In this paper, we study energy conservation techniques for disk array-based network servers. First, we introduce a new conservation technique, called Popular Data Concentration (PDC), that migrates frequently accessed data to a subset of the disks. The goal is to skew the load towards a few of the disks, so that others can be transitioned to low-power modes. Next, we introduce a user-level file server that takes advantage of PDC. In the context of this server, we compare PDC to the Massive Array of Idle Disks (MAID). Using a validated simulator, we evaluate these techniques for conventional and two-speed disks and a wide range of parameters. Our results for conventional disks show that PDC and MAID can only conserve energy when the load on the server is extremely low. When two-speed disks are used, both PDC and MAID can conserve significant energy with only a small fraction of delayed requests. Overall, we find that PDC achieves more consistent and robust energy savings than MAID.

In this paper we study four approaches to conserving disk energy in high-performance network servers. The first approach is to leverage the extensive work on laptop disks and power disks down during periods of idleness. The second approach is to replace highperformance disks with a set of lower power disks that can achieve the same performance and reliability. The third approach is to combine high-performance and laptop disks, such that only one of these two sets of disks is powered on at a time. This approach requires the mirroring (and coherence) of all disk data on the two sets of disks. Finally, the fourth approach is to use multi-speed disks, such that each disk is slowed down for lower energy consumption during periods of light load. We demonstrate that the fourth approach is the only one that can actually provide energy savings for network servers. In fact, our results for Web and proxy servers show that the fourth approach can provide energy savings of up to 23%, in comparison to conventional servers, without any degradation in server performance.

The growing cost of tuning and managing computer systems is leading to out-sourcing of commercial services to hosting centers. These centers provision thousands of dense servers within a relatively small real-estate in order to host the applications/services of different customers who may have been assured by a service-level agreement (SLA). Power consumption of these servers is becoming a serious concern in the design and operation of the hosting centers. The effects of high power consumption manifest not only in the costs spent in designing effective cooling systems to ward off the generated heat, but in the cost of electricity consumption itself. It is crucial to deploy power management strategies in these hosting centers to lower these costs towards enhancing profitability. At the same time, techniques for power management that include shutting down these servers and/or modulating their operational speed, can impact the ability of the hosting center to meet SLAs. In addition, repeated on-off cycles can increase the wear-and-tear of server components, incurring costs for their procurement and replacement. This paper presents a formalism to this problem, and proposes three new online solution strategies based on steady state queuing analysis, feedback control theory, and a hybrid mechanism borrowing ideas from these two. Using real web server traces, we show that these solutions are more adaptive to workload behavior when performing server provisioning and speed control than earlier heuristics towards minimizing operational costs while meeting the SLAs.

Power and energy consumption are key concerns for Internet data centers. These centers house hundreds, sometimes thousands, of servers and supporting cooling infrastructures. Research on power and energy management for servers can ease data center installation, reduce costs, and protect the environment. Given these benefits, researchers have made important strides in conserving energy in servers. Inspired by this initial progress, researchers are delving deeper into this topic. In this paper, we detail the motivation for this research, survey the previous work, describe a few ongoing efforts, and discuss the challenges that lie ahead. 1

Power-performance optimization is a relatively new problem area particularly in the context of server clusters. Poweraware request distribution is a method of scheduling service requests among servers in a cluster so that energy consumption is minimized, while maintaining a particular level of performance. Energy efficiency is obtained by poweringdown some servers when the desired quality of service can be met with fewer servers. We have found that it is critical to take into account the system and workload factors during both the design and the evaluation of such request distribution schemes. We identify the key system and workload factors that impact such policies and their effectiveness in saving energy. We measure a web cluster running an industrystandard commercial web workload to demonstrate that understanding this system-workload context is critical to performing valid evaluations and even for improving the energysaving schemes.

