Abstract—By introduction of microgrids, energy management is required to control the power generation and consumption for residential, industrial, and commercial domains, e.g. in residential microgrids and homes. Energy management may also help us to reach zero net energy for the residential domain. Improvement in technology, cost, and feature size have enabled devices everywhere, to be connected and interactive, as it is called Internet of Things (IoT). The increasing complexity and data, due to the growing number of devices like sensors and actuators, requires powerful computing resources which may be provided by cloud computing. However, scalability has become the potential issue in cloud computing. In this paper, fog com-puting is introduced as a novel platform for energy management. The scalability, adaptability, and open source software/hardware featured in the proposed platform enable the user to implement the energy management with the customized control-as-services, while minimizing the implementation cost and time-to-market. To demonstrate the energy management-as-a-service over fog computing platform in different domains, two prototypes of home energy management and microgrid-level energy management have been implemented and experimented.

Abstract—The power consumption of enormous network de-vices in data centers has emerged as a big concern to data center operators. Despite many traffic-engineering-based solutions, very little attention has been paid on performance-guaranteed energy saving schemes. In this paper, we propose a novel energy-saving model for data center networks by scheduling and routing “deadline-constrained flows ” where the transmission of every flow has to be accomplished before a rigorous deadline, being the most critical requirement in production data center networks. Based on speed scaling and power-down energy saving strategies for network devices, we aim to explore the most energy efficient way of scheduling and routing flows on the network, as well as determining the transmission speed for every flow. We consider two general versions of the problem. For the version of only flow scheduling where routes of flows are pre-given, we show that it can be solved polynomially and we develop an optimal combina-torial algorithm for it. For the version of joint flow scheduling and routing, we prove that it is strongly NP-hard and cannot have a Fully Polynomial-Time Approximation Scheme (FPTAS) unless P=NP. Based on a relaxation and randomized rounding technique, we provide an efficient approximation algorithm which can guarantee a provable performance ratio with respect to a polynomial of the total number of flows. I.