Renewable and cooling aware workload management for sustainable data centers

By Zhenhua Liu, Yuan Chen, Cullen E. Bash, Adam Wierman, Daniel Gmach, Zhikui Wang, Manish Marwah, Chris Hyser. 2012

488 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering, Information Systems, ML Systems & Hardware

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. Fan Yang
2. Andrew A. Chien
3. Fanxin Kong
4. Shui Yu
5. Rolf Stadler
6. Yonggang Wen
7. Xue Liu
8. Brendan Jennings

In Electrical and Electronic Engineering

1. Fanxin Kong
2. Yonggang Wen
3. Xue Liu
4. Miyuru Dayarathna

In Information Systems

1. Fan Yang
2. Andrew A. Chien
3. Shui Yu
4. Rolf Stadler
5. Yonggang Wen
6. Xue Liu
7. Brendan Jennings
8. Fanxin Kong

In ML Systems & Hardware

1. Andrew A. Chien
2. Fan Yang
3. Rolf Stadler
4. Yonggang Wen
5. Xue Liu
6. Brendan Jennings
7. Miyuru Dayarathna

Citations in the corpus

• Data centres in future European energy systems—energy efficiency, integration and policy (2019, background): “Scheduling delay-tolerant workloads within a data centre can lead to a temporal load shift (Goiri et al. 2014; Liu et al. 2012).”
• Resource Management in Clouds: Survey and Research Challenges (2015, background): “[144] focus on workload management in the context of data center energy use and the desire to maximize the use of energy from renewable 596 J Netw Syst Manage (2015) 23:567–619”
• Data Center Energy Consumption Modeling: A Survey (2016, methodology): “[217] Derived using the server power model described in Equation (22) Depends on multiple assumptions.”
• Carbon-Aware Computing for Datacenters (2021, unknown): “Research investigations for temporal shifting of flexible compute jobs proposed an optimization-based framework with stylized models for job-level resource demand modeling [25]–[27], and server-level power consumption.”
• Large-Scale and Extreme-Scale Computing with Stranded Green Power: Opportunities and Costs (2017, background): “A variety of research efforts study addition of local renewable generation to datacenters to reduce brown power usage [30], [31], [32], [33], [34], [35], [36], by exploring work-”
• Joint Optimization of Task Scheduling and Image Placement in Fog Computing Supported Software-Defined Embedded System (2016, background): “[17] reduce electricity cost and environmental impact using a holistic approach of workload balancing that integrates renewable supply, dynamic pricing, and QoS.”
• A Survey on Green-Energy-Aware Power Management for Datacenters (2014, methodology): “The first is to employ machine learning meth-ods to predict power generation for the future, such as the autoregressive (AR) model and k-NN algorithm used in Liu et al. [2012].”