The objective of the present study is to empirically explore the implementation of Green IT measures from the perspective of the dynamic resources in organizations. To this end, the paper integrates two streams of research- 1) the dynamic resources or capabilities of a firm and, 2) the adoption of Green IT measures. The findings suggest that two organizations in our sample have been successful in mobilizing their dynamic resources while implementing Green IT. As a result, these organizations have leveraged Green IT implementation for strategic purposes. Organizational awareness about Green IT is critical to initiating strategic actions in this area.

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a consistent and scientifically defensible set of data on Best Management Practice (“BMP”) designs and related performance. Although the individuals who completed the work on behalf of the Sponsors (“Project Team”) made an extensive effort to assess the quality of the data entered for consistency and accuracy, the Database information and/or any analysis results are provided on an “AS-IS ” basis and use of the Database, the data information, or any apparatus, method, or process disclosed in the Database is at the user’s sole risk. The Sponsors and the Project Team disclaim all warranties and/or conditions of any kind, express or implied, including, but not limited to any warranties or conditions of title, non-infringement of a third party’s intellectual property, merchantability, satisfactory quality, or fitness for a particular purpose. The Project Team does not warrant that the functions contained in the Database will meet the user’s requirements or that the operation of the Database will be uninterrupted or error free, or that any defects in the Database will be corrected.

Abstract High temperatures within a data center can cause a number of problems, such as increased cooling costs and increased hardware failure rates. To overcome this problem, researchers have shown that workload management, focused on a data center’s thermal properties, effectively reduces temperatures within a data center. In this paper, we propose a method to predict a workload’s thermal effect on a data center, which will be suitable for real-time scenarios. We use machine learning techniques, such as artificial neural networks (ANN) as our prediction methodology. We use real data taken from a data center’s normal operation to conduct our experiments. To reduce the data’s complexity, we introduce a thermal impact matrix to capture the spacial relationship between the data center’s heat sources, such as the compute nodes. Our results show that machine learning techniques can predict the workload’s thermal effects in a timely manner, thus making them well suited for real-time scenarios. Based on the temperature prediction techniques, we developed a

The real workload on a large scale computer system varies from time to time. Often it has many idle nodes during most operation time. These idle nodes consume energy, but do nothing useful. To save the huge amount of energy wasted by such active idle nodes, most modern compute nodes are equipped with multiple level dynamic sleep mechanisms to reduce power consumption. However, awaking sleeping nodes takes time. The deeper a node sleeps, the less energy it consumes, but the longer wakeup latency. How to balance between the systems energy consumption and the response time is a key problem in the power management of large scale systems. This paper proposes a self-adaptive approach to manage the sleep states of idle nodes to achieve low energy consumption and high performance at the same time. The proposed approach has two distinctive features. First, idle nodes are hierarchical organised. In this model, idle nodes are classified into several groups according to their sleep states. Each group contains nodes of same level of sleep depth and forms a reserve pool of a certain readiness level. When a resource is requested, nodes in the pool of highest level of readiness are preferentially allocated. When the nodes in the pool of the highest readiness level are not sufficient, the nodes in the pool(s) of next level(s) of readiness are allocated. After each allocation and reclaim of nodes, the numbers of nodes in each level of pools are adjusted by changing the sleep depth of the nodes up and down. Thus, the reserve pools can be maintained for high performance requirement. When resources are released from applications, they are placed

Data centers have become a critical computing infrastructure in the era of cloud computing. Tempera-ture monitoring and forecasting are essential for preventing server shutdowns because of overheating and improving a data center’s energy efficiency. This article presents a novel cyber-physical approach for tem-perature forecasting in data centers, one that integrates Computational Fluid Dynamics (CFD) modeling, in situ wireless sensing, and real-time data-driven prediction. To ensure forecasting fidelity, we leverage the realistic physical thermodynamic models of CFD to generate transient temperature distribution and cali-brate it using sensor feedback. Both simulated temperature distribution and sensor measurements are then used to train a real-time prediction algorithm. As a result, our approach reduces not only the computational complexity of online temperature modeling and prediction, but also the number of deployed sensors, which enables a portable, noninvasive thermal monitoring solution that does not rely on the infrastructure of a monitored data center. We extensively evaluated the proposed system on a rack of 15 servers and a testbed of five racks and 229 servers in a small-scale production data center. Our results show that our system can predict the temperature evolution of servers with highly dynamic workloads at an average error of 0.52◦C, within a duration up to 10 minutes. Moreover, our approach can reduce the required number of sensors by

Specialization of datacenter resources brings performance and energy improvements in response to the growing scale and diversity of cloud applications. Yet heterogeneous hardware adds complexity and volatility to latency-sensitive applications. A resource allocation mechanism that leverages architectural principles can overcome both of these obstacles. We integrate research in heterogeneous architectures with recent advances in multi-agent systems. Em-bedding architectural insight into proxies that bid on behalf of applications, a market effectively allocates hardware to applications with diverse preferences and valuations. Exploring a space of heterogeneous dat-acenter configurations, which mix server-class Xeon and mobile-class Atom processors, we find an optimal heterogeneous balance that improves both welfare and energy-efficiency. We further design and evaluate twelve design points along the Xeon-to-Atom spectrum, and find that a mix of three processor architectures achieves a 12 × reduction in response time violations relative to equal-power homogeneous systems.

Abstract There is a lack of thermal models for storage clusters; most existing thermal models do not take into account the utilization of hard drives (HDDs) and solid state disks (SSDs). To address this problem, we build a ther-mal model for hybrid storage clusters that are comprised of HDDs and SSDs. We start this study by generating the thermal profiles of hard drives and solid state disks. The profiling results show that both HDDs and SSDs have pro-found impacts on temperatures of storage nodes in a cluster. Next, we build two types of hybrid storage clusters, namely,

Global warming and the increase of toxic waste generated by electronic devices are some of the issues that are being currently addressed through the use of the so-called “green technologies”. Although the solution to these important problems does not depend on a single individual, industry, government, or nation, there are contributions that can be made at each of the levels indicated to reduce global warming and toxic waste. In this paper the authors’ addresses some of these issues within the context of green data centers and why it is necessary to stimulate the creation of such centers to help us save the environment and the global community at large. In this respect green data centers can be both environmentally and financially efficient by reducing energy consumption.

Rising data center energy consumption and increasing energy costs have combined to elevate the importance of reducing data center energy consumption as a strategy to reduce costs, manage capacity, and promote environmental responsibility. Within almost every organization, data center energy consumption has been driven by demand for greater computing capacity and increased IT centralization. The demand has been increasing by approximately 12 % per year. 1 While this was occurring, U.S. electricity prices have increased by 4.4 % per year. 2 The financial implications are significant. Estimates of annual power costs for U.S. data centers now range as high as $3.3 billion. 1 The good news is that there is general agreement within the industry that improvements in data center efficiency are possible. A 2007 EPA report to the U.S. Congress concluded that best practices can reduce data center energy consumption