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179
A Taxonomy and Survey of Energy-Efficient Data Centers and Cloud Computing Systems
"... Traditionally, the development of computing systems has been focused on performance improvements driven by the demand of applications from consumer, scientific and business domains. However, the ever increasing energy consumption of computing systems has started to limit further performance growth d ..."
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Cited by 58 (4 self)
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Traditionally, the development of computing systems has been focused on performance improvements driven by the demand of applications from consumer, scientific and business domains. However, the ever increasing energy consumption of computing systems has started to limit further performance growth due to overwhelming electricity bills and carbon dioxide footprints. Therefore, the goal of the computer system design has been shifted to power and energy efficiency. To identify open challenges in the area and facilitate future advancements it is essential to synthesize and classify the research on power and energy-efficient design conducted to date. In this work we discuss causes and problems of high power / energy consumption, and present a taxonomy of energy-efficient design of computing systems covering the hardware, operating system, virtualization and data center levels. We survey various key works in the area and map them to our taxonomy to guide future design and development efforts. This chapter is concluded with a discussion of advancements identified in energy-efficient computing and our vision on future
Exploiting platform heterogeneity for power efficient data centers
- In Proceedings of the IEEE International Conference on Autonomic Computing (ICAC
, 2007
"... It has recently become clear that power management is of critical importance in modern enterprise computing environments. The traditional drive for higher performance has influenced trends towards consolidation and higher densities, artifacts enabled by virtualization and new small form factor serve ..."
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Cited by 57 (4 self)
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It has recently become clear that power management is of critical importance in modern enterprise computing environments. The traditional drive for higher performance has influenced trends towards consolidation and higher densities, artifacts enabled by virtualization and new small form factor server blades. The resulting effect has been increased power and cooling requirements in data centers which elevate ownership costs and put more pressure on rack and enclosure densities. To address these issues, in this paper, we enable power-efficient management of enterprise workloads by exploiting a fundamental characteristic of data centers: “platform heterogeneity”. This heterogeneity stems from the architectural and management-capability variations of the underlying platforms. We define an intelligent workload allocation method that leverages heterogeneity characteristics and efficiently maps workloads to the best fitting platforms, significantly improving the power efficiency of the whole data center. We perform this allocation by employing a novel analytical prediction layer that accurately predicts workload power/performance across different platform architectures and power management capabilities. This prediction infrastructure relies upon platform and workload descriptors that we define as part of our work. Our allocation scheme achieves on average 20 % improvements in power efficiency for representative heterogeneous data center configurations, highlighting the significant potential of heterogeneity-aware management. 1
Benefits and Limitations of Tapping into Stored Energy For Datacenters
, 2011
"... Datacenter power consumption has a significant impact on both its recurring electricity bill (Op-ex) and one-time construction costs (Cap-ex). Existing work optimizing these costs has relied primarily on throttling devices or workload shaping, both with performance degrading implications. In this p ..."
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Cited by 53 (7 self)
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Datacenter power consumption has a significant impact on both its recurring electricity bill (Op-ex) and one-time construction costs (Cap-ex). Existing work optimizing these costs has relied primarily on throttling devices or workload shaping, both with performance degrading implications. In this paper, we present a novel knob of energy buffer (eBuff) available in the form of UPS batteries in datacenters for this cost optimization. Intuitively, eBuff stores energy in UPS batteries during “valleys”- periods of lower demand, which can be drained during “peaks ”- periods of higher demand. UPS batteries are normally used as a fail-over mechanism to transition to captive power sources upon utility failure. Furthermore, frequent discharges can cause UPS batteries to fail prematurely. We conduct detailed analysis of battery operation to figure out feasible operating regions given such battery lifetime and datacenter availability concerns. Using insights learned from this analysis, we develop peak reduction algorithms that combine the UPS battery knob with existing throttling based techniques for minimizing datacenter power costs. Using an experimental platform, we offer insights about Op-ex savings offered by eBuff for a wide range of workload peaks/valleys, UPS provisioning, and application SLA constraints. We find that eBuff can be used to realize 15-45 % peak power reduction, corresponding to 6-18 % savings in Op-ex across this spectrum. eBuff can also play a role in reducing Cap-ex costs by allowing tighter overbooking of power infrastructure components and we quantify the extent of such Cap-ex savings. To our knowledge, this is the first paper to exploit stored energy- typically lying untapped in the datacenter- to address the peak power draw problem.
Energy Management for MapReduce Clusters
"... The area of cluster-level energy management has attracted significant research attention over the past few years. One class of techniques to reduce the energy consumption of clusters is to selectively power down nodes during periods of low utilization to increase energy efficiency. One can think of ..."
