Energy consumption of data centers has increased dramatically for the past years. There is urgent need to build energy-efficient data centers, and growing attention has been paid to reducing cooling costs of data centers. The temperatures of data nodes in data centers have been identified as key factors to cooling costs.

Research Facilities

c7000 cluster c7000 cluster
Fig 1. HP C7000 Cluster with 8 Servers deployed at the MDF Data Center at CSUN.
  • Server: 8 * (ProLiant BL460c Gen8 + HP WS460c Expansion Blade)
    • CPU: 2 * Intel(R) Xeon(R) CPU E5-2690 v2 @ 3.00GHz (10 Cores)
    • Memory: 96 GB
    • Disk: 2 * Intel SSD DC S3520 480 GB
    • GPU: NVIDIA Tesla P4 8GB GDDR5
    • OS: Ubuntu 22.04.1 LTS

Active Research Projects

  • Thermal Modeling of Cluster Systems using Machine Learning
    • Alumni: Adrian Gonzalez, Icess Iana Nisce, Sai Pilla Vishnu, Christopher Pishaki
  • Energy-efficient Workload Management on Cluster Systems
    • Matthew Smith, Brandon Ismalej
    • Alumni: David Macoto Ward
  • Energy-aware Dynamic Virtual Machine Scheduling in Cloud Datacenter
    • Looking for highly motivated students to join this project
    • Alumni: Jonathan Chua, Nikhil Vepuri, Bakkr Alnaji
  • WATCHER: Autonomous Wheelchair with Co-bot Health Aid
    • Looking for highly motivated students to join this project
  • 3D Vehicle Detection in Real-time Traffic Flow for Intelligent Transportation System
    • Rachel Gilyard, Jimwell Castillo, Luis Cedeno, Teo Dominguez, Param Ridham Desai, Mahi Kachhadiya
    • Alumni: Jonathan Cordova, Alexander Rose (ARCS Fellow), Ruitao Wu (ARCS Fellow), Ziaur Chowdhury, Gustavo Velasquez Sanchez, Xin Gao, Cesar Villa, Icess Nisce (ARCS Fellow), Brian Uribe, Javier Carranza, Alondra Gonzalez, Matthew Davis, Andres Mercado, Jose Flores
  • Boracle - The Oracle for Your Body + Smart Textile Project
    • Matthew Smith (ARCS Fellow), Toan Pham, Manpreet Dhindsa
    • Alumni: Carl Austin Dimalanta (ARCS Fellow), Manpreet Dhindsa, Daniel Carter
  • A Framework for Smart Textile Large Scale Consumer Research
    • Yijing An, Kian Bonakdar (ARCS Fellow), Akul Kumar, Carl Austin Dimalanta, Himanshu Kuma, Jeel Patel, Harshit Penta, Kian Bonakdar, Matthew Garcia, Carlos Figueroa, Taylor Nastally, Francisco Revoreda, Thomas Dinh, Emi Anyakpor
  • Autonomous Growth System for Space Farming
    • Christian (John) Vega
    • Alumni: Manoj Nizampatnam
  • Geo Information-Visualization for Natural Disasters
    • Alumni: David Castrejon, Connor Wang, Denys Osmak, Luis Padilla, Farid Koushaneh, Edwin Rivera, Matthew Bellman, Alondra Sanchez Ochoa

