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44
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.
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%.
MemScale: Active Low-Power Modes for Main Memory ∗
"... Main memory is responsible for a large and increasing fraction of the energy consumed by servers. Prior work has focused on exploiting DRAM low-power states to conserve energy. However, these states require entire DRAM ranks to be idled, which is difficult to achieve even in lightly loaded servers. ..."
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Cited by 48 (4 self)
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Main memory is responsible for a large and increasing fraction of the energy consumed by servers. Prior work has focused on exploiting DRAM low-power states to conserve energy. However, these states require entire DRAM ranks to be idled, which is difficult to achieve even in lightly loaded servers. In this paper, we propose to conserve memory energy while improving its energyproportionality by creating active low-power modes for it. Specifically, we propose MemScale, a scheme wherein we apply dynamic voltage and frequency scaling (DVFS) to the memory controller and dynamic frequency scaling (DFS) to the memory channels and DRAM devices. MemScale is guided by an operating system policy that determines the DVFS/DFS mode of the memory subsystem based on the current need for memory bandwidth, the potential energy savings, and the performance degradation that applications are willing to withstand. Our results demonstrate that Mem-Scale reduces energy consumption significantly compared to modern memory energy management approaches. We conclude that the potential benefits of the MemScale mechanisms and policy more than compensate for their small hardware cost. C.5 [Computer System Im-
S.K.S.: Spatio-temporal thermal-aware job scheduling to minimize energy consumption in virtualized heterogeneous data centers (Elsevier
- Computer Networks, Special Issue on Resource Management in Heterogeneous Data Centers
"... Job scheduling in data centers can be considered from a cyber-physical point of view, as it affects the data center’s computing performance (i.e. the cyber aspect) and energy efficiency (the physical aspect). Driven by the growing needs to green contemporary data centers, this paper uses recent tech ..."
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Cited by 38 (9 self)
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Job scheduling in data centers can be considered from a cyber-physical point of view, as it affects the data center’s computing performance (i.e. the cyber aspect) and energy efficiency (the physical aspect). Driven by the growing needs to green contemporary data centers, this paper uses recent technological advances in data center virtualization and proposes cyber-physical, spatio-temporal (i.e. start time and servers assigned), thermal-aware job scheduling algorithms that minimize the energy consumption of the data center under performance constraints (i.e. deadlines). Savings are possible by being able to temporally “spread ” the workload, assign it to energy-efficient computing equipment, and further reduce the heat recirculation and therefore the load on the cooling systems. This paper provides three categories of thermal-aware energy-saving scheduling techniques: a) FCFS-Backfill-XInt and FCFS-Backfill-LRH, thermal-aware job placement enhancements to the popular first-come first-serve with back-filling (FCFSbackfill) scheduling policy; b) EDF-LRH, an online earliest-deadline-first scheduling algorithm with thermal-aware placement; and c) an offline genetic algorithm for SCheduling to minimize thermal cross-INTerference (SCINT), which is suited for batch scheduling of backlogs. Simulation results, based on real job logs from the ASU Fulton HPC data center, show that the thermal-aware enhancements to FCFS-backfill achieve up to 25 % savings compared to FCFS-backfill with first-fit placement, depending on the intensity of the incoming workload, while SCINT achieves up to 60 % savings. The performance of EDF-LRH nears that of the offline SCINT for low loads, and it degrades to the performance of FCFS-backfill for high loads. However, EDF-LRH requires milliseconds
Exploring Power-Performance Tradeoffs in Database Systems
"... Abstract — With the total energy consumption of computing systems increasing in a steep rate, much attention has been paid to the design of energy-efficient computing systems and applications. So far, database system design has focused on improving performance of query processing. The objective of t ..."
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Cited by 30 (7 self)
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Abstract — With the total energy consumption of computing systems increasing in a steep rate, much attention has been paid to the design of energy-efficient computing systems and applications. So far, database system design has focused on improving performance of query processing. The objective of this study is to experimentally explore the potential of power conservation in relational database management systems. We hypothesize that, by modifying the query optimizer in a DBMS to take the power cost of query plans into consideration, we will be able to reduce the power usage of database servers and control the tradeoffs between power consumption and system performance. We also identify the sources of such savings by investigating the resource consumption features during query processing in DBMSs. To that end, we provide an in-depth anatomy and qualitatively analyze the power profile of typical queries in the TPC benchmarks. We perform extensive experiments on a physical testbed based on the PostgreSQL system using workloads generated from the TPC benchmarks. Our hypothesis is supported by such experimental results: power savings in the range of 11 %- 22 % can be achieved by equipping the DBMS with a query optimizer that selects query plans based on both estimated processing time and power requirements. I.
