Abstract
Recently, mobile edge computing (MEC) technology was developed to mitigate the overload problem in networks and cloud systems. An MEC system computes the offloading computation tasks from resource-constrained Internet of Things (IoT) devices. In addition, several convergence technologies with renewable energy resources (RERs) such as photovoltaics have been proposed to improve the survivability of IoT systems. This paper proposes an MEC integrated with RER system, which is referred to as energy-harvesting (EH) MEC. Since the energy supply of RERs is unstable due to various reasons, EH MEC needs to consider the state-of-charge (SoC) of the battery to ensure system stability. Therefore, in this paper, we propose an offloading scheduling algorithm considering the battery of EH MEC as well as the service quality of experience (QoE). The proposed scheduling algorithm consists of a two-stage operation, where the first stage consists of admission control of the offloading requests and the second stage consists of computation frequency scheduling of the MEC server. For the first stage, a non-convex optimization problem is designed considering the computation capability, SoC, and request deadline. To solve the non-convex problem, a greedy algorithm is proposed to obtain approximate optimal solutions. In the second stage, based on Lyapunov optimization, a low-complexity algorithm is proposed, which considers both the workload queue and battery stability. In addition, performance evaluations of the proposed algorithm were conducted via simulation. However, this paper has a limitation in terms of verifying in a real-world scenario.
Highlights
In recent years, along with the development of the Internet of Things (IoT) technology, it has become easier to connect mobile devices to the Internet [1,2,3]
These algorithms are not suitable for the proposed EH mobile edge computing (MEC) environment where the MEC server is equipped with energy harvesting module, i.e., our proposed scheme aims at the stable operation of MEC server in energy harvesting environment, but previous studies aimed at survivability of IoT devices
This paper proposes optimization formulations and their corresponding algorithms for computation offloading scheduling in an EH MEC system
Summary
Along with the development of the Internet of Things (IoT) technology, it has become easier to connect mobile devices to the Internet [1,2,3]. The explosive growth of IoT data has resulted in increased traffic load in networks and cloud systems This overload reduces the quality of experience (QoE) of the services and can result in network blackout, which shuts down the network system [6,7]. This paper proposes an EH MEC scheduling algorithm that considers the battery stability. The MEC server determines its computation frequency based on the battery state-of-charge (SoC) (referred to as MEC scheduling). If the SoC of the battery is sufficient, the MEC server raises the computation frequency for faster offloading service. They cannot be solved in non-deterministic polynomial time To solve this problem, we propose a greedy algorithm to find approximate solutions for offloading scheduling. For MEC scheduling, a Lyapunov optimization-based scheduling algorithm is proposed to find the optimal computation frequency in real time.
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