Research Projects

Mohammad Hajiesmaili, UMass Amherst

Data Center Energy Optimization

This project is supported by an NSF CNS Award and a Google Faculty Research Award.

In recent years, there has been an unprecedented growth in energy footprint and cost of data centers as critical infrastructures of Internet services. Powering data centers from a portfolio of energy sources and elasticity in load execution are two key features that enable optimizing the energy cost by shifting the energy load in the temporal domain. This project intends to develop energy cost minimization algorithms that utilize the potentials of shifting the data center energy load in time. Having extreme and multidimensional uncertainty as the fundamental challenge, this proposal proposes a disciplined research based on algorithmic and learning understandings of the underlying optimization problems. Successful implementation of this proposal provides a beneficial design that can optimize the cost and robustness of data center energy operations and hence will have a significant impact on lowering the overall cost of Internet services.

This project focuses on three key goals of data center energy operations: minimizing the energy cost, maximizing the robustness against uncertainty, and improving the energy footprint of data centers. Toward this, it underpins the theoretical foundations of energy and load management in data centers from online algorithm and learning perspectives. Both are promising approaches since they do not rely on any exact or stochastic modeling of the future, hence, they are robust against extreme variability. The cornerstone of this proposal is to decompose the general problem into two subproblems of energy procurement and load management. Then, it develops robust and cost-effective online algorithms for each subproblem with provable performance guarantees against severe uncertainty. Finally, by leveraging the insights from the algorithm design for the subproblems, and the optimal offline solutions, it develops efficient learning algorithms for the general problem. This project is divided into three major tasks:

  1. Online Optimization for Energy Procurement: A disciplined design that tackles the cost minimization data center energy procurement. It designs online algorithms with provably best performance guarantees.

  2. Online Optimization for Load Management: It designs cost-effective and deadline-aware algorithms for managing elastic loads in data centers, with provable competitiveness against optimal offline solutions.

  3. Online Learning for Joint Energy and Load Management: A learning-based framework that learns joint optimization of the energy procurement and scheduling of the elastic load.

More broadly, this research brings fundamental algorithmic and learning understandings into data center energy research and also extends the existing results on classic online conversion problems, i.e., the online search for best prices in order to buy and/or sell assets, with single-dimensional uncertainty, to the multidimensional uncertainty. Hence, the proposed research makes fundamental theoretical contributions by advancing conversion problems to address multidimensional uncertainty. Finally, data centers are key infrastructures that enable a variety of Internet services. Hence, our work on optimizing the cost of a data center will have a significant positive impact on lowering the cost of Internet services. More broadly, it will facilitate the efficient and reliable incorporation of renewables into the data center, thereby reducing the carbon footprint of data centers. Such improvements will play a key role in moving toward a more sustainable data center and the electric grid.


Online Optimization and Learning

This project focuses on fundamental theoretical problems in online optimization and learning.


Online Algorithms for Electric Vehicle Charging Scheduling

This project studies the classical problem of online scheduling of deadline-sensitive jobs with different weight values and investigates its extension to Electric Vehicle (EV) charging scheduling by taking into account the processing rate limit of jobs and charging station capacity constraint. The problem lies in the category of time-coupled online scheduling problems without the availability of future information. it develops several online algorithms and analyzes their competitiveness using competitive ratio. Also, we investigate some other aspects of the problem such as how to provide on-arrival commitment and how to make sure that truthfulness is a dominant strategy for the users.


Online Energy Generation Scheduling

Energy generation scheduling is a fundamental problem in the next generation of energy systems that determines the on/off status and the output level of energy sources with the goal of minimizing the cost and satisfying both electricity and heat demand. With the penetration of distributed energy resources such as solar panel, the uncertainty in both renewable generation and demand makes the problem drastically different from its counterparts and in traditional power systems and brings out the essential need of online algorithm design. This project studies the problem of online energy generation scheduling in several different settings.