On the energy optimization for precedence constrained applications using local search algorithms

Pecero, Johnatan E.; Huacuja, Héctor Joaquín Fraire; Bouvry, Pascal; Santiago, Alejandro; Locés, Mario César López; Barbosa, Juan Javier González

doi:10.1109/hpcsim.2012.6266902

Cited by 7 publications

(3 citation statements)

References 16 publications

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“…VM consolidation is viewed as an act of combining into an integral whole, which saves energy by allocating work among fewer machines and turning off unused machines [18]. Using this approach, VMs allocated to underutilized hosts are consolidated to other servers and the remaining hosts are transformed into low power mode.…”

Section: Related Workmentioning

confidence: 99%

“…Thus, some users may experience high latencies, and others may even not receive services at all [14], which directly affects the requests that have Quality of Service (QoS) constraints. Unfortunately, current resource management approaches, like Dynamic Voltage Frequency Scaling (DVFS) [13] and VM consolidation [18], cannot function when the holistic arXiv:1706.02113v3 [cs.DC] 29 Jul 2017 data center is overloaded. The saturated resource not only brings over-utilized situation to hosts, but also causes high energy consumption.…”

Section: Introductionmentioning

confidence: 99%

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Energy Efficient Scheduling of Application Components via Brownout and Approximate Markov Decision Process

Buyya

2017

Service-Oriented Computing

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Unexpected loads in Cloud data centers may trigger overloaded situation and performance degradation. To guarantee system performance, cloud computing environment is required to have the ability to handle overloads. The existing approaches, like Dynamic Voltage Frequency Scaling and VM consolidation, are effective in handling partial overloads, however, they cannot function when the whole data center is overloaded. Brownout has been proved to be a promising approach to relieve the overloads through deactivating application non-mandatory components or microservices temporarily. Moreover, brownout has been applied to reduce data center energy consumption. It shows that there are trade-offs between energy saving and discount offered to users (revenue loss) when one or more services are not provided temporarily. In this paper, we propose a brownout-based approximate Markov Decision Process approach to improve the aforementioned trade-offs. The results based on real trace demonstrate that our approach saves 20% energy consumption than VM consolidation approach. Compared with existing energy-efficient brownout approach, our approach reduces the discount amount given to users while saving similar energy consumption.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Energy Efficient Scheduling of Application Components via Brownout and Approximate Markov Decision Process

Buyya

2017

Service-Oriented Computing

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“…VM consolidation is regarded to be an act of combining into an integral whole, which helps minimizing the energy consumed by allocating work among fewer machines and turning off unused machines [8]. Under this approach, the VMs hosted on underutilized hosts would be consolidated to other servers and the remaining hosts would be transformed into power-saving state.…”

Section: Related Workmentioning

confidence: 99%

Energy Efficient Scheduling of Cloud Application Components with Brownout

Dastjerdi

Buyya

2016

IEEE Trans. Sustain. Comput.

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It is common for cloud data centers meeting unexpected loads like request bursts, which may lead to overloaded situation and performance degradation. Dynamic Voltage Frequency Scaling and VM consolidation have been proved effective to manage overloads. However, they cannot function when the whole data center is overloaded. Brownout provides a promising direction to avoid overloads through configuring applications to temporarily degrade user experience. Additionally, brownout can also be applied to reduce data center energy consumption. As a complementary option for Dynamic Voltage Frequency Scaling and VM consolidation, our combined brownout approach reduces energy consumption through selectively and dynamically deactivating application optional components, which can also be applied to self-contained microservices. The results show that our approach can save more than 20% energy consumption and there are trade-offs between energy saving and discount offered to users.

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Energy efficiency in cloud computing data center: a survey on hardware technologies

2021

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On the energy optimization for precedence constrained applications using local search algorithms

Cited by 7 publications

References 16 publications

Energy Efficient Scheduling of Application Components via Brownout and Approximate Markov Decision Process

Energy Efficient Scheduling of Application Components via Brownout and Approximate Markov Decision Process

Energy Efficient Scheduling of Cloud Application Components with Brownout

Energy efficiency in cloud computing data center: a survey on hardware technologies

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