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Cited by 52 (3 self)
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The area of cluster-level energy management has attracted significant research attention over the past few years. One class of techniques to reduce the energy consumption of clusters is to selectively power down nodes during periods of low utilization to increase energy efficiency. One can think of a number of ways of selectively powering down nodes, each with varying impact on the workload response time and overall energy consumption. Since the MapReduce framework is becoming “ubiquitous”, the focus of this paper is on developing a framework for systematically considering various MapReduce node power down strategies, and their impact on the overall energy consumption and workload response time. We closely examine two extreme techniques that can be accommodated in this framework. The first is based on a recently proposed technique called “Covering Set ” (CS) that keeps only a small fraction of the nodes powered up during periods of low utilization. At the other extreme is a technique that we propose in this paper, called the All-In Strategy (AIS). AIS uses all the nodes in the cluster to run a workload and then powers down the entire cluster. Using both actual evaluation and analytical modeling we bring out the differences between these two extreme techniques and show that AIS is often the right energy saving strategy. 1.
Optimal Online Deterministic Algorithms and Adaptive Heuristics for Energy and Performance Efficient Dynamic Consolidation of Virtual Machines in Cloud Data Centers
"... The rapid growth in demand for computational power driven by modern service applications combined with the shift to the Cloud computing model have led to the establishment of large-scale virtualized data centers. Such data centers consume enormous amounts of electrical energy resulting in high opera ..."
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Cited by 51 (5 self)
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The rapid growth in demand for computational power driven by modern service applications combined with the shift to the Cloud computing model have led to the establishment of large-scale virtualized data centers. Such data centers consume enormous amounts of electrical energy resulting in high operating costs and carbon dioxide emissions. Dynamic consolidation of virtual machines (VMs) using live migration and switching idle nodes to the sleep mode allow Cloud providers to optimize resource usage and reduce energy consumption. However, the obligation of providing high quality of service to customers leads to the necessity in dealing with the energy-performance trade-off, as aggressive consolidation may lead to performance degradation. Due to the variability of workloads experienced by modern applications, the VM placement should be optimized continuously in an online manner. To understand the implications of the online nature of the problem, we conduct competitive analysis and prove competitive ratios of optimal online deterministic algorithms for the single VM migration and dynamic VM consolidation problems. Furthermore, we propose novel adaptive heuristics for dynamic consolidation of VMs based on an analysis of historical data from the resource usage by VMs. The proposed algorithms significantly reduce energy consumption, while ensuring a high level of adherence to the Service Level Agreements (SLA). We validate the high efficiency of the proposed algorithms by extensive simulations using real-world workload traces from more than a thousand
Statistical Profiling-based Techniques for Effective Power Provisioning in Data Centers
"... Abstract: Current capacity planning practices based on heavy over-provisioning of power infrastructure hurt (i) the operational costs of data centers as well as (ii) the computational work they can support. We explore a combination of statistical multiplexing techniques to improve the utilization of ..."
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Cited by 50 (6 self)
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Abstract: Current capacity planning practices based on heavy over-provisioning of power infrastructure hurt (i) the operational costs of data centers as well as (ii) the computational work they can support. We explore a combination of statistical multiplexing techniques to improve the utilization of the power hierarchy within a data center. At the highest level of the power hierarchy, we employ controlled underprovisioning and over-booking of power needs of hosted workloads. At the lower levels, we introduce the novel notion of soft fuses to flexibly distribute provisioned power among hosted workloads based on their needs. Our techniques are built upon a measurement-driven profiling and prediction framework to characterize key statistical properties of the power needs of hosted workloads and their aggregates. We characterize the gains in terms of the amount of computational work (CPU cycles) per provisioned unit of power – Computation per Provisioned Watt (CPW). Our technique is able to double the CPW offered by a Power Distribution Unit (PDU) running the e-commerce benchmark TPC-W compared to conventional provisioning practices. Over-booking the PDU by 10 % based on tails of power profiles yields a further improvement of 20%. Reactive techniques implemented on our Xen VMM-based servers dynamically modulate CPU DVFS states to ensure power draw below the limits imposed by soft fuses. Finally, information captured in our profiles also provide ways of controlling application performance degradation despite overbooking. The 95 th percentile of TPC-W session response time only grew from 1.59 sec to 1.78 sec—a degradation of 12%.
Joint Optimization of Idle and Cooling Power in Data Centers While Maintaining Response Time
"... Server power and cooling power amount to a significant fraction of modern data centers ’ recurring costs. While data centers provision enough servers to guarantee response times under the maximum loading, data centers operate under much less loading most of the times (e.g., 30-70 % of the maximum lo ..."
