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.
 The 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 multistage 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 multistage pipeline subject to soft end-to-end response-time 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 percent less energy compared to the default (Linux) energy saving policy Dynamic voltage and frequency scaling (DVFS) is a well-known technique for gaining energy savings on desktop and laptop computers. However, its use in server settings requires careful consideration of any potential impacts on end-to-end service performance of hosted applications. In this paper, we develop a simple metric called the gradient" that allows prediction of the impact of changes in processor frequency on the end-to-end transaction response times of multitier applications. We show how frequency gradients can be measured on a running system in a push-button manner without any prior knowledge of application semantics, structure, or configuration settings. Using experimental results, we demonstrate that the frequency gradients provide accurate predictions, and enable end-to-end performance-aware DVFS for mulitier applications. This paper investigates the design issues and energy savings benefits of service prioritization in multi-tier Web server clusters. In many services, classes of clients can be naturally assigned different priorities based on their performance requirements. We show that if the whole multi-tier system is effectively prioritized, additional power and energy savings are realizable while keeping an existing cluster-wide energy management technique, through exploiting the different performance requirements of separate service classes. We find a simple prioritization scheme to be highly effective without requiring intrusive modifications to the system. In order to quantify its benefits, we perform extensive experimental evaluation on a real testbed. It is shown that the scheme significantly improves both total system power savings and energy efficiency, at the same time as improving throughput and enabling the system to meet per-class performance requirements.