MIMO Power Control for High-Density Servers in an Enclosure

Wang, Xiaorui; Chen, Ming; Fu, Xing

doi:10.1109/tpds.2010.31

Cited by 47 publications

(42 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The MIMO advantage was also observed by Wang et al in the context of high-density servers [19]. We obtained similar results for compression systems attempting to deal with the trade-offs between power consumption and performance.…”

Section: Related Worksupporting

confidence: 87%

See 1 more Smart Citation

Model discovery for energy-aware computing systems: An experimental evaluation

Grosu

Muppalla

et al. 2011

2011 International Green Computing Conference and Workshops

View full text Add to dashboard Cite

Abstract-We present a model-discovery methodology for energy-aware computing systems that achieves high prediction accuracy. Model discovery, or system identification, is a critical first step in designing advanced controllers that can dynamically manage the energy-performance trade-off in an optimal manner. Our methodology favors Multiple-InputsMultiple-Outputs (MIMO) models over a collection of Single-Input-Single-Output (SISO) models, when the inputs and outputs of the system are coupled in a nontrivial way. In such cases, MIMO is generally more accurate than SISO over a wide range of inputs in predicting system behavior. Our experimental evaluation, carried out on a representative server workload, validates our approach. We obtained an average prediction accuracy of 77% and 76% for MIMO power and performance, respectively. We also show that MIMO models are consistently more accurate than SISO ones.

show abstract

Section: Related Worksupporting

confidence: 87%

“…Control theory has been applied to database systems [4], storage systems [10], [11], Web servers [3], [15], and data centers [13], [19]- [21] to provide QoS (e.g., performance and power) guarantees. Abdelzaher et al surveyed the application of feedback control to software systems [1].…”

Section: Related Workmentioning

confidence: 99%

Model discovery for energy-aware computing systems: An experimental evaluation

Grosu

Muppalla

et al. 2011

2011 International Green Computing Conference and Workshops

View full text Add to dashboard Cite

show abstract

“…In [8] were investigated three different ways to formulate the input and output variables of the relative management scheme in order to reduce the nonlinearity, while maintaining the scalability and applicability of the feedback control. Final results indicated that taking the ratios of the response time and resource caps of consecutive client classes based on the priority is the most suitable and effective setting.…”

Section: Hybrid Cloud Managementmentioning

confidence: 99%

Features Management and Middleware of Hybrid Cloud Infrastructures

Nikulchev¹,

Lukyanchikov²,

Pluzhnik³

et al. 2016

ijacsa

View full text Add to dashboard Cite

Abstract-The wide spread of cloud computing has identified the need to develop specialized approaches to the design, management and programming for cloud infrastructures. In the article were reviewed the peculiarities of the hybrid cloud and middleware software development, adaptive to implementing the principles of governance and change in the structure of storing data in clouds. The examples and results of experimental research are presented.

show abstract

“…Wang et al [26] proposed a MIMO controller to accurately regulate the total power consumption of an enclosure by conducting processor frequency scaling for each server while optimizing multi-tier application performance.…”

Section: Related Workmentioning

confidence: 99%

Self-Tuning Batching with DVFS for Improving Performance and Energy Efficiency in Servers

Cheng

Guo

Zhou

2013

2013 IEEE 21st International Symposium on Modelling, Analysis and Simulation of Computer and Telecommunication Systems

View full text Add to dashboard Cite

Abstract-Performance improvement and energy efficiency are two important goals in provisioning Internet services in data center servers. In this paper, we propose and develop a self-tuning request batching mechanism to simultaneously achieve the two correlated goals. The batching mechanism increases the cache hit rate at the front-tier Web server, which provides the opportunity to improve application's performance and energy efficiency of the server system. The core of the batching mechanism is a novel and practical two-layer control system that adaptively adjusts the batching interval and frequency states of CPUs according to the service level agreement and the workload characteristics. The batching control adopts a self-tuning fuzzy model predictive control approach for application performance improvement. The power control dynamically adjusts the frequency of CPUs with DVFS in response to workload fluctuations for energy efficiency. A coordinator between the two control loops achieves the desired performance and energy efficiency. We implement the mechanism in a testbed and experimental results demonstrate that the new approach significantly improves the application's performance in terms of the system throughput and average response time. The results also illustrate it can reduce the energy consumption of the server system by 13% at the same time.

show abstract

MIMO Power Control for High-Density Servers in an Enclosure

Cited by 47 publications

References 30 publications

Model discovery for energy-aware computing systems: An experimental evaluation

Model discovery for energy-aware computing systems: An experimental evaluation

Features Management and Middleware of Hybrid Cloud Infrastructures

Self-Tuning Batching with DVFS for Improving Performance and Energy Efficiency in Servers

Contact Info

Product

Resources

About