EVALUATING SCHEDULING METHODS FOR ENERGY COST REDUCTION IN A HETEROGENEOUS DATA CENTER ENVIRONMENT

Over the past two decades the rise of information technologies (IT) has enabled businesses to communicate, coordinate, and cooperate in unprecedented ways. However, this did not come without a price. Today, IT infrastructure accounts for a substantial fraction of the national energy consumption in most advanced countries. Subsequently, research turned to finding ways of making IT more sustainable and lessening the environmental impact of IT infrastructure. In our previous work we developed LINFIX, an innovative method for handling the scheduling problem in data centers, which substantially reduced the total energy consumption compared to commonly used practices. Due to the computational complexity of the scheduling problem, we were, however, unable to estimate the cost reduction of LINFIX compared to what is theoretically possible. In this work we employ a genetic algorithm to provide a benchmark to better assess the quality of the LINFIX solutions. While the genetic algorithm frequently finds better solutions, the additional average cost reduction when compared to LINFIX is less than 0.1 percent. Taking the computational speed into account, this confirms our hypothesis that LINFIX provides very energy efficient scheduling plans in short time

Architecture of an end-to-end energy consumption model for a cloud data center

Estimates show that a significant proportion of future ICT related energy consumption will be from Cloud Computing. Based on detail analysis and survey of energy consumption and optimization trends in cloud computing, this research presents a comprehensive end-to-end energy consumption model of a cloud facility extending from the end-user equipment to the data center facility. The model is subdivided into three planes and four associated layers and depicts the cross-plane and cross-layer relationships between the components in terms of energy consumption and potential optimization areas and provides a reference framework for planning power optimization strategies at a cloud facility

Improving data center efficiency through smart grid integration and intelligent analytics

The ever-increasing growth of the demand in IT computing, storage and large-scale cloud services leads to the proliferation of data centers that consist of (tens of) thousands of servers. As a result, data centers are now among the largest electricity consumers worldwide. Data center energy and resource efficiency has started to receive significant attention due to its economical, environmental, and performance impacts. In tandem, facing increasing challenges in stabilizing the power grids due to growing needs of intermittent renewable energy integration, power market operators have started to offer a number of demand response (DR) opportunities for energy consumers (such as data centers) to receive credits by modulating their power consumption dynamically following specific requirements. This dissertation claims that data centers have strong capabilities to emerge as major enablers of substantial electricity integration from renewables. The participation of data centers into emerging DR, such as regulation service reserves (RSRs), enables the growth of the data center in a sustainable, environmentally neutral, or even beneficial way, while also significantly reducing data center electricity costs. In this dissertation, we first model data center participation in DR, and then propose runtime policies to dynamically modulate data center power in response to independent system operator (ISO) requests, leveraging advanced server power and workload management techniques. We also propose energy and reserve bidding strategies to minimize the data center energy cost. Our results demonstrate that a typical data center can achieve up to 44% monetary savings in its electricity cost with RSR provision, dramatically surpassing savings achieved by traditional energy management strategies. In addition, we investigate the capabilities and benefits of various types of energy storage devices (ESDs) in DR. Finally, we demonstrate RSR provision in practice on a real server. In addition to its contributions on improving data center energy efficiency, this dissertation also proposes a novel method to address data center management efficiency. We propose an intelligent system analytics approach, "discovery by example", which leverages fingerprinting and machine learning methods to automatically discover software and system changes. Our approach eases runtime data center introspection and reduces the cost of system management.2018-11-04T00:00:00