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42
Optimal Power Allocation in Server Farms
, 2009
"... Server farms today consume more than 1.5 % of the total electricity in the U.S. at a cost of nearly $4.5 billion. Given the rising cost of energy, many industries are now seeking solutions for how to best make use of their available power. An important question which arises in this context is how to ..."
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Cited by 102 (3 self)
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Server farms today consume more than 1.5 % of the total electricity in the U.S. at a cost of nearly $4.5 billion. Given the rising cost of energy, many industries are now seeking solutions for how to best make use of their available power. An important question which arises in this context is how to distribute available power among servers in a server farm so as to get maximum performance. By giving more power to a server, one can get higher server frequency (speed). Hence it is commonly believed that, for a given power budget, performance can be maximized by operating servers at their highest power levels. However, it is also conceivable that one might prefer to run servers at their lowest power levels, which allows more servers to be turned on for a given power budget. To fully understand the effect of power allocation on performance in a server farm with a fixed power budget, we introduce a queueing theoretic model, which allows us to predict the optimal power allocation in a variety of scenarios. Results are verified via extensive experiments on an IBM BladeCenter. We find that the optimal power allocation varies for different scenarios. In particular, it is not always optimal to run servers at their maximum power levels. There are scenarios where it might be optimal to run servers at their lowest power levels or at some intermediate power levels. Our analysis shows that the optimal power allocation is non-obvious and depends on many factors such as the power-to-frequency relationship in the processors, the arrival rate of jobs, the maximum server frequency, the lowest attainable server frequency and the server farm configuration. Furthermore, our theoretical model allows us to explore more general settings than we can implement, including arbitrarily large server farms and different power-to-frequency curves. Importantly, we show that the optimal power allocation can significantly
Delivering Energy Proportionality with Non Energy-Proportional Systems – Optimizing the Ensemble
"... With power having become a critical issue in the operation of data centers today, there has been an increased push towards the vision of “energy-proportional computing”, in which no power is used by idle systems, very low power is used by lightly loaded systems, and proportionately higher power at h ..."
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Cited by 61 (0 self)
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With power having become a critical issue in the operation of data centers today, there has been an increased push towards the vision of “energy-proportional computing”, in which no power is used by idle systems, very low power is used by lightly loaded systems, and proportionately higher power at higher loads. Unfortunately, given the state of the art of today’s hardware, designing individual servers that exhibit this property remains an open challenge. However, even in the absence of redesigned hardware, we demonstrate how optimization-based techniques can be used to build systems with off-the-shelf hardware that, when viewed at the aggregate level, approximate the behavior of energy-proportional systems. This paper explores the viability and tradeoffs of optimization-based approaches using two different case studies. First, we show how different power-saving mechanisms can be combined to deliver an aggregate system that is proportional in its use of server power. Second, we show early results on delivering a proportional cooling system for these servers. When compared to the power consumed at 100 % utilization, results from our testbed show that optimization-based systems can reduce the power consumed at 0 % utilization to 15 % for server power and 32 % for cooling power. 1
Dynamic knobs for responsive power-aware computing
- In Proceedings of the 16th International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS’11
, 2011
"... We present PowerDial, a system for dynamically adapting application behavior to execute successfully in the face of load and power fluctuations. PowerDial transforms static configuration parameters into dynamic knobs that the PowerDial control system can manipulate to dynamically trade off the accur ..."
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Cited by 55 (19 self)
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We present PowerDial, a system for dynamically adapting application behavior to execute successfully in the face of load and power fluctuations. PowerDial transforms static configuration parameters into dynamic knobs that the PowerDial control system can manipulate to dynamically trade off the accuracy of the computation in return for reductions in the computational resources that the application requires to produce its results. These reductions translate directly into performance improvements and power savings. Our experimental results show that PowerDial can enable our benchmark applications to execute responsively in the face of power caps that would otherwise significantly impair responsiveness. They also show that PowerDial can significantly reduce the number of machines required to service intermittent load spikes, enabling reductions in power and capital costs.
Power routing: dynamic power provisioning in the data center
- ACM SIGPLAN Notices
"... Data center power infrastructure incurs massive capital costs, which typically exceed energy costs over the life of the facility. To squeeze maximum value from the infrastructure, researchers have proposed over-subscribing power circuits, relying on the observation that peak loads are rare. To ensur ..."
