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. 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.

Related Publications:

• Lin Yang, Mohammad H. Hajiesmaili, Ramesh Sitaraman, Adam Wierman, Enrique Mallada, and Wing Wong, ‘‘Online Linear Optimization with Inventory Management Constraints,’’ in Proc. of ACM Sigmetrics, Boston, MA, USA, 2020, to appear.

• Sohaib Ahmad, Arielle Rosenthal, Mohammad H. Hajiesmaili, and Ramesh Sitaraman, ‘‘Learning from Optimal: Energy Procurement Strategies for Data Centers,’’ in Proc. of ACM eEnergy, Phoenix, AR, USA, 2019.

• Russell Lee, Mohammad H. Hajiesmaili, and Jian Li, “ Learning-Assisted Competitive Algorithms for Peak-Aware Energy Scheduling,” preprint. 

This project focuses on fundamental theoretical problems in online optimization and learning and develops foundational algorithms that are provably efficient in decision making under uncertainty.

Related Publications:

• Lin Yang, Mohammad H. Hajiesmaili, and Wing S. Wong, ‘‘Online Linear Programming with Uncertain Constraints,’’ in Proc. of IEEE CISS, Baltimore, MD, USA, 2019. (Invited Paper)

• Lin Yang, Lei Deng, Mohammad H. Hajiesmaili, Cheng Tan, Wing S. Wong, and ‘‘An Optimal Algorithm for Online Non-Convex Learning,’’ in Proc. of ACM SIGMETRICS, Irvine, CA, USA, 2018.

• Lin Yang, Wing S. Wong, and Mohammad H. Hajiesmaili,‘‘An Optimal Randomized Online Algorithm for QoS Buffer Management,’’ in Proc. of ACM SIGMETRICS, Irvine, CA, USA, 2018.

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.

Related Publications:

• Bahram Alinia, Mohammad H. Hajiesmaili, Zachary Lee, Noel Crespi, and Enrique Mallada, ‘‘Online EV Scheduling Algorithms for Adaptive Charging Networks with Global Peak Constraints,’’ IEEE Transactions on Sustainable Computing, to appear.

• Bahram Alinia, Mohammad H. Hajiesmaili, and Noel Crespi, ‘‘Online EV Charging Scheduling With On-Arrival Commitment,’’ IEEE Transactions on Intelligent Transportation Systems, vol. 20, no. 12, 4524 – 4537, 2019.

• Bahram Alinia, M. Sadegh Talebi, Mohammad H. Hajiesmaili, Ali Yekkekhani, and Noel Crespi, ‘‘Competitive Online Scheduling Algorithms with Applications in Deadline-Constrained EV Charging,’’ in Proc. of IEEE/ACM IWQoS, Alberta, Canada, 2018.

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 panels, 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 for online algorithm design. This project studies the problem of online energy generation scheduling in several different settings.

Related Publications:

• Hanling Yi, Mohammad H. Hajiesmaili, Ying Zhang, Minghua Chen, and Xiaojun Lin, ‘‘Impact of the Uncertainty of Distributed Renewable Generation on Deregulated Electricity Supply Chain,’’ IEEE Transactions on Smart Grid, vol. 9, no. 6, 6183–6193, 2018.

• Ying Zhang, Mohammad H. Hajiesmaili, Sinan Cai, Minghua Chen, and Qi Zhu, ‘‘Peak-Aware Online Economic Dispatching for Microgrids,’’ IEEE Transactions on Smart Grid, vol. 9, no. 1, 323–335, 2018.

• Mohammad H. Hajiesmaili, Minghua Chen, Enrique Mallada, and Sid Chau, ‘‘Crowd-Sourced Storage-Assisted Demand Response in Microgrids,’’ in Proc. of ACM eEnergy, Hong Kong, 2017, an extended abstract version appears in ACM Greenmetrics, 2017.

• Mohammad H. Hajiesmaili, Sid Chi-Kin Chau, Minghua Chen, and Longbo Huang, ‘‘Online Microgrid Energy Generation Scheduling Revisited: The Benefits of Randomization and Interval Prediction,’’ in Proc. of ACM e-Energy, Waterloo, Canada, 2016. (Best Paper Candidate)

• Ying Zhang, Mohammad H. Hajiesmaili, and Minghua Chen, ‘‘Peak-Aware Online Economic Dispatching for Microgrids,’’ in Proc. of ACM eEnergy, Bangalore, India, July 2015. (Best Paper Candidate)

Selected past projects: 

• Device-to-device Cellular Load Balancing

  • Mohammad H. Hajiesmaili, Lei Deng, Minghua Chen, and Zongpeng Li, ‘‘Incentivizing Device-to-Device Load Balancing for Cellular Networks: An Online Auction Design,’’ IEEE Journal on Selected Areas in Communications, vol. 35, no. 2, pp. 265–279, 2017.

