Model-Based Design, Analysis, and Implementations for Power and Energy-Efficient Computing Systems

Modern computing systems are becoming increasingly complex. On one end of the spectrum, personal computers now commonly support multiple processing cores, and, on the other end, Internet services routinely employ thousands of servers in distributed locations to provide the desired service to its users. In such complex systems, concerns about energy usage and power consumption are increasingly important. Moreover, growing awareness of environmental issues has added to the overall complexity by introducing new variables to the problem. In this regard, the ability to abstractly focus on the relevant details allows model-based design to help significantly in the analysis and solution of such problems. In this dissertation, we explore and analyze model-based design for energy and power considerations in computing systems. Although the presented techniques are more generally applicable, we focus their application on large-scale Internet services operating in U.S. electricity markets. Internet services are becoming increasingly popular in the ICT ecosystem of today. The physical infrastructure to support such services is commonly based on a group of cooperative data centers (DCs) operating in tandem. These DCs are geographically distributed to provide security and timing guarantees for their customers. To provide services to millions of customers, DCs employ hundreds of thousands of servers. These servers consume a large amount of energy that is traditionally produced by burning coal and employing other environmentally hazardous methods, such as nuclear and gas power generation plants. This large energy consumption results in significant and fast-growing financial and environmental costs. Consequently, for protection of local and global environments, governing bodies around the globe have begun to introduce legislation to encourage energy consumers, especially corporate entities, to increase the share of renewable energy (green energy) in their total energy consumption. However, in U.S. electricity markets, green energy is usually more expensive than energy generated from traditional sources like coal or petroleum. We model the overall problem in three sub-areas and explore different approaches aimed at reducing the environmental foot print and operating costs of multi-site Internet services, while honoring the Quality of Service (QoS) constraints as contracted in service level agreements (SLAs). Firstly, we model the load distribution among member DCs of a multi-site Internet service. The use of green energy is optimized considering different factors such as (a) geographically and temporally variable electricity prices, (b) the multitude of available energy sources to choose from at each DC, (c) the necessity to support more than one SLA, and, (d) the requirements to offer more than one service at each DC. Various approaches are presented for solving this problem and extensive simulations using Google’s setup in North America are used to evaluate the presented approaches. Secondly, we explore the area of shaving the peaks in the energy demand of large electricity consumers, such as DCs by using a battery-based energy storage system. Electrical demand of DCs is typically peaky based on the usage cycle of their customers. Resultant peaks in the electrical demand require development and maintenance of a costlier energy delivery mechanism, and are often met using expensive gas or diesel generators which often have a higher environmental impact. To shave the peak power demand, a battery can be used which is charged during low load and is discharged during the peak loads. Since the batteries are costly, we present a scheme to estimate the size of battery required for any variable electrical load. The electrical load is modeled using the concept of arrival curves from Network Calculus. Our analysis mechanism can help determine the appropriate battery size for a given load arrival curve to reduce the peak. Thirdly, we present techniques to employ intra-DC scheduling to regulate the peak power usage of each DC. The model we develop is equally applicable to an individual server with multi-/many-core chips as well as a complete DC with an intermix of homogeneous and heterogeneous servers. We evaluate these approaches on single-core and multi-core chip processors and present the results. Overall, our work demonstrates the value of model-based design for intelligent load distribution across DCs, storage integration, and per DC optimizations for efficient energy management to reduce operating costs and environmental footprint for multi-site Internet services

Optimal Power Cost Management Using Stored Energy in Data Centers

Since the electricity bill of a data center constitutes a significant portion of its overall operational costs, reducing this has become important. We investigate cost reduction opportunities that arise by the use of uninterrupted power supply (UPS) units as energy storage devices. This represents a deviation from the usual use of these devices as mere transitional fail-over mechanisms between utility and captive sources such as diesel generators. We consider the problem of opportunistically using these devices to reduce the time average electric utility bill in a data center. Using the technique of Lyapunov optimization, we develop an online control algorithm that can optimally exploit these devices to minimize the time average cost. This algorithm operates without any knowledge of the statistics of the workload or electricity cost processes, making it attractive in the presence of workload and pricing uncertainties. An interesting feature of our algorithm is that its deviation from optimality reduces as the storage capacity is increased. Our work opens up a new area in data center power management.Comment: Full version of Sigmetrics 2011 pape

OPTIMIZING THE USE OF ENERGY STORAGE AS A DEMAND RESPONSE TOOL

The renewable energies expansion over last years, due to the need to bring electricity production towards ever higher levels of green production and the increase of the demand, have brought further stability problems to the main grid. The handling of the integration of these alternative sources and the optimization of the electricity grid have given high attention on the role of demand response program as a key part for the target. The combination of battery storage units with real-time prices is part of the research effort that aims to reduce the instability of the grid and the energy costs of the users. Literature shows good potential for the control strategies as the relative wide range of technologies developed recently for the scope, even if for the residential customers usually the potential is constrained by the limited controllable loads and their significant share of consumption. However, the aspect of user comfort is not always fully considered leading to less realistic conclusions. The objective of the work described in the dissertation was then to obtain a reduction in residential energy costs through the optimal scheduling of user appliances supported by the use of battery storage, under a real-time price scheme, while limiting the discomfort for the customer. Although the first results of applying a real time pricing scheme based on the current variations in price observed in the Iberian wholesale market led only to small profits when not considering additional self-generation, they increased significantly if a small photovoltaic based production is considered, and reached significant cost savings (circa 70%) in periods of high solar generation. But, when applying a real time price following the fluctuations of the renewable energy supply, which produced much higher variations in price, the results improved considerably, reaching cost savings as high as 85%. The implemented model shows the true relevance of Demand Response and Energy Storage, producing meaningful savings if the supply costs change with the availability of renewable energy supply. With self-generation, the obtained value is even higher in the perspective of the individual customer, maximizing the cost-effectiveness of such investment