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Cited by 32 (3 self)
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Data center power infrastructure incurs massive capital costs, which typically exceed energy costs over the life of the facility. To squeeze maximum value from the infrastructure, researchers have proposed over-subscribing power circuits, relying on the observation that peak loads are rare. To ensure availability, these proposals employ power capping, which throttles server performance during utilization spikes to enforce safe power budgets. However, because budgets must be enforced locally—at each power distribution unit (PDU)—local utilization spikes may force throttling even when power delivery capacity is available elsewhere. Moreover, the need to maintain reserve capacity for fault tolerance on power delivery paths magnifies the impact of utilization spikes. In this paper, we develop mechanisms to better utilize installed power infrastructure, reducing reserve capacity margins and avoiding performance throttling. Unlike conventional high-availability data centers, where collocated servers share identical primary and secondary power feeds, we reorganize power feeds to create shuffled power distribution topologies. Shuffled topologies spread secondary power feeds over numerous PDUs, reducing reserve capacity requirements to tolerate a single PDU failure. Second, we propose Power Routing, which schedules IT load dynamically across redundant power feeds to: (1) shift slack to servers with growing power demands, and (2) balance power draw across AC phases to reduce heating and improve electrical stability. We describe efficient heuristics for scheduling servers to PDUs (an NP-complete problem). Using data collected from nearly 1000 servers in three production facilities, we demonstrate that these mechanisms can reduce the required power infrastructure capacity relative to conventional high-availability data centers by 32 % without performance degradation.
SHIP: Scalable Hierarchical Power Control for Large-Scale Data Centers
"... In today’s data centers, precisely controlling server power consumption is an essential way to avoid system failures caused by power capacity overload or overheating due to increasingly high server density. While various power control strategies have been recently proposed, existing solutions are no ..."
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Cited by 28 (11 self)
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In today’s data centers, precisely controlling server power consumption is an essential way to avoid system failures caused by power capacity overload or overheating due to increasingly high server density. While various power control strategies have been recently proposed, existing solutions are not scalable to control the power consumption of an entire large-scale data center, because these solutions are designed only for a single server or a rack enclosure. In a modern data center, however, power control needs to be enforced at three levels: rack enclosure, power distribution unit, and the entire data center, due to the physical and contractual power limits at each level. This paper presents SHIP, a highly scalable hierarchical power control architecture for large-scale data centers. SHIP is designed based on well-established control theory for analytical assurance of control accuracy and system stability. Empirical results on a physical testbed show that our control solution can provide precise power control, as well as power differentiations for optimized system performance. In addition, our extensive simulation results based on a real trace file demonstrate the efficacy of our control solution in large-scale data centers composed of thousands of servers.
Co-con: Coordinated control of power and application performance for virtualized server clusters
, 2008
"... Abstract — Today’s data centers face two critical challenges. First, various customers need to be assured by meeting their required service-level agreements such as response time and throughput. Second, server power consumption must be controlled in order to avoid failures caused by power capacity o ..."
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Cited by 26 (5 self)
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Abstract — Today’s data centers face two critical challenges. First, various customers need to be assured by meeting their required service-level agreements such as response time and throughput. Second, server power consumption must be controlled in order to avoid failures caused by power capacity overload or system overheating due to increasing high server density. However, existing work controls power and applicationlevel performance separately and thus cannot simultaneously provide explicit guarantees on both. This paper proposes Co-Con, a novel cluster-level control architecture that coordinates individual power and performance control loops for virtualized server clusters. To emulate the current practice in data centers, the power control loop changes hardware power states with no regard to the application-level performance. The performance control loop is then designed for each virtual machine to achieve the desired performance even when the system model varies significantly due to the impact of power control. Co-Con configures the two control loops rigorously, based on feedback control theory, for theoretically guaranteed control accuracy and system stability. Empirical results demonstrate that Co-Con can simultaneously provide effective control on both application-level performance and underlying power consumption. I.
Coordinating power control and performance management for virtualized server clusters
- IEEE TRANS. PARALLEL DISTRIB. SYST
, 2011
"... Today’s data centers face two critical challenges. First, various customers need to be assured by meeting their required service-level agreements such as response time and throughput. Second, server power consumption must be controlled in order to avoid failures caused by power capacity overload or ..."
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Cited by 24 (0 self)
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Today’s data centers face two critical challenges. First, various customers need to be assured by meeting their required service-level agreements such as response time and throughput. Second, server power consumption must be controlled in order to avoid failures caused by power capacity overload or system overheating due to increasing high server density. However, existing work controls power and application-level performance separately, and thus, cannot simultaneously provide explicit guarantees on both. In addition, as power and performance control strategies may come from different hardware/software vendors and coexist at different layers, it is more feasible to coordinate various strategies to achieve the desired control objectives than relying on a single centralized control strategy. This paper proposes Co-Con, a novel cluster-level control architecture that coordinates individual power and performance control loops for virtualized server clusters. To emulate the current practice in data centers, the power control loop changes hardware power states with no regard to the application-level performance. The performance control loop is then designed for each virtual machine to achieve the desired performance even when the system model varies significantly due to the impact of power control. Co-Con configures the two control loops rigorously, based on feedback control theory, for theoretically guaranteed control accuracy and system stability. Empirical results on a physical testbed demonstrate that Co-Con can simultaneously provide effective control on both application-level performance and underlying power consumption.