Master Students

  • Rachel Gilyard - Accelerating PointPillars with Background Filtering for Faster Roadside Vehicle Detection and Classification
  • Harshit Penta - A Comprehensive Testing Approach using Jest for React Native Mobile Applications
  • Christian Vega - Robotic Arm Control Module For Lettuce Cultivation System
  • Sergio Ramirez - An Ensemble Approach for California Wildfire Prediction using Mini Batch and Multiprocessing
  • Fernando Vargas - Injury Prevention and Prediction using Machine Learning
  • Kian Bonakdar - TBD
  • Carl Austin Dimalanta - Boracle: A centralized data platform for human health using smart wearable devices (graduated in Spring 2024)
  • Jonathan Cordova - 3D Roadside Vehicle Detection and Classification in Traffic Flow using Complex YOLO (graduated in Spring 2024)
  • Jonathan Chua - Building a Cloud Infrastructure Research Platform: Leveraging Ansible and Zabbix for Real-world Testing and Validation of Scheduling Algorithms (graduated in Spring 2024)
  • Manpreet Dhindsa - Arrythmia Detection and Classification (graduated in Spring 2024)
  • Bakkr Alnaji - Harnessing 'Mesh App and Service Architecture' (MASA) for Achieving User Multiexperience, Business Agility, and Scale: A Comprehensive Study (graduated in Spring 2024)
  • Adrian Gonzalez - Thermal and energy modeling of cluster systems (graduated in Spring 2024)
  • Tarun Vihar Tumati - SBTCert: A Soulbound Token Certificate Verification System (graduated in Spring 2023)
  • Nikhil Vepuri - Performance Analysis of Virtual Machine Monitoring Systems (graduated in Spring 2023)
  • Alexander Rose - Real-time Traffic Monitoring System for Intelligent Transportation (graduated in Fall 2023)
  • Manoj Nizampatnam - Image Classification for Plant Health Monitoring in Space Farming (graduated in Fall 2023)
  • Akul Kumar - Unlocking Athletic Performance Insights Through Smart Clothing (graduated in Fall 2023)
  • Sai Pilla Vishnu - Thermal and Energy Prediction for Energy-efficient Data Centers using Machine Learning (graduate on Fall 2022)

Undergraudate Students

  • David Macoto Ward (fall 2020 - fall 2021): CSU LSAMP PROUD 2023 Award
  • Icess Iana Nisce (summer 2022 - present): Best Poster Award in IEEE GESSC 2022 conference
  • Carlos Arturo Morales Sierra (summer 2022)
  • Christopher Pishaki (fall 2022 - present): Spring 2023 Edison STEM-NET Student Research Fellowship Award

Student Faculty Research

California State University, Northridge (Fall 2021 - Present)

Earlham College

  1. Energy-aware Management System on Cluster (Summer 2018)
      Undergraduate Student Participants:
    • Thuy Thai (Software Engineer at Google)
    • Eli Ramthun (Energy Analyst at DNV, PhD at University of Texas, Austin)
  2. Energy-Efficient Load Balancing on Cluster Systems (Summer 2017)
      Undergraudate Student Participants:
    • Eli Ramthun (Energy Analyst at DNV, PhD at University of Texas, Austin)
    • Phuc Tran (AWS Software Engineer at Amazon, PhD in University of California, Irvine)
    • Niraj Parajuli (Consultant at Macedon Technologies)
    • Byron Roosa (Senior Security Consultant at Blue Bastion)
  3. Thermal and Energy Modeling of Cluster Systems (Summer 2016)
      Undergraudate Student Participants:
    • Tuguldur Baigalmaa (Software Engineer at Terraform Labs)
    • Daiki Akiyoshi (Associate at BlackRock)
    • Lam Nguyen (Software Engineer at Google)
  4. Thermal Profiling of Cluster Systems (Summer 2015)
      Undergraduate Student Participants:
    • Tuguldur Baigalmaa (Software Engineer at Terraform Labs)
    • Wilson Lim (Software Developer at DriveWealth)

Cluster Monitoring Systems

System monitoring is an important basis for system modelling and improvement. For achieving higher efficiency in performance and lower energy consumption in cluster systems, we design a monitoring system that tracks the performance and temperature of clusters for education and research purposes. The total energy consumption of clusters can be estimated by using the performance and temperature data. Specifically, in our proposed system, all real-time data (including temperature and activities of components of computing nodes) is collected and stored in Round-Robin Databases (RRDs). These data can be visualized or downloaded through a friendly user interface for further analysis. Moreover, our system also provides users with a powerful runtime comparison feature, which allows users to compare the performance of a running experiment with historical experimental results without waiting for the completion of experiments. The data visualization and user interfaces in the monitoring system are demonstrated by using an experiment on our cluster system.

