NIPD: Non-Intrusive Power Disaggregation in Legacy Datacenters

Tang, Guoming; Weixiang, Jiang; Zhou, Xu; Liu, Fangming; Wu, Kui

doi:10.1109/tc.2016.2582163

Cited by 16 publications

(11 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…One or even all of the models may need to be retrained in case system hardware is upgraded. Tang et al [29] proposed a softwarebased hierarchical power estimation approach for legacy datacenters. Similar to our rationale, they build power mapping functions (PMFs) of server states along with a selective incremental training process to achieve nonintrusive, zero-cost, accurate power monitoring.…”

Section: Related Workmentioning

confidence: 99%

“…For instance, IBM PowerExecutive [30] is a plug-in tool for gaining as well as capping actual power consumption of servers under the specified architecture -System X. Hardware (e.g., sensors) power measuring can be the best option for homogeneous datacenters but is hardly a solution for heterogeneous ones such as legacy systems and cloud datacenters [28] [29]. The main reasons are but not limited to expensiveness, poor scalability [3] and coarse granularity.…”

Section: Introductionmentioning

confidence: 99%

“…The main reasons are but not limited to expensiveness, poor scalability [3] and coarse granularity. By contrast, power monitoring systems built on software are able to support fine-grained, low-cost, easyto-extend monitoring in a cloud system that can be highly heterogeneous and constantly scaling [29]. The core of software power monitoring is the pre-built power consumption model, which is defined as one or multiple functions that map system performance related metrics to system power or energy consumption [4].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Power Consumption Model for Cloud Servers Based on Elman Neural Network

Lin

et al. 2021

IEEE Trans. Cloud Comput.

View full text Add to dashboard Cite

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Power Consumption Model for Cloud Servers Based on Elman Neural Network

Lin

et al. 2021

IEEE Trans. Cloud Comput.

View full text Add to dashboard Cite

show abstract

“…The erasure coding storage system can obtain the same or even higher data reliability with low additional storage space . Thus, it can save the storage resource and energy consumption of the data center efficiently . This system has received extensive attention in the field of distributed storage…”

Section: Introductionmentioning

confidence: 99%

“…1,4 Thus, it can save the storage resource and energy consumption of the data center efficiently. 5 This system has received extensive attention in the field of distributed storage. 2 However, the reduction in storage space brings about high repair cost.…”

mentioning

confidence: 99%

In‐network block repairing for erasure coding storage systems

Xia

Guo

Cheng

2019

Concurrency and Computation

View full text Add to dashboard Cite

Summary In the erasure coding storage system, it is necessary to extract multiple data blocks from other remaining storage nodes to a new node when a storage node fails, which repairs the failed data block satisfactorily. However, this would incur the incast problem at this new node. The existing solutions for the repair process in the incast problem mainly rely on path planning and resource allocation. Although these solutions improve the performance of repairing the failed data blocks, they still waste a large amount of storage and bandwidth resources unavoidably. In this paper, we propose the incast problem to be resolved economically via the in‐network aggregation. Specifically, we assume that the switches in data centers have certain data processing capabilities and can aggregate data flows efficiently. Thereafter, we propose a set of in‐network methods to repair a failed data block in the erasure coding storage systems, taking the fat‐tree data center as an example. Thus, the incast problem can be solved effectively during the data transmission process. Compared with the prior methods, our approach effectively avoids the overhead of extra path computing, as well as significantly reduces the link cost of repairing data blocks, while promising similar or faster repair speed.

show abstract

Modeling the correlation between the workload and the power consumed by a server using stochastic and non‐parametric approaches

2022

View full text Add to dashboard Cite

In this article, we address a critical concern of the growth of computer servers number and the resulting power expenditures in data centers by analyzing the statistical metrics related to the workload executed in each physical node. The aim is to build a stochastic model for power consumption estimation based on historical data. Relying on in-depth investigation and experimental testing of the power consumption features and performance of the various workload datasets, we propose a model that considers the workload and the power consumed to be executed by a server as random variables. Based on the properties of the probabilistic distribution function of each random variable, we establish the correlation relationship between the workload and the power consumption using a non-parametric approach. Our use of a non-parametric method to learn a given probability model is challenging because it requires estimating the full distribution from the available data samples. The accuracy of our approach is demonstrated by estimating the energy consumption of various workloads. The experimental and simulation results show that our model outperforms many existing approaches in terms of accuracy, and it can be applied to a wide variety of workloads.

show abstract

NIPD: Non-Intrusive Power Disaggregation in Legacy Datacenters

Cited by 16 publications

References 22 publications

A Power Consumption Model for Cloud Servers Based on Elman Neural Network

A Power Consumption Model for Cloud Servers Based on Elman Neural Network

In‐network block repairing for erasure coding storage systems

Modeling the correlation between the workload and the power consumed by a server using stochastic and non‐parametric approaches

Contact Info

Product

Resources

About