Optimized resource allocation in edge-cloud environment
Edge computing has recently emerged as a promising solution that provides services with stringent requirements, like extremely low latency and bandwidth costs, for a class of new 5G applications. However, due to their limited computing capacity, edge data centers are not able to afford a large number of user requests. A combined scheme of edge and cloud is therefore required to provide extended cloud computing service at the last-mile close to end-users. Key challenges faced by this complex system which includes edge, core, and public cloud is optimizing resource allocation to provide desired QoS levels and meet data center capacity constraints. In this paper, we present a completed formulation of the optimized resource allocation problem in the edge-cloud environment, and then propose an algorithmic solution to the problem. Experimental simulations show that our solution is cost-effective with an improvement in terms of efficiency compared to prior work