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 closest work to this paper is @cite_27 by They proposed a coordinated distributed DVS policy based on a feedback controller for three-tier web server systems. However, their work fails to provide accurate DVFS control for two reasons: first, the simple DVS algorithm uses CPU utilization as the indicator in determining which server's clock frequency should be scaled, while the optimized algorithm is difficult to be applied because of its complexity; second, the two algorithms take the average server-side latency of all requests as the controlled variable, while our experiments show that massive requests have a number of different patterns. In @cite_24 , proposed a multi-mode energy management for multi-tier server clusters, which exploited DVS together with multiple sleep states. In @cite_7 , invented a service prioritization scheme for multi-tier web server clusters, which assigned different priorities based on their performance requirements. In @cite_6 , developed a simple metric called that can predict the impact of changes in processor frequency upon the end-to-end transaction response times of multi-tier applications.
@cite_24 @cite_27 @cite_6 @cite_7
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Energy management for servers is now necessary for technical, financial, and environmental reasons. This paper describes three policies designed to reduce energy consumption in Web servers. The policies employ two power management mechanisms: dynamic voltage scaling (DVS), an existing mechanism, and request batching, a new mechanism introduced in this paper. The first policy uses DVS in isolation, except that we extend recently introduced task-based DVS policies for use in server environments with many concurrent tasks. The second policy uses request batching to conserve energy during periods of low workload intensity. The third policy uses both DVS and request batching mechanisms to reduce processor energy usage over a wide range of workload intensities. All the policies trade off system responsiveness to save energy. However, the policies employ the mechanisms in a feedback-driven control framework in order to conserve energy while maintaining a given quality of service level, as defined by a percentile-level response time. We evaluate the policies using Salsa, a web server simulator that has been extensively validated for both energy and response time against measurements from a commodity web server. Three daylong static web workloads from real web server systems are used to quantify the energy savings: the Nagano Olympics98 web server, a financial services company web site, and a disk intensive web workload. Our results show that when required to maintain a 90th-percentile response time of 50ms, the DVS and request batching policies save from 8.7 to 38 and from 3.1 to 27 respectively of the CPU energy used by the base system. The two polices provide these savings for complementary workload intensities. The combined policy is effective for all three workloads across a broad range of intensities, saving from 17 to 42 of the CPU energy. We consider the problem of resource allocation in downlink OFDMA systems for multi service and unknown environment. Due to users' mobility and intercell interference, the base station cannot predict neither the Signal to Noise Ratio (SNR) of each user in future time slots nor their probability distribution functions. In addition, the traffic is bursty in general with unknown arrival. The probability distribution functions of the SNR, channel state and traffic arrival density are then unknown. Achieving a multi service Quality of Service (QoS) while optimizing the performance of the system (e.g. total throughput) is a hard and interesting task since it depends on the unknown future traffic and SNR values. In this paper we solve this problem by modeling the multiuser queuing system as a discrete time linear dynamic system. We develop a robust H∞ controller to regulate the queues of different users. The queues and Packet Drop Rates (PDR) are controlled by proposing a minimum data rate according to the demanded service type of each user. The data rate vector proposed by the controller is then fed as a constraint to an instantaneous resource allocation framework. This instantaneous problem is formulated as a convex optimization problem for instantaneous subcarrier and power allocation decisions. Simulation results show small delays and better fairness among users. 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 Many power management strategies have been proposed for enterprise servers based on dynamic voltage and frequency scaling (DVFS), but those solutions cannot further reduce the energy consumption of a server when the server processor is already at the lowest DVFS level and the server utilization is still low (e.g., 5 or lower). To achieve improved energy efficiency, request batching can be conducted to group received requests into batches and put the processor into sleep between the batches. However, it is challenging to perform request batching on a virtualized server because different virtual machines on the same server may have different workload intensities. Hence, putting the shared processor into sleep may severely impact the performance of all the virtual machines. This paper proposes Virtual Batching, a novel request batching solution for virtualized servers with primarily light workloads. Our solution dynamically allocates CPU resources such that all the virtual machines can have approximately the same performance level relative to their allowed peak values. Based on this uniform level, our solution determines the time length for periodically batching incoming requests and putting the processor into sleep. When the workload intensity changes from light to moderate, request batching is automatically switched to DVFS to increase processor frequency for performance guarantees. Empirical results based on a hardware testbed and real trace files show that Virtual Batching can achieve the desired performance with more energy conservation than several well-designed baselines, e.g., 63 more, on average, than a solution based on DVFS only.