: each service can adapt to the amount of resources that the NO has allocated to it, balancing the fraction of service computation performed at the edge and relying on remote servers, e.g., in the Cloud, for the rest. We propose EdgeMORE, a resource allocation strategy in which SPs express their capabilities to adapt to different resource constraints, by declaring the different configurations under which they are able to run, specifying the resources needed and the utility provided to the NO. The NO then chooses the most convenient option per each SP, in order to maximize the total utility. We formalize EdgeMORE as a Integer Linear Program. We show via simulation that EdgeMORE greatly improves EC utility with respect to the standard where no multiple options for running services are allowed

Wireless and Computing Resource Allocation for Selfish Computation Offloading in Edge Computing

We consider the problem of allocating wireless and computing resources to a set of autonomous wireless devices in an edge computing system. Devices in the system can decide whether or not to use edge computing resources for offloading computing tasks so as to minimize their completion time, while the edge cloud operator can allocate wireless and computing resources to the devices. We model the interaction between devices and the operator as a Stackelberg game, prove the existence of Stackelberg equilibria, and propose an efficient decentralized algorithm for computing equilibria. We provide a bound on the price of anarchy of the game, which also serves as an approximation ratio bound for the proposed algorithm. Our simulation results show that the joint allocation of wireless and computing resources by the operator can halve the completion times compared to a system with static resource allocation. At the same time, the convergence time of the proposed algorithm is approximately linear in the number of devices, and thus it could be effectively implemented for edge computing resource management.QC 20190905</p