Feedback Control Scheduling in Energy-Efficient and Thermal-Aware Data Centers

Zhao, Xiaogang; Peng, Tao; Qin, Xiao; Hu, Qiping; Ding, Ling; Fang, Zhijun

doi:10.1109/tsmc.2015.2434797

Cited by 15 publications

(8 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…[11][12][13][14][15], the coefficient of performance (CoP) metric was used to measure the efficiency of the system besides the CRAC units. In addition to the CoP for achieving the cooling cost savings in CRAC, Zhao et al [11] used Eq. (1) to select the appropriate supply selection,…”

Section: Cooling Modelmentioning

confidence: 99%

“…where D is the heat distribution matrix that converts power supplied by p into the temperature domain [11] , and is the time interval. The cooling system is supplied the air temperature T sup .k /.…”

Section: Cooling Modelmentioning

confidence: 99%

“…The results show that while using TACS can achieve low-temperature differences among servers, there were no hotspots. Zhao et al [11] proposed the ThermoRing task allocation approach to monitor inlet temperatures in real time. They used the following equation to obtain the inlet temperature of node i in a period of time k [11] ,…”

Section: Thermal Modelmentioning

confidence: 99%

See 2 more Smart Citations

Research advances on AI-powered thermal management for data centers

Liu

Aljbri

Song

et al. 2022

Tsinghua Sci. Technol.

View full text Add to dashboard Cite

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.

show abstract

Section: Cooling Modelmentioning

confidence: 99%

Section: Cooling Modelmentioning

confidence: 99%

See 1 more Smart Citation

Research advances on AI-powered thermal management for data centers

Liu

Aljbri

Song

et al. 2022

Tsinghua Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…A workload aware approach is proposed in [100] based on control theoretic principles. At the datacenter layer, the study in [101] presented ThermoRing, a model-predictive control based scheduling strategy to reduce cooling costs in data centers.…”

Section: Section 4 Other Prediction Approaches 41 Model Predictive Cmentioning

confidence: 99%

A Survey of Prediction and Classification Techniques in Multicore Processor Systems

Ababei

Moghaddam

2019

IEEE Trans. Parallel Distrib. Syst.

View full text Add to dashboard Cite

show abstract

“…Low thermal efficiency may risk the safe operation of data centers. Zhao et al designed a model‐predictive control‐based scheduling strategy called ThermoRing, which incorporates a heat distribution matrix to keep the maximum inlet temperatures of nodes under a redline temperature. Lordan et al improved an existing programming model to reduce energy consumption in distributed infrastructures.…”

Section: Related Workmentioning

confidence: 99%

Thermal‐aware task assignments in high performance computing clusters

Taneja

Kulkarni

et al. 2017

Concurrency and Computation

Self Cite

View full text Add to dashboard Cite

Summary Cluster‐level thermal management has gained much attention over the past decade due to rising cooling costs associated with data centers. In this research, we propose and implement a static scheduler called SSched and a dynamic one named DSched. These 2 algorithms schedule jobs based on CPU and disk temperatures of a Hadoop cluster's nodes. Our schedulers rely on a monitoring mechanism to keep track of CPU and disk utilization, maintaining CPU and disk temperatures below a threshold through thermal‐aware scheduling decisions. To facilitate the design of SSched and DSched, we classify jobs into the CPU‐intensive and disk‐intensive categories. When a job arrives, SSched retrieves the utilization stats from a profiled log, estimates the thermal behavior, and places the job on NodeManager to minimize thermal impacts. Unlike SSched, DSched improves thermal efficiency of Hadoop clusters through dynamic load balancing. DSched keeps track of the coolest and hottest nodes in the cluster; tasks are migrated from hot nodes into cool ones if any hot spot is detected. To evaluate the effectiveness of our schedulers, we keep track of average CPU and disk temperatures in a node, managing an optimal outlet temperature across a cluster. We demonstrate that compared with the traditional Hadoop scheduler, SSched and DSched achieve approximately 15% savings in terms of cooling cost with little performance overhead.

show abstract

Feedback Control Scheduling in Energy-Efficient and Thermal-Aware Data Centers

Cited by 15 publications

References 32 publications

Research advances on AI-powered thermal management for data centers

Research advances on AI-powered thermal management for data centers

A Survey of Prediction and Classification Techniques in Multicore Processor Systems

Thermal‐aware task assignments in high performance computing clusters

Contact Info

Product

Resources

About