Shaolei Ren

Research Profile

Dr. Shaolei Ren’s research centers around performance modeling and optimization of computer systems, with a balanced emphasis on analytical approaches and real system validation.

Recently, he has been working on the following projects:

  • Energy-efficient design of distributed stream mining systems: As an integrated component of Big Data analytics, stream mining systems have successfully found its usage in a wide spectrum of applications such as traffic analysis, health informatics, disaster information management, and security surveillance. The computational complexities associated with stream mining operations, combined with the soaring energy cost and increasingly larger-scale distributed data sources, motivate the exploration of novel energy-efficient solutions that take into account the unique characteristics of stream mining applications.


  • Integration of green energy in cloud computing: It is becoming increasingly imperative to power data centers using green energy for a sustainable growth of the IT industry. Nonetheless, the intermittent nature of green energy (e.g., solar, wind, hydro) makes it challenging to systematically integrate it with data centers. What adds to the design challenges is that data center managers need to plan well in advance for the long-term operation of data centers to achieve the aggressive goal of “net-zero”. To address these challenges, Dr. Ren is working on online algorithms that optimize the data center operation while capping the brown energy usage.


  • Resource management in heterogeneous systems for delay-sensitive services: While heterogeneous systems such as heterogeneous multicore processors and heterogeneous clusters have been hailed as promising solutions to energy efficiency for multihoming environments in which a mixture of workloads — both delay-sensitive and delay-tolerant workloads — coexists, it is largely unknown how to exploit the emerging hardware heterogeneity for delay-sensitive interactive services. In collaboration with Microsoft Research, Dr. Ren is investigating new theories and algorithms that reap the benefits of heterogeneity in the context of delay-sensitive applications.

Previous Accomplishments

  • Awards
    • IBM T. J. Watson Research Emerging Leaders in Multimedia and Signal Processing
    • IEEE International Conference on Communications (2009) Best Paper Award
  • Collaboration
    • Microsoft Research, Redmond
    • Tsinghua University, China
    • University of Rome “La Sapienza
    • Yonsei University, Korea
    • Hong Kong University of Science and Technology
    • University of California, Los Angeles

Relevant Publications

  1. S. Liu, S. Ren, G. Quan, M. Zhao, and S.-P. Ren, “Profit-Aware Load Balancing for Distributed Cloud Data Centers,” IEEE IPDPS, 2013.
  2. S. Ren and M. van der Schaar, “Dynamic Scheduling and Pricing in Wireless Cloud Computing,” IEEE Infocom, 2013.
  3. S. Ren, Y. He, and F. Xu, “Provably-Efficient Job Scheduling for Energy and Fairness in Geographically Distributed Data Centers,” ICDCS, 2012.
  4. S. Ren and M. van der Schaar, “Efficient Resource Provisioning and Rate Allocation for Stream Mining in a Community Cloud,” IEEE Transactions on Multimedia (special issue: “Cloud-Based Mobile Media: Infrastructure, Services and Applications”), to appear.
  5. S. Ren, J. Park, and M. van der Schaar, “Entry and Spectrum Sharing Scheme Selection in Femtocell Communications Markets,” IEEE/ACM Transactions on Networking, to appear.


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