ECOGreen: Electricity Cost Optimization for Green Datacenters in Emerging Power Markets
| Autor: | David Atienza, Ali Pahlevan, Ayse K. Coskun, Marina Zapater |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Power management
Control and Optimization Computer science VM allocation 02 engineering and technology 7. Clean energy Resource (project management) Server Green datacenters 0202 electrical engineering electronic engineering information engineering Resource management Power market EES and renewable power Flexibility (engineering) Renewable Energy Sustainability and the Environment business.industry Quality-of-service Environmental economics Bidding Regulation service reserves Electricity cost 020202 computer hardware & architecture Renewable energy Computational Theory and Mathematics Hardware and Architecture 020201 artificial intelligence & image processing Electricity business Software |
| Zdroj: | IEEE Transactions on Sustainable Computing |
| ISSN: | 2377-3790 |
| Popis: | 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 percent 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 percent electricity costs when compared to other state-of-the-art datacenter electricity cost minimization techniques that participate in the power market. |
| Databáze: | OpenAIRE |
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