Green data centers: Using hierarchies for scalable energy efficiency in large web clusters

Sousa, Lumárya Souza de; Leite, Julius; Loques, Orlando

doi:10.1016/j.ipl.2013.04.010

Cited by 10 publications

(3 citation statements)

References 4 publications

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“…Moreover, CPU utilization and response time are quite distinct from each other, and they are not symmetrical metrics, so response time cannot be simply changed to CPU utilization in their approaches. Although [25] uses utilization as a QoS indicator, it is based on divide-and-conquer (it does not consider server DSO and CPU DFS together) and heuristic methods, so it cannot obtain the best optimization result. Reference [23] imposes punishment on the servers whose utilization is greater than 80% when calculating the power efficiency but does not ensure the server's utilization.…”

Section: Related Studiesmentioning

confidence: 99%

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Online Power-Aware Deployment and Load Distribution Optimization for Application Server Clusters

et al. 2019

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The energy conservation of application server clusters is a pressing problem. In this paper, we propose an online power-aware deployment and load distribution optimization strategy for application server clusters, whose objective is to minimize the cluster's power while ensuring that the server's CPU utilization is not higher than a preset value. The strategy includes two schemes: a mixed integer linear programming (MILP)-based scheme and a mixed integer non-linear programming (MINLP)-based scheme. The former formulates the cluster optimization problem as a MILP problem and adopts a toolkit to solve it. When the cluster scale is small, it can find the global optimal solution quickly. So, the MILP-based scheme is applicable to small-scale clusters. In the MINLP-based scheme, we first formulate the cluster optimization problem as a non-linear programming problem, and then design a method to reduce the number of variables and reformulate it as an MINLP problem. We finally propose an efficient solution method based on the flower pollination algorithm. Due to the small number of variables and the high solution efficiency, the solution method can quickly obtain a high-quality solution, so the MINLP-based scheme can be applied to largescale clusters. The experimental results demonstrate the effectiveness of our strategy.

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Section: Related Studiesmentioning

confidence: 99%

“…. , C-1}} whose max{x/Power i (x) , x ∈(0, L i,F i )} is the highest; if (L predict − l is big enough, such that n i,j can be adjusted to N i − n i,3−j , and l i,j can be adjusted to L i,F i ) { 25:…”

Section: B Mixed Integer Non-linear Programming (Minlp) Problemmentioning

confidence: 99%

Online Power-Aware Deployment and Load Distribution Optimization for Application Server Clusters

et al. 2019

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“…Statistics shows that large and complex server farms and data centers all over the world constitute the majority of global ICT energy consumption [4,5]. This attracts several attentions and results into numerous research practices.…”

Section: Introductionmentioning

confidence: 99%

OCSO: Off-the-cloud service optimization for green efficient service resource utilization

Fang

Liu

et al. 2014

J Cloud Comp

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Many efforts have been made in optimizing cloud service resource management for efficient service provision and delivery, yet little research addresses how to consume the provisioned service resources efficiently. Meanwhile, typical existing resource scaling management approaches often rest on single monitor category statistics and are driven by certain threshold algorithms, they usually fail to function effectively in case of dealing with complicated and unpredictable workload patterns. Fundamentally, this is due to the inflexibility of using static monitor, threshold and scaling parameters. This paper presents Off-the-Cloud Service Optimization (OCSO), a novel user-side optimization solution which specifically deals with service resource consumption efficiency from the service consumer perspective. OCSO rests on an intelligent resource scaling algorithm which relies on multiple service monitor metrics plus dynamic threshold and scaling parameters. It can achieve proactive and continuous service optimizations for both real-world IaaS and PaaS services, through OCSO cloud service API. From the two series of experiments conducted over Amazon EC2 and ElasticBeanstalk using OCSO prototype, it is demonstrated that the proposed approach can make significant improvement over Amazon native automated service provision and scaling options, regardless of scaling up/down or in/out.

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Online energy-efficient deployment based on equivalent continuous DFS for large-scale web cluster

et al. 2018

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Green data centers: Using hierarchies for scalable energy efficiency in large web clusters

Cited by 10 publications

References 4 publications

Online Power-Aware Deployment and Load Distribution Optimization for Application Server Clusters

Online Power-Aware Deployment and Load Distribution Optimization for Application Server Clusters

OCSO: Off-the-cloud service optimization for green efficient service resource utilization

Online energy-efficient deployment based on equivalent continuous DFS for large-scale web cluster

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