An optimal big data processing for smart grid based on hybrid MDM/R architecture to strengthening RE Integration and EE in datacenter

摘要

Supply chain is a hard business area, where you need to have a perfect balance between demand and supply, day in and day out, by an intricate system that sits underneath it all. Achieving such a system by meeting the ambitious targets of the agreement on climate change can only be achieved through an effective combination of energy efficiency and renewable energy integration. The uncertainty and variability of renewable energy generation can pose challenges for grid operators and can requires additional actions to balance the system. Significant researches which aim is to improve energy efficiency of data center indicates that operating reserves could be procured from many complex and costly techniques. In this paper, we investigate the problem from scheduling of workloads in a data center in order to minimize its energy consumption budget, minimize the conventional grid dependence, and maximize the renewable energy provided to data center, by the ability to temporarily delay or degrade service, with a modified supply-following algorithm. This algorithm attempts to align power consumption with the amount of wind power available, while minimizing the time by which jobs exceed their deadlines. Modification of the algorithm has been performed in the direction of big data processing (wind trace, workload requests, prices, horizontal ellipsis ), servers management. This modification is performed by jobs classification into predefined classes using the classification and regression trees algorithm. New hybrid architecture that manages the Meter Data Management Repository MDM/R was introduced using MapReduce programming model for ETL process and Massive Parallel Processing Database for requests which strongly influences the accuracy and the speediness of the scheduler.