Energy Efficient Resource Management and Task Scheduling for IoT Services in Edge Computing Paradigm

Abstract

With the growing popularity of the Internet of Things (IoT), energy efficiency has been a critical concern during the design and development of IoT service systems. Meanwhile, edge computing has drawn significant attention as a burgeoning computing paradigm. This paper studies the energy efficiency issue of IoT systems by proposing a joint scheme of resource allocation and task scheduling under the edge computing paradigm. Specifically, dynamic processes of the IoT services and system are formulated by generalized queueing network models, based on which quantitative analyses of performance and energy consumption are conducted. The resource management and task scheduling are formulated by Markov Decision Process (MDP), which can balance the tradeoff between energy costs and QoS requirements. To attack the challenge of MDP search space explosion due to the large scale of IoT systems, Ordinal Optimization (OO) techniques are applied to the MDP algorithms, which are able to significantly narrow the search of MDP by slightly softening the optimization objective to a good enough subset. Finally, we conduct simulation experiments based on real-world IoT data. Evaluations and comparisons demonstrate that our approach is effective and efficient in practice.