Cost-efficient workload scheduling in Cloud Assisted Mobile Edge Computing

Mobile edge computing is envisioned as a promising computing paradigm with the advantage of low latency. However, compared with conventional mobile cloud computing, mobile edge computing is constrained in computing capacity, especially under the scenario of dense population. In this paper, we propose a Cloud Assisted Mobile Edge computing (CAME) framework, in which cloud resources are leased to enhance the system computing capacity. To balance the tradeoff between system delay and cost, mobile workload scheduling and cloud outsourcing are further devised. Specifically, the system delay is analyzed by modeling the CAME system as a queuing network. In addition, an optimization problem is formulated to minimize the system delay and cost. The problem is proved to be convex, which can be solved by using the Karush-Kuhn-Tucker (KKT) conditions. Instead of directly solving the KKT conditions, which incurs exponential complexity, an algorithm with linear complexity is proposed by exploiting the linear property of constraints. Extensive simulations are conducted to evaluate the proposed algorithm. Compared with the fair ratio algorithm and the greedy algorithm, the proposed algorithm can reduce the system delay by up to 33% and 46%, respectively, at the same outsourcing cost. Furthermore, the simulation results demonstrate that the proposed algorithm can effectively deal with the challenge of heterogeneous mobile users and balance the tradeoff between computation delay and transmission overhead.