Demand-Response Power Management Strategy Using Time Shifting Capabilities

Energy efficiency is an important concern in the operation and deployment of the communications networks and cloud computing services, and due to the rising energy consumption, demand-response strategies are envisioned as promising alternatives. Despite the fact that many works have been proposed to improve the energy efficiency, these techniques and mechanisms do not consider the dynamic behavior of the energy generation or the possibility of rescheduling the energy loads according to the amount of available energy, moreover these proposals are mostly focused on reducing energy consumption. In this context, this paper presents a novel strategy in which the data centers, at the core of the network infrastructure, services and innovations, perform the power management by fostering the cooperation of consumers to adapt the energy demands to the available power. In this demand-response approach, data centers as power managers, are responsible for executing algorithms that allows to obtain the optimal scheduling of tasks in time. This proactive redistribution of demands aims to maximize the full available power utilization and minimize power waste. The proposed strategy is developed to find the exact solution using brute force algorithms based on a combinatorial algorithm, in order to be able to develop future heuristics for practical implementations. Simulations results validate its performance, while demonstrating improvements in the use of power and in the execution of tasks.

[1]  Sonja Klingert,et al.  Making Data Centers Fit for Demand Response: Introducing GreenSDA and GreenSLA Contracts , 2018, IEEE Transactions on Smart Grid.

[2]  Shaolei Ren,et al.  A Truthful Incentive Mechanism for Emergency Demand Response in Geo-Distributed Colocation Data Centers , 2016, ACM Trans. Model. Perform. Evaluation Comput. Syst..

[3]  Franco Davoli,et al.  Energy Efficiency in the Future Internet: A Survey of Existing Approaches and Trends in Energy-Aware Fixed Network Infrastructures , 2011, IEEE Communications Surveys & Tutorials.

[4]  Jan T. Bialasiewicz,et al.  Power-Electronic Systems for the Grid Integration of Renewable Energy Sources: A Survey , 2006, IEEE Transactions on Industrial Electronics.

[5]  Julian de Hoog,et al.  Interconnecting Fog computing and microgrids for greening IoT , 2016, 2016 IEEE Innovative Smart Grid Technologies - Asia (ISGT-Asia).

[6]  Sangtae Ha,et al.  Clarifying Fog Computing and Networking: 10 Questions and Answers , 2017, IEEE Communications Magazine.

[7]  Sonja Klingert,et al.  Integrating data centres into demand-response management: A local case study , 2013, IECON 2013 - 39th Annual Conference of the IEEE Industrial Electronics Society.

[8]  Florin Mariasiu,et al.  Electric vehicle battery technologies: From present state to future systems , 2015 .

[9]  Wu He,et al.  Internet of Things in Industries: A Survey , 2014, IEEE Transactions on Industrial Informatics.