GreenColo: A novel incentive mechanism for minimizing carbon footprint in colocation data center

Islam, Mohammad A.; Ren, Shaolei; Wang, Xiaorui

doi:10.1109/igcc.2014.7039140

Cited by 16 publications

(5 citation statements)

References 14 publications

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“…where a single company manages all the infrastructure necessary to support their computing needs. There are also some recent proposals of stylized, decentralized optimization models to incentivize colocation tenants to carbonand cost-effectively manage their power demand [38], [39].…”

Section: A Related Researchmentioning

confidence: 99%

Carbon-Aware Computing for Datacenters

Radovanović¹,

Koningstein²,

Schneider³

et al. 2021

Preprint

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The amount of CO2 emitted per kilowatt-hour on an electricity grid varies by time of day and substantially varies by location due to the types of generation. Networked collections of warehouse scale computers, sometimes called Hyperscale Computing, emit more carbon than needed if operated without regard to these variations in carbon intensity. This paper introduces Google's system for Carbon-Intelligent Compute Management, which actively minimizes electricity-based carbon footprint and power infrastructure costs by delaying temporally flexible workloads. The core component of the system is a suite of analytical pipelines used to gather the next day's carbon intensity forecasts, train day-ahead demand prediction models, and use risk-aware optimization to generate the next day's carbon-aware Virtual Capacity Curves (VCCs) for all datacenter clusters across Google's fleet. VCCs impose hourly limits on resources available to temporally flexible workloads while preserving overall daily capacity, enabling all such workloads to complete within a day. Data from operation shows that VCCs effectively limit hourly capacity when the grid's energy supply mix is carbon intensive and delay the execution of temporally flexible workloads to "greener" times.

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Section: A Related Researchmentioning

confidence: 99%

Carbon-Aware Computing for Datacenters

Radovanović¹,

Koningstein²,

Schneider³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Lastly, datacenter providers are considering the possibility to provide economical incentives to the user if they are willing to reduce their power consumption [29,30]. In such a case, after a negotiation with the users, datacenter operators could use Nornir to constraint the power consumption of users applications.…”

Section: The User Perspectivementioning

confidence: 99%

“…To implement a new executor, the designer must define a subclass of the Knob class (Listing 5, lines [29][30][31][32][33][34][35]. In the constructor, the knobValues vector must be populated with the set of values that the knob can assume.…”

Section: Executementioning

confidence: 99%

Simplifying self-adaptive and power-aware computing with Nornir

Sensi

Matteis

Danelutto

2018

Future Generation Computer Systems

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Self-adaptation is an emerging requirement in parallel computing. It enables the dynamic selection of resources to allocate to the application in order to meet performance and power consumption requirements. This is particularly relevant in Fog Applications, where data is generated by a number of devices at a varying rate, according to users' activity. By dynamically selecting the appropriate number of resources it is possible, for example, to use at each time step the minimum amount of resources needed to process the incoming data.Implementing such kind of algorithms may be a complex task, due to low-level interactions with the underlying hardware and to non-intrusive and low-overhead monitoring of the applications. For these reasons, in this paper we propose Nornir, a C++based framework, which can be used to enforce performance and power consumption constraints on parallel applications running on shared memory multicores. The framework can be easily customized by algorithm designers to implement new self-adaptive policies. By instrumenting the applications in the PARSEC benchmark, we provide to strategy designers a wide set of applications already interfaced to Nornir. In addition to this, to prove its flexibility, we implemented and compared several state-of-the-art existing policies, showing that Nornir can also be used to easily analyze different algorithms and to provide useful insights on them.

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“…where a single company manages all the infrastructure necessary to support their computing needs. However, there are also some recent proposals of stylized, decentralized optimization models used to incentivize colocation tenants to carbon-and cost-effectively manage their power demand [12], [13].…”

Section: Introductionmentioning

confidence: 99%

Carbon-Aware Computing for Datacenters

Radovanović

Koningstein

Schneider

et al. 2023

IEEE Trans. Power Syst.

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The amount of CO2 emitted per kilowatt-hour on an electricity grid varies by time of day and substantially varies by location due to the types of generation. Networked collections of warehouse scale computers, sometimes called Hyperscale Computing, emit more carbon than needed if operated without regard to these variations in carbon intensity. This paper introduces Google's system for global Carbon-Intelligent Compute Management, which actively minimizes electricity-based carbon footprint and power infrastructure costs by delaying temporally flexible workloads. The core component of the system is a suite of analytical pipelines used to gather the next day's carbon intensity forecasts, train day-ahead demand prediction models, and use risk-aware optimization to generate the next day's carbon-aware Virtual Capacity Curves (VCCs) for all datacenter clusters across Google's fleet. VCCs impose hourly limits on resources available to temporally flexible workloads while preserving overall daily capacity, enabling all such workloads to complete within a day with high probability. Data from Google's in-production operation shows that VCCs effectively limit hourly capacity when the grid's energy supply mix is carbon intensive and delay the execution of temporally flexible workloads to "greener" times.

show abstract

GreenColo: A novel incentive mechanism for minimizing carbon footprint in colocation data center

Cited by 16 publications

References 14 publications

Carbon-Aware Computing for Datacenters

Carbon-Aware Computing for Datacenters

Simplifying self-adaptive and power-aware computing with Nornir

Carbon-Aware Computing for Datacenters

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