Power management of datacenter workloads using per-core power gating
- Computer Architecture Letters
"... Abstract—While modern processors offer a wide spectrum of software-controlled power modes, most datacenters only rely on Dynamic Voltage and Frequency Scaling (DVFS, a.k.a. P-states) to achieve energy efficiency. This paper argues that, in the case of datacenter workloads, DVFS is not the only optio ..."
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Cited by 28 (0 self)
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Abstract—While modern processors offer a wide spectrum of software-controlled power modes, most datacenters only rely on Dynamic Voltage and Frequency Scaling (DVFS, a.k.a. P-states) to achieve energy efficiency. This paper argues that, in the case of datacenter workloads, DVFS is not the only option for processor power management. We make the case for per-core power gating (PCPG) as an additional power management knob for multi-core processors. PCPG is the ability to cut the voltage supply to selected cores, thus reducing to almost zero the leakage power for the gated cores. Using a testbed based on a commercial 4-core chip and a set of real-world application traces from enterprise environments, we have evaluated the potential of PCPG. We show that PCPG can significantly reduce a processor’s energy consumption (up to 40%) without significant performance overheads. When compared to DVFS, PCPG is highly effective saving up to 30 % more energy than DVFS. When DVFS and PCPG operate together they can save up to almost 60%. 1
Leveraging stored energy for handling power emergencies in aggressively provisioned datacenters
- In ACM ASPLOS
, 2012
"... All in-text references underlined in blue are linked to publications on ResearchGate, letting you access and read them immediately. ..."
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Cited by 24 (6 self)
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All in-text references underlined in blue are linked to publications on ResearchGate, letting you access and read them immediately.
Data center evolution: A tutorial on state of the art, issues,
- and challenges,” Computer Networks,
, 2009
"... Abstract Data centers form a key part of the infrastructure upon which a variety of information technology services are built. As data centers continue to grow in size and complexity, it is desirable to understand aspects of their design that are worthy of carrying forward, as well as existing or u ..."
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Cited by 22 (1 self)
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Abstract Data centers form a key part of the infrastructure upon which a variety of information technology services are built. As data centers continue to grow in size and complexity, it is desirable to understand aspects of their design that are worthy of carrying forward, as well as existing or upcoming shortcomings and challenges that would have to be addressed. We envision the data center evolving from owned physical entities to potentially outsourced, virtualized and geographically distributed infrastructures that still attempt to provide the same level of control and isolation that owned infrastructures do. We define a layered model for such data centers and provide a detailed treatment of state of the art and emerging challenges in storage, networking, management and power/thermal aspects.
Towards Thermal Aware Workload Scheduling in a Data Center
"... Abstract—High density blade servers are a popular technology for data centers, however, the heat dissipation density of data centers increases exponentially. There is strong evidence to support that high temperatures of such data centers will lead to higher hardware failure rates and thus an increas ..."
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Cited by 21 (2 self)
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Abstract—High density blade servers are a popular technology for data centers, however, the heat dissipation density of data centers increases exponentially. There is strong evidence to support that high temperatures of such data centers will lead to higher hardware failure rates and thus an increase in maintenance costs. Improperly designed or operated data centers may either suffer from overheated servers and potential system failures, or from overcooled systems, causing extraneous utilities cost. Minimizing the cost of operation (utilities, maintenance, device upgrade and replacement) of data centers is one of the key issues involved with both optimizing computing resources and maximizing business outcome. This paper proposes an analytical model, which describes data center resources with heat transfer properties and workloads with thermal features. Then a thermal aware task scheduling algorithm is presented which aims to reduce power consumption and temperatures in a data center. A simulation study is carried out to evaluate the performance of the algorithm. Simulation results show that our algorithm can significantly reduce temperatures in data centers by introducing endurable decline in performance.
