Data centers are critical, energy-hungry infrastructures that run large-scale Internet-based services. Energy consumption models are pivotal in designing and optimizing energy-efficient operations to curb excessive energy consumption in data centers. In this paper, we survey the state-of-the-art techniques used for energy consumption modeling and prediction for data centers and their components. We conduct an in-depth study of the existing literature on data center power modeling, covering more than 200 models. We organize these models in a hierarchical structure with two main branches focusing on hardware-centric and software-centric power models. Under hardware-centric approaches we start from the digital circuit level and move on to describe higher-level energy consumption models at the hardware component level, server level, data center level, and finally systems of systems level. Under the software-centric approaches we investigate power models developed for operating systems, virtual machines and software applications. This systematic approach allows us to identify multiple issues prevalent in power modeling of different levels of data center systems, including: i) few modeling efforts targeted at power consumption of the entire data center ii) many state-of-the-art power models are based on a few CPU or server metrics, and iii) the effectiveness and accuracy of these power models remain open questions. Based on these observations, we conclude the survey by describing key challenges for future research on constructing effective and accurate data center power models.

Data Center Energy Consumption Modeling: A Survey

Although cloud computing has rapidly emerged as a widely accepted computing paradigm, the research on cloud computing is still at an early stage. Cloud computing suffers from different challenging issues related to security, software frameworks, quality of service, standardization, and power consumption. Efficient energy management is one of the most challenging research issues. The core services in cloud computing system are the SaaS (Software as a Service), PaaS (Platform as a Service), and IaaS (Infrastructure as a Service). In this paper, we study state-of-the-art techniques and research related to power saving in the IaaS of a cloud computing system, which consumes a huge part of total energy in a cloud computing system. At the end, some feasible solutions for building green cloud computing are proposed. Our aim is to provide a better understanding of the design challenges of energy management in the IaaS of a cloud computing system.

State-of-the-art research study for green cloud computing

With the increase in electronic traffic, heat generated by electronic equipment, and the concomitant costs of powering cooling systems in electronic data centers are increasing continually. Various research groups working in academic institutions, research laboratories and industries have been applying a variety of tools to study and improve performance of data centers. This paper reviews recent contributions to the state of knowledge in data center thermal management. First, we review numerical and experimental studies on the most common air-cooled raised floor plenum type data centers. We then summarize published research on rack layout, efficiency and performance metrics for data centers, dynamic control and life cycle analyses, and validation of numerical models. Finally, we review some recently proposed cooling strategies and numerical optimization efforts. We find that the research carried out on thermal management of data centers has helped improve performance in many instances (such as rear door water cooled heat exchanger type rack), and has helped establish some physics-based criteria for data center designers. Based on the trends observed in this review, we expect further improvements to all aspects of data center design and operation in the near future, with a focus on real-time measurement & control, model validation and heuristics based optimization. In addition, some significant changes such as thermal energy storage and smart-grid capabilities are also expected to be incorporated into data center control strategies.

Advances in data center thermal management

A large portion of the power consumption of data centers can be attributed to cooling. In dynamic thermal management mechanisms for data centers and servers, thermal setpoints are typically chosen statically and conservatively, which leaves significant room for improvement in the form of improved energy efficiency. In this paper, we propose two hierarchical thermal-aware power optimization techniques that are complementary to each other and achieve (i) lower overall system power with no performance penalty or (ii) higher performance within the same power budget.

https://researcher.watson.ibm.com/researcher/files/us-lefurgy/igcc2011_tapo_paper.pdf

TAPO: Thermal-aware power optimization techniques for servers and data centers

Techniques for data center analysis are provided. In one aspect, a method for modeling thermal distributions in a data center is provided. The method includes the following steps. Vertical temperature distribution data is obtained for a plurality of locations throughout the data center. The vertical temperature distribution data for each of the locations is plotted as an s curve, wherein the vertical temperature distribution data reflects physical conditions at each of the locations which is reflected in a shape of the s curve. Each of the s curves is represented with a set of parameters that characterize the shape of the s curve, wherein the s curve representations make up a knowledge base model of predefined s curve types from which thermal distributions and associated physical conditions at the plurality of locations throughout the data center can be analyzed.

Knowledge-based models for data centers

The combination of rapidly increasing energy use of data centers (DCs), which is triggered by dramatic increases in IT (information technology) demands, and increases in energy costs and limited energy supplies has made the energy efficiency of DCs a central concern from both a cost and a sustainability perspective This paper describes three important technology components that address the energy consumption in DCs First, we present a mobile measurement technology (MMT) for optimizing the space and energy efficiency of DCs The technology encompasses the interworking of an advanced metrology technique for rapid data collection at high spatial resolution and measurement-driven modeling techniques, enabling optimal adjustments of a DC environment within a target thermal envelope Specific example data demonstrating the effectiveness of MMT is shown Second, the static MMT measurements obtained at high spatial resolution are complemented by and integrated with a real-time sensor network The requirements and suitable architectures for wired and wireless sensor solutions are discussed Third, an energy and thermal model analysis for a DC is presented that exploits both the high-spatial-resolution (but static) MMT data and the high-timeresolved (but sparse) sensor data The combination of these two data types (static and dynamic), in conjunction with innovative modeling techniques, provides the basis for extending the MMT concept toward an interactive energy management solution

J. M. Cook

Papers

Uncovering energy-efficiency opportunities in data centers