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摘要
Cloud computing has become the de facto computing platform for application processing in the era of the Internet of Things (IoT). However, limitations of the cloud model, such as the high transmission latency and high costs are giving birth to a new computing paradigm called edge computing (a.k.a fog computing). Fog computing aims to move the data processing close to the network edge so as to reduce Internet traffic. However, since the servers at the fog layer are not as powerful as the ones in the cloud, there is a need to balance the data processing in between the fog and the cloud. Moreover, besides the data offloading issue, the energy efficiency of fog computing nodes has become an increasing concern. Densely deployed fog nodes are a major source of carbon footprint in IoT systems. To reduce the usage of the brown energy resources (e.g. powered by energy produced through fossil fuels), green energy is an alternative option. In this paper, we propose employing dual energy sources for supporting the fog nodes, where solar power is the primary energy supply and grid power is the backup supply. Based on that, we present a comprehensive analytic framework for incorporating green energy sources to support the running of IoT and fog computing-based systems, and to handle the tradeoff in terms of average response time, average monetary, and energy costs in the IoT. This paper describes an online algorithm, Lyapunov optimization on time and energy cost (LOTEC), based on the technique of Lyapunov optimization. LOTEC is a quantified near optimal solution and is able to make control decision on application offloading by adjusting the two-way tradeoff between average response time and average cost. We evaluate the performance of our proposed algorithm by a number of experiments. Rigorous analysis and simulations have demonstrated its performance.
- 出版日期2017
- 单位中国人民解放军国防科学技术大学
