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.

Summer Research

Department of Computer Science, Earlham College.

1. Energy-aware Management System on Cluster

(Summer 2018: Eli Ramthun, Thuy Thai)

2. Energy-Efficient Load Balancing on Cluster Systems

(Summer 2017: Eli Ramthun, Phuc Tran, Niraj Parajuli, and Byron Roosa)

3. Thermal and Energy Modeling of Cluster Systems

(Summer 2016: Tuguldur Baigalmaa, Daiki Akiyoshi, and Lam Nguyen)

4. Thermal Profiling of Cluster Systems

(Summer 2015: Tuguldur Baigalmaa and Wilson Lim)

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.


[1] 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)

[2] 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.


[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 [Back to Top]

Evidences have shown that disks have non-negligible impact on data nodes. In modern data centers, a single data node usually supports multiple disks. For instance, a Teradata equipment is able to hold more than 100 disks. How to place data will affect the thermal behaviors of data nodes. Investigation of the thermal impacts and energy consumption of data nodes with various data placement schemes and workloads motivated me to build a new data placement strategy. The data placement strategy contains two-stage schemes: in the initial stage, data are distributed evenly inside the data center; and then in the redistribution stage, data are migrated according to outlet temperature distributions.


[1] X.-F. Jiang, M. I. Alghamdi, M. M. Al Assaf, X.-J. Ruan, J. Zhang, M.-K. Qiu, and X. Qin, "Thermal Modeling and Analysis of Cloud Data Storage Systems", Journal of Communications, Special Issue on Cloud and Big Data, Volume 9, No. 4, April 2014.

Predictive Energy-aware Management (PEAM)

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.


[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.