Energy consumption in hosting Internet services is becoming a pressing issue as these services scale up. Dynamic server provisioning techniques are effective in turning off unnecessary servers to save energy. Such techniques, mostly studied for request-response services, face challenges in the context of connection servers that host a large number of long-lived TCP connections. In this paper, we characterize unique properties, performance, and power models of connection servers, based on a real data trace collected from the deployed Windows Live Messenger. Using the models, we design server provisioning and load dispatching algorithms and study subtle interactions between them. We show that our algorithms can save a significant amount of energy without sacrificing user experiences. 1

The previous research on cluster-based servers has focused on homogeneous systems. However, real-life clusters are almost invariably heterogeneous in terms of the performance, capacity, and power consumption of their hardware components. In this paper, we argue that designing efficient servers for heterogeneous clusters requires defining an efficiency metric, modeling the different types of nodes with respect to the metric, and searching for request distributions that optimize the metric. To concretely illustrate this process, we design a cooperative Web server for a heterogeneous cluster that uses modeling and optimization to minimize the energy consumed per request. Our experimental results for a cluster comprised of traditional and blade nodes show that our server can consume 42 % less energy than an energyoblivious server, with only a negligible loss in throughput. The results also show that our server conserves 45 % more energy than an energy-conscious server that was previously proposed for homogeneous clusters.

Abstract — Recent advances have demonstrated the potential benefits of coordinated management of thermal load in data centers, including reduced cooling costs and improved resistance to cooling system failures. A key unresolved obstacle to the practical implementation of thermal load management is the ability to predict the effects of workload distribution and cooling configurations on temperatures within a data center enclosure. The interactions between workload, cooling, and temperature are dependent on complex factors that are unique to each data center, including physical room layout, hardware power consumption, and cooling capacity; this dictates an approach that formulates management policies for each data center based on these properties. We propose and evaluate a simple, flexible method to infer a detailed model of thermal behavior within a data center from a stream of instrumentation data. This data — taken during normal data center operation — includes continuous readings taken from external temperature sensors, server instrumentation, and computer room air conditioning units. Experimental results from a representative data center show that automatic thermal mapping can predict accurately the heat distribution resulting from a given workload distribution and cooling configuration, thereby removing the need for static or manual configuration of thermal load management systems. We also demonstrate how our approach adapts to preserve accuracy across changes to cluster attributes that affect thermal behavior — such as cooling settings, workload distribution, and power consumption. I.

With increasing costs of energy consumption and cooling, power management in server clusters has become an increasingly important design issue. Current clusters for real-time applications are designed to handle peak loads, where all servers are fully utilized. In practice, peak load conditions rarely happen and clusters are most of the time underutilized. This creates the opportunity for using slower frequencies, and thus smaller energy consumption, with little or no impact on the Quality of Service (QoS), for example, performance and timeliness. In this work we present a cluster-wide QoS-aware technique that dynamically reconfigures the cluster to reduce energy consumption during periods of reduced load. Moreover, we also investigate the effects of local QoS-aware power management using Dynamic Voltage Scaling (DVS). Since most real-world clusters consist of machines of different kind (in terms of both performance and energy consumption) we focus on heterogeneous clusters. For validation, we describe and evaluate an implementation of the proposed scheme using the Apache Webserver in a small realistic cluster. Our experimental results show that using our scheme it is possible to save up to 45 % of the total energy consumed by the servers, maintaining average response times within the specified deadlines and number of dropped requests within the required amount. 1

Abstract — Energy and cooling costs of web server farms are among their main financial expenditures. This paper explores the benefits of dynamic voltage scaling (DVS) for power management in server farms. Unlike previous work, which addressed DVS on individual servers and on load-balanced server replicas, this paper addresses DVS in multi-stage service pipelines. Contemporary Web server installations typically adopt a three-tier architecture in which the first tier presents a Web interface, the second executes scripts that implement business logic, and the third serves database accesses. From a user’s perspective, only the end-to-end response across the entire pipeline is relevant. This paper presents a rigorous optimization methodology and an algorithm for minimizing the total energy expenditure of the multi-stage pipeline subject to soft end-to-end responsetime constraints. A distributed power management service is designed and evaluated on a real three-tier server prototype for coordinating DVS settings in a way that minimizes global energy consumption while meeting end-to-end delay constraints. The service is shown to consume as much as 30 % less energy compared to the default (Linux) energy saving policy.