Award Abstract # 1730043
II-EN: Collaborative Research: Enhancing the Parasol Experimental Testbed for Sustainable Computing

NSF Org: CNS
Division Of Computer and Network Systems
Recipient: RUTGERS, THE STATE UNIVERSITY
Initial Amendment Date: June 8, 2017
Latest Amendment Date: July 18, 2018
Award Number: 1730043
Award Instrument: Standard Grant
Program Manager: Marilyn McClure
mmcclure@nsf.gov
 (703)292-5197
CNS
 Division Of Computer and Network Systems
CSE
 Directorate for Computer and Information Science and Engineering
Start Date: July 1, 2017
End Date: June 30, 2022 (Estimated)
Total Intended Award Amount: $691,713.00
Total Awarded Amount to Date: $701,713.00
Funds Obligated to Date: FY 2017 = $691,713.00
FY 2018 = $10,000.00
History of Investigator:
  • Thu Nguyen (Principal Investigator)
    Tdnguyen@cs.Rutgers.Edu
  • Ivan Rodero (Co-Principal Investigator)
  • Abhishek Bhattacharjee (Co-Principal Investigator)
  • Ulrich Kremer (Co-Principal Investigator)
  • Manish Parashar (Former Co-Principal Investigator)
Recipient Sponsored Research Office: Rutgers University New Brunswick
3 RUTGERS PLZ
NEW BRUNSWICK
NJ  US  08901-8559
(848)932-0150
Sponsor Congressional District: 12
Primary Place of Performance: Rutgers University New Brunswick
NJ  US  08854-8019
Primary Place of Performance
Congressional District:
06
Unique Entity Identifier (UEI): M1LVPE5GLSD9
Parent UEI:
NSF Program(s): CCRI-CISE Cmnty Rsrch Infrstrc,
CSR-Computer Systems Research
Primary Program Source: 01001718DB NSF RESEARCH & RELATED ACTIVIT
01001819DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s): 7359, 7218
Program Element Code(s): 735900, 735400
Award Agency Code: 4900
Fund Agency Code: 4900
Assistance Listing Number(s): 47.070

ABSTRACT

This project will enhance an experimental datacenter for sustainable computing. Datacenters consume vast amounts of energy, totaling about 1.8% of the US electricity usage in 2014. Thus, the energy efficiency, energy-related costs, and overall sustainability of datacenters are critical concerns. NSF funded an experimental green datacenter called Parasol, which has previously demonstrated that the combination of green design and intelligent software management systems can lead to significant reductions in energy consumption, carbon emission, and cost. The enhanced version of this project will update energy sources, network technologies and management software.

Running real experiments in live conditions using Parasol led to findings that were not possible in simulation. This proposal seeks to update and enhance Parasol with current and next generation power-efficient servers, improve network connectivity and integrate software-defined networking (SDN) and Wi-Fi capabilities, increase solar energy generation capacity, add a low emission fuel cell power source, diversify energy storage, and improve the cooling system to advance green computing. The investigators will update and enhance Parasol's current software stack for monitoring, programmatic control, and remote access for the new hardware enhancements. Specific research goals are resource management in green datacenters, including coordinated workload, cooling, and energy scheduling against environmental and load variability to maximize the benefits of green datacenters and to help improve grid power management. A specific goal is to leverage accelerators such as GPUs and deep learning hardware, which promise excellent performance/watt ratios.

PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH

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Liu, Liu and Isaacman, Sibren and Kremer, Ulrich "Global cost/quality management across multiple applications" ESEC/FSE 2020: Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering , 2020 https://doi.org/10.1145/3368089.3409721 Citation Details
Liu, Liu and Isaacman, Sibren and Kremer, Ulrich "An Adaptive Application Framework with Customizable Quality Metrics" ACM Transactions on Design Automation of Electronic Systems , v.27 , 2022 https://doi.org/10.1145/3477428 Citation Details
Zhang, Kuo and Wang, Peijian and Gu, Ning and Nguyen, Thu D. "GreenDRL: Managing Green Datacenters Using Deep Reinforcement Learning" Proceedings of the ACM Symposium on Cloud Computing (SoCC) , 2022 https://doi.org/10.1145/3542929.3563501 Citation Details

PROJECT OUTCOMES REPORT

Disclaimer

This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.

The Parasol “green” micro-datacenter was built to provide an isolated and instrumented testbed in support of research on sustainable computing. It has proven to be an effective instrument for research involving power/energy, cooling, and their coordination with and impact on resource management, scheduling, and system/application performance and dependability. As cloud computing evolves rapidly to include “edge” computing, Parasol will also provide an effective platform for studying edge datacenters and their uses to support novel application services. Unfortunately, Parasol was aging and its utility as a research instrument was degrading as infrastructure components and computing equipment started to fail.

The major goal of this project is to update and enhance Parasol to extend its lifetime and increase its capabilities.  Specifically, the goals for the project are to (1) populate the enhanced testbed with state-of-the-art heterogeneous servers, hardware accelerators, and improved network connectivity; (2) repair and enhance Parasol’s infrastructure with special emphasis on the cooling system; (3) update and enhance Parasol’s software stack for monitoring, programmatic control, and remote access so that the testbed can be used by a significantly expanded set of researchers from Rutgers, Ohio State, and Stony Brook; and, (4) continue to collect data from Parasol’s operation for use in simulation and analytical studies of green datacenters.

All of the above goals have been achieved at the conclusion of the project. Parasol is now equipped with state of the art heterogeneous computing servers and network switches, connected to a heterogeneous cluster in a machine room, and is fully operational. The renovated instrument will enable the PIs to carry out a rich research agenda that explores the entire hardware/software ecosystem in green (edge) datacenters, as well as interactions between green (edge) datacenters and external systems. Specific research areas include: (1) Resource management, including issues such as coordinated workload, cooling, and energy scheduling in the presence of environmental and load variability to maximize the benefits of green datacenters. (2) Scheduling of emerging edge datacenter applications such as object recognition and video processing on heterogeneous hardware, including CPUs, GPUs, and video encoding/decoding accelerators, and in the context of green edge datacenters. (3) Developing analytic models to manage accelerators and processing speeds across whole racks as energy budgets vary—i.e., computational sprinting. (4) Using green edge data and computation centers in distributed application workflows that span mobile, edge, in-transit, and cloud computing. (5) Exploring adaptive automatic configuration management frameworks that can help users and applications adapt to changing runtime conditions for appropriate tradeoff between application quality and resource availability and consumption. (6) Investigating scheduling, placement, and sharing of GPU resources to improve GPU utilization and reduce latency when execution DNN training and inference jobs on GPU-equipped servers. (7) Developing algorithms for the dynamic resource management of virtual machines in datacenters in an online manner and incorporating changepoint detection into the online resource management algorithms. (8) Developing algorithms for scheduling jobs over interchangeable resources such as CPUs and GPUs to optimize performance while providing fairness among users sharing the cluster.

In fact, as the renovation of Parasol was done in several steps, the PIs have already successfully used the instrument in a number of projects. This has led to a number of publications along the research directions described above, as well as open source products and data available through the PIs’ websites.

 


Last Modified: 11/03/2022
Modified by: Thu D Nguyen

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