Energy-Delay Tradeoff for Dynamic Offloading in Mobile-Edge Computing System With Energy Harvesting Devices

Mobile-edge computing (MEC) has aroused significant attention for its performance to accelerate application's operation and enrich user's experience. With the increasing development of green computing, energy harvesting (EH) is considered as an available technology to capture energy from circumambient environment to supply extra energy for mobile devices. In this paper, we propose an online dynamic tasks assignment scheduling to investigate the tradeoff between energy consumption and execution delay for an MEC system with EH capability. We formulate it into an average weighted sum of energy consumption and execution delay minimization problem of mobile device with the stability of buffer queues and battery level as constraints. Based on the Lyapunov optimization method, we obtain the optimal scheduling about the CPU-cycle frequencies of mobile device and transmit power for data transmission. Besides, the dynamic online tasks offloading strategy is developed to modify the data backlogs of queues. The performance analysis shows the stability of the battery energy level and the tradeoff between energy consumption and execution delay. Moreover, the MEC system with EH devices and task buffers implements the high energy efficient and low latency communications. The performance of the proposed online algorithm is validated with extensive trace-driven simulations.

I. Introduction

The growing preference for mobile devices has promoted the rapid development of mobile applications [1], leading to extremely intensive data computing [2] and more rigorous requirements for mobile devices, i.e., execution and storage ability, as well as battery energy [3]. Mobile-edge computing (MEC) is a new paradigm that relies on its internal provided wireless network to acquire computational capabilities [4]. Compared with the traditional remote cloud (e.g., Amazon, Google, etc.) [5], offloading workload to an MEC server not only reduces the congestion of data transmission but also saves the energy consumption of user [6], [7] on account of the MEC server physically close to users [8]. Therefore, it is pressing to take advantage of the MEC features to share the intensive workload of users (through offloading) [9] and reduce traffic overhead of industrial applications [10]. In addition, it is worthwhile to leverage energy harvesting (EH) technology to capture the green energy (e.g., solar, wind and/or solar radiation, etc.) for charging battery constantly [11]. In conclusion, the MEC system with EH devices improves its computational service capabilities [12], [13], which is also consistent with the notion of green communication [14].