Fear of increasing prices and concern about climate change are motivating residential power conservation efforts. We investigate the effectiveness of several unsupervised disaggregation methods on low frequency power measurements collected in real homes. Specifically, we consider variants of the factorial hidden Markov model. Our results indicate that a conditional factorial hidden semi-Markov model, which integrates additional features related to when and how appliances are used in the home and more accurately represents the power use of individual appliances, outperforms the other unsupervised disaggregation methods. Our results show that unsupervised techniques can provide perappliance power usage information in a non-invasive manner, which is ideal for enabling power conservation efforts.
The demand for data center computing increased significantly in recent years resulting in huge energy consumption. Data centers typically comprise three main subsystems: IT equipment provides services to customers; power infrastructure supports the IT and cooling equipment; and the cooling infrastructure removes the generated heat. This work presents a novel approach to model the energy flows in a data center and optimize its holistic operation. Traditionally, supply-side constraints such as energy or cooling availability were largely treated independently from IT workload management. This work reduces cost and environmental impact using a holistic approach that integrates energy supply, e.g., renewable supply and dynamic pricing, and cooling supply, e.g., chiller and outside air cooling, with IT workload planning to improve the overall attainability of data center operations. Specifically, we predict renewable energy as well as IT demand and design an IT workload management plan that schedules IT workload and allocates IT resources within a data center according to time varying power supply and cooling efficiency. We have implemented and evaluated our approach using traces from real data centers and production systems. The results demonstrate that our approach can reduce the recurring power costs and the use of non-renewable energy by as much as 60% compared to existing, non-integrated techniques, while still meeting operational goals and Service Level Agreements.
This paper presents a novel approach to correctly allocate resources in data centers, such that SLA violations and energy consumption are minimized. Our approach first analyzes historical workload traces to identify long-term patterns that establish a "base" workload. It then employs two techniques to dynamically allocate capacity: predictive provisioning handles the estimated base workload at coarse time scales (e.g., hours or days) and reactive provisioning handles any excess workload at finer time scales (e.g., minutes). The combination of predictive and reactive provisioning achieves a significant improvement in meeting SLAs, conserving energy, and reducing provisioning costs. We implement and evaluate our approach using traces from four production systems. The results show that our approach can provide up to 35% savings in power consumption and reduce SLA violations by as much as 21% compared to existing techniques, while avoiding frequent power cycling of servers.
Improving the cooling efficiency of servers has become an essential requirement in data centers today as the power used to cool the servers has become an increasingly large component of the total power consumption. Additionally, fan speed control has emerged in recent years as a critical part of system thermal architecture. However, the state of the art in server fan control often results in over provisioning of air flow that leads to high fan power consumption. It can be exacerbated in server architectures that share cooling resources among server components, where single hot spot can often drive the operation of a multiplicity of fans. To address this problem, this paper presents a novel multi-input multi-output (MIMO) fan controller that utilizes thermal models developed from first-principles to manipulate the operation of fans. The controller tunes the speeds of individual fans proactively based on prediction of the sever temperatures. Experimental results show that, with fans controlled by the optimal controller, over-provisioning of cooling air is eliminated, temperatures are more tightly controlled and fan energy consumption is reduced by up to 20% compared to that with a zone-based feedback controller.
Reduction of resource consumption in data centers is becoming a growing concern for data center designers, operators and users. Accordingly, interest in the use of renewable energy to provide some portion of a data center's overall energy usage is also growing. One key concern is that the amount of renewable energy necessary to satisfy a typical data center's power consumption can lead to prohibitively high capital costs for the power generation and delivery infrastructure, particularly if on-site renewables are used. In this paper, we introduce a method to operate a data center with renewable energy that minimizes dependence on grid power while minimizing capital cost. We achieve this by integrating data center demand with the availability of resource supplies during operation. We discuss results from the deployment of our method in a production data center.
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