Energy-Efficient Server Clusters

Elnozahy, E. N. Mootaz; Kistler, Michael; Rajamony, Ram

doi:10.1007/3-540-36612-1_12

Cited by 326 publications

(264 citation statements)

References 9 publications

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“…The technique proposed by Mastroleon [38] directly controls hardware energy savings by adjusting power distribution to different components in the data center. The problem with these hardware-based techniques is that most of them focus on real-time tasks and do not consider minimizing energy use by active computing nodes [39]. A popular trend currently being implemented in data center architecture is the use of large-scale, modular data centers composed of shipping containers filled with servers, but more radical proposals range from micro data centers placed in condominium closets to floating barges filled with servers running off of power generated from ocean currents [40].…”

Section: Related Workmentioning

confidence: 99%

Power Usage Effectiveness Metrics to Measure Efficiency and Performance of Data Centers

Uddin

Alsaqour

Shah

et al. 2014

Appl. Math. Inf. Sci.

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Abstract:Intensifying computation demand from enterprises has driven the growth of large, multifaceted data centers to manage current Internet, financial, commercial, and business applications. A data center comprises thousands of servers and other equipment that require substantial amounts of power to operate. This condition results in numerous challenges for the data center industry, such as massive energy consumption, underutilization of installed equipment, emission of greenhouse gases, and effect on global warming. This paper highlights the significance of identifying metrics to determine the performance and efficiency of a data center, which can help such a facility achieve operational cost savings through proper implementation of performance-measuring metrics. This paper discusses the implementation of Power Usage Effectiveness metrics in a tier-level data center in Pakistan. The results show that the overall performance value of the facility is 3.3, which indicates poor and inefficient operations.

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Section: Related Workmentioning

confidence: 99%

Power Usage Effectiveness Metrics to Measure Efficiency and Performance of Data Centers

Uddin

Alsaqour

Shah

et al. 2014

Appl. Math. Inf. Sci.

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“…Heo et al [23] have developed an adaptation graph analysis mechanism to solve the conflicts between interacting adaptive components, e.g., On/Off and dynamic voltage scaling policies in server farms, to minimize energy consumption. Elnozahy et al [20] investigate various combinations of dynamic voltage scaling and node on/off policies to reduce the energy consumption in server farms. Other strategies on reducing energy consumption of servers are also proposed (e.g., [26,47]).…”

Section: Related Workmentioning

confidence: 99%

“…It has also been reported that in a conservative estimation, Google hosts more than 500,000 servers in its data centers distributed in different locations and consumes at least 6.3 × 10 5 MWh in total annually [39]. Therefore, minimizing the energy consumption of cloud-scale data centers has recently received a lot of research attention (e.g., [20,17,29,13,47,18]). …”

Section: Introductionmentioning

confidence: 99%

GreenWare: Greening Cloud-Scale Data Centers to Maximize the Use of Renewable Energy

Zhang

Wang

2011

Lecture Notes in Computer Science

141

111

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Abstract. To reduce the negative environmental implications (e.g., CO2 emission and global warming) caused by the rapidly increasing energy consumption, many Internet service operators have started taking various initiatives to operate their cloud-scale data centers with renewable energy. Unfortunately, due to the intermittent nature of renewable energy sources such as wind turbines and solar panels, currently renewable energy is often more expensive than brown energy that is produced with conventional fossil-based fuel. As a result, utilizing renewable energy may impose a considerable pressure on the sometimes stringent operation budgets of Internet service operators. Therefore, two key questions faced by many cloud-service operators are 1) how to dynamically distribute service requests among data centers in different geographical locations, based on the local weather conditions, to maximize the use of renewable energy, and 2) how to do that within their allowed operation budgets.In this paper, we propose GreenWare, a novel middleware system that conducts dynamic request dispatching to maximize the percentage of renewable energy used to power a network of distributed data centers, subject to the desired cost budget of the Internet service operator. Our solution first explicitly models the intermittent generation of renewable energy, e.g., wind power and solar power, with respect to varying weather conditions in the geographical location of each data center. We then formulate the core objective of GreenWare as a constrained optimization problem and propose an efficient request dispatching algorithm based on linear-fractional programming (LFP). We evaluate GreenWare with realworld weather, electricity price, and workload traces. Our experimental results show that GreenWare can significantly increase the use of renewable energy in cloud-scale data centers without violating the desired cost budget, despite the intermittent supplies of renewable energy in different locations and time-varying electricity prices and workloads.

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“…A considerable amounts of energy can be saved by reducing resource consumption during non-peak conditions. Significant research efforts have been expended on applying dynamic voltage scaling (DVS) to computing systems in order to save power while meeting time or performance constraints [13,6,12,28,27,33].…”

Section: Application To Power and Performance In Data Centersmentioning

confidence: 99%

“…In realistic systems p varies between 2.5 and 3 [12]. A i , B i , and p can be obtained by curve fitting against empirical measurements when profiling the system off-line.…”

Section: Formulating the Optimization Problemmentioning

confidence: 99%

Introduction to Control Theory And Its Application to Computing Systems

Abdelzaher

Diao²,

Hellerstein

et al. 2008

Performance Modeling and Engineering

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Feedback control is central to managing computing systems and data networks. Unfortunately, computing practitioners typically approach the design of feedback control in an ad hoc manner. Control theory provides a systematic approach to designing feedback loops that are stable in that they avoid wild oscillations, accurate in that they achieve objectives such as target response times for service level management, and settle quickly to their steady state values. This paper provides an introduction to control theory for computing practitioners with an emphasis on applications in the areas of database systems, real-time systems, virtualized servers, and power management.

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Energy-Efficient Server Clusters

Cited by 326 publications

References 9 publications

Power Usage Effectiveness Metrics to Measure Efficiency and Performance of Data Centers

Power Usage Effectiveness Metrics to Measure Efficiency and Performance of Data Centers

GreenWare: Greening Cloud-Scale Data Centers to Maximize the Use of Renewable Energy

Introduction to Control Theory And Its Application to Computing Systems

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