A Gray-Box Feedback Control Approach for System-Level Peak Power Management

Gong, Jiayu; Xu, Cheng‐Zhong

doi:10.1109/icpp.2010.63

Cited by 12 publications

(7 citation statements)

References 32 publications

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“…Sharp fluctuations may be responded more than one control period if several step modulations are needed. Gong and Xu [32] proposed a responsive graybox controller, which is helpful for our further work, but they focused on responsiveness and performance degradation rather than power consumption. Besides, the frequency fluctuation also becomes an issue when it comes to fine-grained DVFS.…”

Section: Discussionmentioning

confidence: 99%

PowerTracer: Tracing Requests in Multi-Tier Services to Reduce Energy Inefficiency

Lü

Zhan

Wang

et al. 2015

IEEE Trans. Comput.

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As energy has become one of the key operating costs in running a data center and power waste commonly exists, it is essential to reduce energy inefficiency inside data centers. In this paper, we develop an innovative framework, called PowerTracer, for diagnosing energy inefficiency and saving power. Inside the framework, we first present a resource tracing method based on request tracing in multi-tier services of black boxes. Then, we propose a generalized methodology of applying a request tracing approach for energy inefficiency diagnosis and power saving in multi-tier service systems. With insights into service performance and resource consumption of individual requests, we develop (1) a bottleneck diagnosis tool that pinpoints the root causes of energy inefficiency, and (2) a power saving method that enables dynamic voltage and frequency scaling (DVFS) with online request tracing. We implement a prototype of PowerTracer, and conduct extensive experiments to validate its effectiveness. Our tool analyzes several state-of-thepractice and state-of-the-art DVFS control policies and uncovers existing energy inefficiencies. Meanwhile, the experimental results demonstrate that PowerTracer outperforms its peers in power saving.

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Section: Discussionmentioning

confidence: 99%

PowerTracer: Tracing Requests in Multi-Tier Services to Reduce Energy Inefficiency

Lü

Zhan

Wang

et al. 2015

IEEE Trans. Comput.

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“…The savings achieved by the algorithm presented in [7] are attained mostly by exploiting identified dynamics of applications running, and thus designing a control law that makes it possible to adequately react to load changes. A discussion concerned with the possibility of designing PI and PID processor frequency controllers may be also found in [18]. Similar mechanisms may be applied to control network devices, either software (Linux)-based routers implemented on general grade PC-class machines [19], [20] or specialized network devices -see, for instance, [9].…”

Section: Local Cpu Power Controlmentioning

confidence: 99%

Energy Aware Data Centers and Networks: a Survey

Arabas

2018

JTIT

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The past years have brought about a great variety of clusters and clouds. This, combined with their increasing size and complexity, has resulted in an obvious need for power-saving control mechanisms. Upon presenting a basis on which such solutions - namely low-level power control interfaces, CPU governors and network topologies – are constructed, the paper summarizes network and cluster resources control algorithms. Finally, the need for integrated, hierarchical control is expressed, and speciﬁc examples are provided.

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“…Xu et al proposed an adaptive controller to modulate CPU share between different classes of traffic for page-view response time guarantees [21], [22]. In [23], [24], Gong and Xu developed a model-predictive feedback controller for responsive power capping. Their approaches rely on predefined explicit system models, building which can be highly knowledge intensive and labor intensive for complex system.…”

Section: Related Workmentioning

confidence: 99%

Coordinated Self-Configuration of Virtual Machines and Appliances Using a Model-Free Learning Approach

Rao

2013

IEEE Trans. Parallel Distrib. Syst.

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Cloud computing has a key requirement for resource configuration in a real-time manner. In such virtualized environments, both virtual machines (VMs) and hosted applications need to be configured on-the-fly to adapt to system dynamics. The interplay between the layers of VMs and applications further complicates the problem of cloud configuration. Independent tuning of each aspect may not lead to optimal system wide performance. In this paper, we propose a framework, namely CoTuner, for coordinated configuration of VMs and resident applications. At the heart of the framework is a model-free hybrid reinforcement learning (RL) approach, which combines the advantages of Simplex method and RL method and is further enhanced by the use of system knowledge guided exploration policies. Experimental results on Xen-based virtualized environments with TPC-W and TPC-C benchmarks demonstrate that CoTuner is able to drive a virtual server cluster into an optimal or near-optimal configuration state on the fly, in response to the change of workload. It improves the systems throughput by more than 30 percent over independent tuning strategies. In comparison with the coordinated tuning strategies based on basic RL or Simplex algorithm, the hybrid RL algorithm gains 25 to 40 percent throughput improvement.

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A Gray-Box Feedback Control Approach for System-Level Peak Power Management

Abstract: Abstract

Cited by 12 publications

References 32 publications

PowerTracer: Tracing Requests in Multi-Tier Services to Reduce Energy Inefficiency

PowerTracer: Tracing Requests in Multi-Tier Services to Reduce Energy Inefficiency

Energy Aware Data Centers and Networks: a Survey

Coordinated Self-Configuration of Virtual Machines and Appliances Using a Model-Free Learning Approach

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