A cyber-physical systems approach to energy management in data centers
- in Proceedings of the 1st ACM/IEEE International Con- 143 ference on Cyber-Physical Systems, ser. ICCPS ’10
"... This paper presents a new control strategy for data centers that aims to optimize the trade-off between maximizing the payoff from the provided quality of computational services and minimizing energy costs for computation and cooling. The data center is modeled as two interacting dynamic networks: a ..."
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Cited by 17 (3 self)
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This paper presents a new control strategy for data centers that aims to optimize the trade-off between maximizing the payoff from the provided quality of computational services and minimizing energy costs for computation and cooling. The data center is modeled as two interacting dynamic networks: a computational (cyber) network representing the distribution and flow of computational tasks, and a thermal (physical) network characterizing the distribution and flow of thermal energy. To make the problem tractable, the control architecture is decomposed hierarchically according to time-scales in the thermal and computational network dynamics, and spatially, reflecting weak coupling between zones in the data center. Simulation results demonstrate the effectiveness of the proposed coordinated control strategy relative to traditional approaches in which the cyber and physical resources are controlled independently.
BigHouse: A simulation infrastructure for data center systems
"... Recently, there has been an explosive growth in Internet services, greatly increasing the importance of data center systems. Applications served from “the cloud ” are driving data center growth and quickly overtaking traditional workstations. Although there are a many tools for evaluating components ..."
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Cited by 15 (2 self)
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Recently, there has been an explosive growth in Internet services, greatly increasing the importance of data center systems. Applications served from “the cloud ” are driving data center growth and quickly overtaking traditional workstations. Although there are a many tools for evaluating components of desktop and server architectures in detail, scalable modeling tools are noticeably missing. We describe BigHouse a simulation infrastructure for data center systems. Instead of simulating servers using detailed microarchitectural models, BigHouse raises the level of abstraction. Using a combination of queuing theory and stochastic modeling, BigHouse can simulate server systems in minutes rather than hours. BigHouse leverages statistical simulation techniques to limit simulation turnaround time to the minimum runtime needed for a desired accuracy. In this paper, we introduce BigHouse, describe its design, and present case studies for how it has already been applied to build and validate models of data center workloads and systems. Furthermore, we describe statistical techniques incorporated into BigHouse to accelerate and parallelize its simulations, and demonstrate its scalability to model large cluster systems while maintaining reasonable simulation time. 1.
MIMO Power Control for High-Density Servers in an Enclosure
- IEEE Transactions on Parallel and Distributed Systems
, 2010
"... Abstract—Power control is becoming a key challenge for effectively operating a modern data center. In addition to reducing operating costs, precisely controlling power consumption is an essential way to avoid system failures caused by power capacity overload or overheating due to increasing high ser ..."
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Cited by 15 (3 self)
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Abstract—Power control is becoming a key challenge for effectively operating a modern data center. In addition to reducing operating costs, precisely controlling power consumption is an essential way to avoid system failures caused by power capacity overload or overheating due to increasing high server density. Control-theoretic techniques have recently shown a lot of promise for power management because of their better control performance and theoretical guarantees on control accuracy and system stability. However, existing work oversimplifies the problem by controlling a single server independently from others. As a result, at the enclosure level where multiple high-density servers are correlated by common workloads and share common power supplies, power cannot be shared to improve application performance. In this paper, we propose an enclosure-level power controller that shifts power among servers based on their performance needs, while controlling the total power of the enclosure to be lower than a constraint. Our controller features a rigorous design based on an optimal Multi-Input-Multi-Output (MIMO) control theory. We present detailed control problem formulation and transformation to a standard constrained least-squares problem, as well as stability analysis in the face of significant workload variations. We then conduct extensive experiments on a physical testbed to compare our controller with three state-of-the-art controllers: a heuristic-based MIMO control solution, a Single-Input-Single-Output (SISO) control solution, and an improved SISO controller with simple power shifting among servers. Our empirical results demonstrate that our controller outperforms all the three baselines by having more accurate power control and up to 11.8 percent better benchmark performance. Index Terms—Power control, power capping, power management, servers, data centers, feedback control. Ç 1
