Thermal Time Shifting: Decreasing Data Center Cooling Costs with Phase-Change Materials

Skach, Matt; Arora, Manish; Hsu, Chang-Hong; Li, Qi; Tullsen, Dean M.; Tang, Lingjia; Mars, Jason

doi:10.1109/mic.2017.2911418

Cited by 16 publications

(5 citation statements)

References 17 publications

(16 reference statements)

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“…However, participation in flexibility programs is weak, facing challenges in coordinating it with the business-critical nature of IT equipment operation. Another branch of research approaches flexibility in data centres from their cooling systems through the utilisation of thermal storage, which allows flexible operation without compromising the servers' operating conditions [21] shows thermal storage improving reliability and demonstrating high economic benefits under variable electricity pricing [31] also demonstrates that combined management of thermal storage and workload scheduling can reduce peak cooling load. Generally, extending flexibility applications to cooling systems opens the possibility of diversifying flexibility services, as illustrated in Ref.…”

Section: Literature Reviewmentioning

confidence: 99%

Impacts of Flexible-Cooling and Waste-Heat Recovery from Data Centres on Energy Systems: A Danish Case Study

Jerez

Bergaentzlé

Keles

2023

Preprint

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Section: Literature Reviewmentioning

confidence: 99%

Impacts of Flexible-Cooling and Waste-Heat Recovery from Data Centres on Energy Systems: A Danish Case Study

Jerez

Bergaentzlé

Keles

2023

Preprint

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“…(1) Heat recycling for cooling power Saving energy either by generating power using TEGs or storing heat using phase-change materials (PCMs) [44] achieves a good result in reducing power consumption. Combining these methods in one work can achieve a better result in reducing power consumption besides introducing an efficient method for minimizing the power cost and reusing the heat.…”

Section: Challenges and Opportunitiesmentioning

confidence: 99%

Research advances on AI-powered thermal management for data centers

Liu

Aljbri

Song

et al. 2022

Tsinghua Sci. Technol.

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Given the complex nature of data centers' thermal management, which costs too many resources, processing time, and energy consumption, thermal awareness and thermal management powered by artificial intelligence (AI ) are the targeted study. In addition to a few research on AI techniques and models, other strategies have also been introduced in recent years. Data center models, including cooling, thermal, power, and workload models, and their relationship are factors that need to be understood in the optimal thermal management system.Simulation approaches have been proposed to help validate new models or methods used for scheduling and consolidating processes and virtual machines (VMs), hotspot identification, thermal state estimation, and power usage change. AI-powered thermal optimization leads to improved process scheduling and consolidation of VMs and eliminates the hotspot from happening. At present, research on AI-powered thermal control is still in its infancy. This paper concludes with four issues in thermal management, which will be the scope of further research.

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“…Other research has examine datacenter-level power and thermal optimization. Skach et al [40] proposed thermal time shifting for cooling in datacenters. Facebook optimized…”

Section: Related Workmentioning

confidence: 99%

ASIC Clouds: Specializing the Datacenter

et al. 2017

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GPU and FPGA-based clouds have already demonstrated the promise of accelerating computing-intensive workloads with greatly improved power and performance.In this paper, we examine the design of ASIC Clouds, which are purpose-built datacenters comprised of large arrays of ASIC accelerators, whose purpose is to optimize the total cost of ownership (TCO) of large, high-volume chronic computations, which are becoming increasingly common as more and more services are built around the Cloud model. On the surface, the creation of ASIC clouds may seem highly improbable due to high NREs and the inflexibility of ASICs. Surprisingly, however, large-scale ASIC Clouds have already been deployed by a large number of commercial entities, to implement the distributed Bitcoin cryptocurrency system.We begin with a case study of Bitcoin mining ASIC Clouds, which are perhaps the largest ASIC Clouds to date. From there, we design three more ASIC Clouds, including a YouTubestyle video transcoding ASIC Cloud, a Litecoin ASIC Cloud, and a Convolutional Neural Network ASIC Cloud and show 2-3 orders of magnitude better TCO versus CPU and GPU.Among our contributions, we present a methodology that given an accelerator design, derives Pareto-optimal ASIC Cloud Servers, by extracting data from place-and-routed circuits and computational fluid dynamic simulations, and then employing clever but brute-force search to find the best jointlyoptimized ASIC, DRAM subsystem, motherboard, power delivery system, cooling system, operating voltage, and case design. Moreover, we show how data center parameters determine which of the many Pareto-optimal points is TCOoptimal. Finally we examine when it makes sense to build an ASIC Cloud, and examine the impact of ASIC NRE.The first two authors contributed equally. To Appear in ISCA 2016.1 See http://blockchain.info to see real-time ledger updates.

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Thermal Time Shifting: Decreasing Data Center Cooling Costs with Phase-Change Materials

Cited by 16 publications

References 17 publications

Impacts of Flexible-Cooling and Waste-Heat Recovery from Data Centres on Energy Systems: A Danish Case Study

Impacts of Flexible-Cooling and Waste-Heat Recovery from Data Centres on Energy Systems: A Danish Case Study

Research advances on AI-powered thermal management for data centers

ASIC Clouds: Specializing the Datacenter

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