As energy proportional computing gradually extends the success of DVFS (Dynamic voltage and frequency scaling) to the entire system, DVFS control algorithms will play a key role in reducing server clusters' power consumption. The focus of this paper is to provide accurate cluster-level DVFS control for power saving in a server cluster. To achieve this goal, we propose a request tracing approach that online classifies the major causal path patterns of a multi-tier service and monitors their performance data as a guide for accurate DVFS control. The request tracing approach significantly decreases the time cost of performance profiling experiments that aim to establish the empirical performance model. Moreover, it decreases the controller complexity so that we can introduce a much simpler feedback controller, which only relies on the single-node DVFS modulation at a time as opposed to varying multiple CPU frequencies simultaneously. Based on the request tracing approach, we present a hybrid DVFS control system that combines an empirical performance model for fast modulation at different load levels and a simpler feedback controller for adaption. We implement a prototype of the proposed system, called PowerTracer, and conduct extensive experiments on a 3-tier platform. Our experimental results show that PowerTracer outperforms its peer in terms of power saving and system performance.
Related work focuses on the performance optimization problem , while our work addresses the power optimization problem under performance constraints @cite_24 . P. @cite_2 proposes power efficiencies at a larger scale by leveraging statistical properties of concurrent resource usage across a collection of systems (ensemble). C. @cite_13 present a technique that controls the peak power consumption of a high-density server . X. @cite_22 @cite_32 propose a cluster-level power controller that shifts power among servers based on their performance needs, while controlling the total power of the cluster to be lower than a constraint. S. @cite_10 develop mechanisms to better utilize installed power infrastructure. S. @cite_25 explore a combination of statistical multiplexing techniques to improve the utilization of the power hierarchy within a data center. R. @cite_4 propose and validate a power management solution that coordinates different individual energy-saving approaches. X. @cite_17 present the aggregate power usage characteristics of large collections of servers (up to 15 thousand) for different classes of applications over a period of approximately six months, and modeling to attack data center-level power provisioning inefficiencies.
@cite_4 @cite_22 @cite_10 @cite_32 @cite_24 @cite_2 @cite_13 @cite_25 @cite_17
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Power delivery, electricity consumption, and heat management are becoming key challenges in data center environments. Several past solutions have individually evaluated different techniques to address separate aspects of this problem, in hardware and software, and at local and global levels. Unfortunately, there has been no corresponding work on coordinating all these solutions. In the absence of such coordination, these solutions are likely to interfere with one another, in unpredictable (and potentially dangerous) ways. This paper seeks to address this problem. We make two key contributions. First, we propose and validate a power management solution that coordinates different individual approaches. Using simulations based on 180 server traces from nine different real-world enterprises, we demonstrate the correctness, stability, and efficiency advantages of our solution. Second, using our unified architecture as the base, we perform a detailed quantitative sensitivity analysis and draw conclusions about the impact of different architectures, implementations, workloads, and system design choices. Power control is becoming a key challenge for effectively operating a modern data center. In addition to reducing operation costs, precisely controlling power consumption is an essential way to avoid system failures caused by power capacity overload or overheating due to increasing high-density. Control-theoretic techniques have recently shown a lot of promise on power management thanks to their better control performance and theoretical guarantees on control accuracy and system stability. However, existing work over-simplifies the problem by controlling a single server independently from others. As a result, at the cluster level where multiple servers are correlated by common workloads and share common power supplies, power cannot be shared to improve application performance. In this paper, we propose a cluster-level power controller that shifts power among servers based on their performance needs, while controlling the total power of the cluster to be lower than a constraint. Our controller features a rigorous design based on an optimal multi-input-multi-output control theory. Empirical results demonstrate that our controller outperforms two state-of-the-art controllers, by having better application performance and more accurate power control. 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. 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. 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 CPWoffered 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 95th percentile of TPC-W session response time only grew from 1.59 sec to 1.78 sec--a degradation of 12 . Large-scale Internet services require a computing infrastructure that can beappropriately described as a warehouse-sized computing system. The cost ofbuilding datacenter facilities capable of delivering a given power capacity tosuch a computer can rival the recurring energy consumption costs themselves.Therefore, there are strong economic incentives to operate facilities as closeas possible to maximum capacity, so that the non-recurring facility costs canbe best amortized. That is difficult to achieve in practice because ofuncertainties in equipment power ratings and because power consumption tends tovary significantly with the actual computing activity. Effective powerprovisioning strategies are needed to determine how much computing equipmentcan be safely and efficiently hosted within a given power budget. In this paper we present the aggregate power usage characteristics of largecollections of servers (up to 15 thousand) for different classes ofapplications over a period of approximately six months. Those observationsallow us to evaluate opportunities for maximizing the use of the deployed powercapacity of datacenters, and assess the risks of over-subscribing it. We findthat even in well-tuned applications there is a noticeable gap (7 - 16 )between achieved and theoretical aggregate peak power usage at the clusterlevel (thousands of servers). The gap grows to almost 40 in wholedatacenters. This headroom can be used to deploy additional compute equipmentwithin the same power budget with minimal risk of exceeding it. We use ourmodeling framework to estimate the potential of power management schemes toreduce peak power and energy usage. We find that the opportunities for powerand energy savings are significant, but greater at the cluster-level (thousandsof servers) than at the rack-level (tens). Finally we argue that systems needto be power efficient across the activity range, and not only at peakperformance levels.