Publication:

[1] X.-F. Jiang, T. Baigalmaa, L. Nguyen, D. Akiyoshi, E. Ramthun, N. Parajuli, and C. Peck, "High Performance Cluster Monitoring System", 2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), Honolulu, HI, USA, 12-15 Nov, 2018.

Thermal Modeling of Data Storage Systems

Disks have not been paid fully attention in their impacts on outlet temperatures of data nodes. In modern data centers, Teradata equipments could support more than 100 disks in a single data node. We expect large affect from the disks' temperature.

We proposed a thermal modeling approach to estimate temperatures of CPUs and disks (including hard drives and solid state disks). This approach can be applied to investigate thermal behaviours of storage systems under various utilizations patterns.

With the thermal modeling approach, outlet temperatures of data nodes can be estimated under particular workloads without deploying temperature sensors in storage systems. In addition, differences between inlet and outlet temperatures of data nodes can be modeled based on CPU and disk temperatures. Combining these models enables administrators to set up an appropriate inlet temperature of storage systems, which protects computing facilities from working in high temperature. Moreover, the cooling costs of storage systems can be calculated by using the proposed thermal model and the COP (Coefficient of Performance) model.

Publications:

[1] X.-F. Jiang,Y.-H. Liu, X.-J. Ruan, T. Baigalmaa, L. Nguyen, D. Akiyoshi, and C. Peck, "Energy Modeling of Cluster System", 2018 IEEE International Workshop on Signal Processing Systems (SiPS),Cape Town, South Africa, 21-24 Oct, 2018.

[2] X.-F. Jiang, M. I. Alghamdi, J. Zhang, M. Al Assaf, X.-J. Ruan, T. Muzaffar, and X. Qin, "Thermal Modeling and Analysis of Storage Systems", Proc. the 31th IEEE International Performance Computing and Communications Conference (IPCCC), 1-3 Dec. 2012. (Acceptance Rate: 27.8%, 32/115)

[3] X.-F. Jiang, M. M. Al Assaf, J. Zhang, M. I. Alghamdi, X.-J. Ruan, T. Muzaffar, and X. Qin, "Thermal modeling of hybrid storage clusters", Journal of Signal Processing Systems, 2013.

Thermal-aware Energy-efficient Task Scheduling

Dispatching tasks plays a significant important role in balancing the workload and reducing the energy consumption of data storage systems. Conventional task scheduling strategies distribute tasks for decreasing the computing cost of data nodes in storage systems. New trends are brought up by considering the reduction of cooling cost of data nodes.

With energy consumption of data nodes estimated by the thermal models, we proposed a task scheduling strategy, which keeps the outlet temperature of data nodes balanced across data storage systems. By considering the temperature distribution in data storage systems, a task scheduling strategy was proposed to dispatch tasks. The strategy not only selects the best data node that the task should be assigned to in terms of total energy costs of storage systems, but also ensures that the outlet temperatures of data nodes are not over a pre-determined threshold, which protects computing resources from working in high temperature environment.

Publication:

[1] X.-F. Jiang, J. Zhang, X. Qin, M.-H. Jiang, and J.-F. Zhang, "Thermal Modeling and Management of Storage Systems in Data Centers", Handbook on Data Centers (Springer), P915-944, 2015.

Thermal-aware Energy-efficient Data Placement

We built an energy efficient data transmission framework for storage systems. Giving a large amount of transferred data, PEAM judiciously selects the most energy-efficient data transmission method by predicting energy consumptions of all the candidate methods. Giving large amount of data and a group of data transmission methods, PEAM is able to select the most energy-efficient method by evaluating energy consumptions of all potential methods.

The PEAM is composed of three components: an energy cost predictor, a method selector, and a monitor. The energy cost predictor estimates energy consumption of data transmission through a particular method. The method selector chooses the best data transmission method in terms of energy efficiency. The monitor collects run-time information of each data node.

Publication:

[1] X.-F. Jiang, J. Zhang, M. I. Alghamdi, X. Qin, M.-H. Jiang, J.-F. Zhang, "PEAM: Predictive Energy-Aware Management for Storage Systems", Proc. 8th IEEE International Conference on Networking, Architecture, and Storage (NAS 2013), Xi'an, China, 17-19 July 2013.