QoS-aware task offloading in distributed cloudlets with virtual network function services

Pushing the cloud frontier to the network edge has attracted tremendous interest not only from cloud operators of the IT service/software industry but also from network service operators that provide various network services for mobile users. In particular, by deploying cloudlets in metropolitan area networks, network service providers can provide various network services through implementing virtualized network functions to meet the demands of mobile users. In this paper we formulate a novel task offloading problem in a metropolitan area network, where each offloaded task requests a specific network function with a maximum tolerable delay and different offloading requests may require different network services. We aim to maximize the number of requests admitted while minimizing their admission cost within a finite time horizon. We first show that the problem is NP-hard, and then devise an efficient algorithm through reducing the problem to a series of minimum weight maximum matching in auxiliary bipartite graphs. We also consider dynamic changes of offloading request patterns over time, and develop an effective prediction mechanism to release and/or create instances of network functions in different cloudlets for cost savings. We finally evaluate the performance of the proposed algorithms through experimental simulations. Experimental results indicate that the proposed algorithms are promising.