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Cited by 48 (0 self)
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Server power and cooling power amount to a significant fraction of modern data centers ’ recurring costs. While data centers provision enough servers to guarantee response times under the maximum loading, data centers operate under much less loading most of the times (e.g., 30-70 % of the maximum loading). Previous serverpower proposals exploit this under-utilization to reduce the server idle power by keeping active only as many servers as necessary and putting the rest into low-power standby modes. However, these proposals incur higher cooling power due to hot spots created by concentrating the data center loading on fewer active servers, or degrade response times due to standby-to-active transition delays, or both. Other proposals optimize the cooling power but incur considerable idle power. To address the first issue of power, we propose PowerTrade, which trades-off idle power and cooling power for each other, thereby reducing the total power. To address the second issue of response time, we propose SurgeGuard to overprovision the number of active servers beyond that needed by the current loading so as to absorb future increases in the loading. SurgeGuard is a two-tier scheme which uses well-known over-provisioning at coarse time granularities (e.g., one hour) to absorb the common, smooth increases in the loading, and a novel fine-grain replenishment of the over-provisioned reserves at fine time granularities (e.g., five minutes) to handle the uncommon, abrupt loading surges. Using real-world traces, we show that combining Power-Trade and SurgeGuard reduces total power by 30 % compared to previous low-power schemes while maintaining response times within 1.7%.
Temperature-constrained power control for chip multiprocessors with online model estimation
- in ISCA
, 2009
"... As chip multiprocessors (CMPs) become the main trend in processor development, various power and thermal management strategies have recently been proposed to optimize system performance while controlling the power or temperature of a CMP chip to stay below a constraint. The availability of per-core ..."
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Cited by 47 (19 self)
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As chip multiprocessors (CMPs) become the main trend in processor development, various power and thermal management strategies have recently been proposed to optimize system performance while controlling the power or temperature of a CMP chip to stay below a constraint. The availability of per-core DVFS (dynamic voltage and frequency scaling) also makes it possible to develop advanced management strategies. However, most existing solutions rely on open-loop search or optimization with the assumption that power can be estimated accurately, while others adopt oversimplified feedback control strategies to control power and temperature separately, without any theoretical guarantees. In this paper, we propose a chip-level power control algorithm that is systematically designed based on optimal control theory. Our algorithm can precisely control the power of a CMP chip to the desired set point while maintaining the temperature of each core below a specified threshold. Furthermore, an online model estimator is designed to achieve analytical assurance of control accuracy and system stability, even in the face of significant workload variations or unpredictable chip or core variations. Empirical results on a physical testbed show that our controller outperforms two state-of-the-art control algorithms by having better SPEC benchmark performance and more precise power control. In addition, extensive simulation results demonstrate the efficacy of our algorithm for various CMP configurations.
Web search using mobile cores: quantifying and mitigating the price of efficiency
- in ISCA ’10
"... The commoditization of hardware, data center economies of scale, and Internet-scale workload growth all demand greater power efficiency to sustain scalability. Traditional enterprise workloads, which are typically memory and I/O bound, have been well served by chip multiprocessors com-prising of sma ..."
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Cited by 46 (2 self)
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The commoditization of hardware, data center economies of scale, and Internet-scale workload growth all demand greater power efficiency to sustain scalability. Traditional enterprise workloads, which are typically memory and I/O bound, have been well served by chip multiprocessors com-prising of small, power-efficient cores. Recent advances in mobile computing have led to modern small cores capable of delivering even better power efficiency. While these cores can deliver performance-per-Watt efficiency for data center workloads, small cores impact application quality-of-service robustness, and flexibility, as these workloads increasingly invoke computationally intensive kernels. These challenges constitute the price of efficiency. We quantify efficiency for an industry-strength online web search engine in production at both the microarchitecture- and system-level, evaluating search on server and mobile-class architectures using Xeon and Atom processors.
Multi-mode Energy Management for Multi-tier Server Clusters
"... This paper presents an energy management policy for reconfigurable clusters running a multi-tier application, exploiting DVS together with multiple sleep states. We develop a theoretical analysis of the corresponding power optimization problem and design an algorithm around the solution. Moreover, w ..."
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Cited by 44 (0 self)
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This paper presents an energy management policy for reconfigurable clusters running a multi-tier application, exploiting DVS together with multiple sleep states. We develop a theoretical analysis of the corresponding power optimization problem and design an algorithm around the solution. Moreover, we rigorously investigate selection of the optimal number of spare servers for each power state, a problem that has only been approached in an ad-hoc manner in current policies. To validate our results and policies, we implement them on an actual multi-tier server cluster where nodes support all power management techniques considered. Experimental results using realistic dynamic workloads based on the TPC-W benchmark show that exploiting multiple sleep states results in significant additional cluster-wide energy savings up to 23 % with little or no performance degradation.
