Power reduction in HPC data centers: a joint server placement and chassis consolidation approach

Pahlavan, Ali; Momtazpour, Mahmoud; Goudarzi, Maziar

doi:10.1007/s11227-014-1265-z

Cited by 15 publications

(7 citation statements)

References 29 publications

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“…Nonetheless, this optimization problem is non-linear and NP-hard. In our previous works [30], [54], we showed that the execution time of integer linear programming (ILP)-based VM allocation method for a small-scale scenario with 150 VMs is more than 2 hours; thus, runtime allocation would be unfeasible. In addition, combining allocation with bidding makes the execution time exponentially higher due to the increment of problem variables.…”

Section: Ecogreen: Proposed Electricity Cost Optimization Strategy Fomentioning

confidence: 99%

ECOGreen: Electricity Cost Optimization for Green Datacenters in Emerging Power Markets

Pahlevan

Zapater

Coskun

et al. 2021

IEEE Trans. Sustain. Comput.

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Modern datacenters need to tackle efficiently the increasing demand for computing resources while minimizing energy usage and monetary costs. Power market operators have recently introduced emerging demand-response programs, in which electricity consumers regulate their power usage following provider requests to reduce monetary costs. Among different programs, regulation service (RS) reserves are particularly promising for datacenters due to the high credit gain possibilities and datacenters' flexibility in regulating their power consumption. Therefore, it is essential to develop bidding strategies for datacenters to participate in emerging power markets together with power management policies that are aware of power market requirements at runtime. In this paper we propose ECOGreen, a holistic strategy to jointly optimize the datacenter RS problem and virtual machine (VM) allocation that satisfies the hour-ahead power market constraints in the presence of electrical energy storage (EES) and renewable energy. We first find the best power and reserve bidding values as well as the number of active servers in a fast analytical way that works well in practice. Then, we present an online adaptive policy that modulates datacenter power consumption by controlling VMs CPU resource limits and efficiently utilizing demand-side EES and renewable power, while guaranteeing quality-of-service (QoS) constraints. Our results demonstrate that ECOGreen can provide 76% of the datacenter power consumption on average as reserves to the market, due to largely operating on renewable sources and EES. This translates into ECOGreen saving up to 71% electricity costs when compared to other state-of-the-art datacenter electricity cost minimization techniques that participate in the power market.

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Section: Ecogreen: Proposed Electricity Cost Optimization Strategy Fomentioning

confidence: 99%

ECOGreen: Electricity Cost Optimization for Green Datacenters in Emerging Power Markets

Pahlevan

Zapater

Coskun

et al. 2021

IEEE Trans. Sustain. Comput.

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“…They also proposed effective server placement strategies in order to minimize the cooling cost. The latter problem was independently studied by Pahlavan et al [19], who utilized integer linear programming (ILP)-based methods to find the optimal location of each server in the datacenter. By assuming specific heat recirculation patterns, Mukherjee et al [20] designed approximation algorithms for a couple of related thermal-aware scheduling problems.…”

Section: Work On Spatial Correlationmentioning

confidence: 99%

Spatio-temporal thermal-aware scheduling for homogeneous high-performance computing datacenters

Sun

Stolf

Pierson

2017

Future Generation Computer Systems

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“…There are also other studies such as Pahlavan et al [34] who studied power reduction in data centers by developing new server placement methods for chassis consolidation, i.e. placing active servers on the same racks.…”

Section: Cloud Management Systemsmentioning

confidence: 99%

Energy efficiency of dynamic management of virtual cluster with heterogeneous hardware

Kommeri

Niemi²,

Nurminen

2016

J Supercomput

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Cloud computing is an essential part of today's computing world. Continuously increasing amount of computation with varying resource requirements is placed in large data centers. The variation among computing tasks, both in their resource requirements and time of processing, makes it possible to optimize the usage of physical hardware by applying cloud technologies. In this work, we develop a prototype system for load based management of virtual machines in an OpenStack computing cluster. Our prototype is based on an idea of 'packing' idle virtual machines into special park servers optimized for this purpose. We evaluate the method by running real high energy physics analysis software in an OpenStack test cluster and by simulating the same principle using the Cloudsim simulator software. The results show a clear improvement, 9%-48% , in the total energy efficiency when using our method together with resource overbooking and heterogeneous hardware.

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Power reduction in HPC data centers: a joint server placement and chassis consolidation approach

Cited by 15 publications

References 29 publications

ECOGreen: Electricity Cost Optimization for Green Datacenters in Emerging Power Markets

ECOGreen: Electricity Cost Optimization for Green Datacenters in Emerging Power Markets

Spatio-temporal thermal-aware scheduling for homogeneous high-performance computing datacenters

Energy efficiency of dynamic management of virtual cluster with heterogeneous hardware

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