• Cloud Video Conferencing

  • Mohammad H. Hajiesmaili, Lok To Mak, Zhi Wang, Chuan Wu, Minghua Chen, and Ahmad Khonsari, ‘‘Cost-Effective Low-Delay Design for Multi-Party Cloud Video Conferencing,’’ IEEE Transactions on Multimedia, vol. 19, no. 12, pp. 2760–2774, 2017.

  • Mohammad H. Hajiesmaili, Lok To Mak, Zhi Wang, Chuan Wu, Minghua Chen, and Ahmad Khonsari, ‘‘Cost-Effective Low-Delay Cloud-Assisted Video Conferencing,’’ in Proc. of IEEE ICDCS, Columbus, OH, USA, 2015.

• Data Aggregation in Wireless Sensor Networks

  • Bahram Alinia, Mohammad H. Hajiesmaili, Ahmad Khonsari, and Noel Crespi, ‘‘On the Construction of Maximum-Quality Aggregation Trees in Deadline-Constrained WSNs,’’ IEEE Sensors Journal, vol. 17, no. 12, pp. 3930–3943, 2017.

  • Bahram Alinia, Mohammad H. Hajiesmaili, and Ahmad Khonsari, ‘‘On the Construction of Maximum-Quality Aggregation Trees in Deadline-Constrained WSNs,’’ in Proc. of IEEE INFOCOM, Hong Kong, 2015.

• Network Utility Maximization for Delay-sensitive and Multimedia Networking

  • Mohammad H. Hajiesmaili, Mohammad S. Talebi, and Ahmad Khonsari, ‘‘Utility-Optimal Dynamic Rate Allocation under Average End-to-End Delay Requirements,’’ in Proc. of IEEE CDC, Osaka, Japan, 2015.

  • Soheil Javadi, Mohammad H. Hajiesmaili, Ahmad Khonsari, and Behzad Moshiri, ‘‘Energy-Aware Rate Allocation in Mission-Oriented WSNs with Sum-Rate Demand Guarantee,’’ Elsevier Computer Communications, Vol. 59, 52–66, 2015.

  • Mohammad H. Hajiesmaili, Mohammad S. Talebi, and Ahmad Khonsari, ‘‘Joint Multiapth Rate Control and Scheduling for SVC Transmission in Wireless Mesh Network,’’ International Journal of Ad Hoc and Ubiquitous Computing, Vol. 15, No. 4, 239–251, 2014.

  • Mohammad H. Hajiesmaili, Ahmad Khonsari, Ali Sehati, and Mohammad S. Talebi, ‘‘Content-aware Rate Allocation for Efficient Video Streaming via Dynamic Network Utility Maximization,’’ Elsevier J. Network and Computer Applications, Vol. 35, No. 6, 2016–2027, 2012.

  • Mohammad H. Hajiesmaili, Ali Sehati, and Ahmad Khonsari. ‘‘Content-aware Rate Control for Video Transmission with Buffer Constraints in Multipath Networks,’’ in Proc. of IEEE/IFIP NTMS, Istanbul, Turkey, 2012.

  • Mohammad S. Talebi, Ahmad Khonsari, Mohammad H. Hajiesmaili, ‘‘Utility-Proportional Bandwidth Sharing for Multimedia Transmission Supporting Scalable Video Coding,’’ Elsevier Computer Communications, Vol. 33, No. 13, 1543–1556, 2010.

  • Mohammad S. Talebi, Ahmad Khonsari, Mohammad H. Hajiesmaili, ‘‘Optimization Bandwidth Sharing for Multimedia Applications Supporting Scalable Video Coding,’’ in Proc. of IEEE LCN, Zurich, Switzerland, 2009.

  • Mohammad S. Talebi, Ahmad Khonsari, Mohammad H. Hajiesmaili, and Sina Jafarpour ‘‘A Suboptimal Network Utility Maximization Approach for Scalable Multimedia Applications,’’ in Proc. of IEEE GLOBECOM, Honolulu, Hawaii, USA, 2009.