Lab

Ziyu Shao's Lab

About the lab

Welcome to the Intelligence, Information and Decision Lab @ SIST-ShanghaiTech! Our mission is to bridge theory and practice by conducting interdisciplinary research related to intelligent decision making under incomplete information, including theories, models, algorithms, analysis, and implementation, using state-of-the-art mathematical and information techniques.

We are generally interested in the following fields:

Network intelligence for edge computing and cloud computing

AI for networking and networking for AI

Bandit and reinforcement learning

Group intelligence with multi-agent learning and optimization

System for reinforcement learning and machine learning

Featured research (48)

For software-defined networking (SDN) systems, to enhance the scalability and reliability of control plane, existing solutions adopt either multi-controller design with static switch-controller associations, or static control devolution by delegating certain request processing back to switches. Such solutions can fall short in face of temporal variations of request traffics, incurring considerable local computation costs on switches and their communication costs to controllers. So far, it still remains an open problem to develop a joint online scheme that conducts dynamic switch-controller association and dynamic control devolution. In addition, the fundamental benefits of predictive scheduling to SDN systems still remain unexplored. In this paper, we identify the non-trivial trade-off in such a joint design and formulate a stochastic network optimization problem that aims to minimize time-averaged total system costs and ensure long-term queue stability. By exploiting the unique problem structure, we devise a predictive online switch-controller association and control devolution (POSCAD) scheme, which solves the problem through a series of online distributed decision making. Theoretical analysis shows that without prediction, POSCAD can achieve near-optimal total system costs with a tunable trade-off for queue stability. With prediction, POSCAD can achieve even better performance with shorter latencies. We conduct extensive simulations to evaluate POSCAD. Notably, with mild-value of future information, POSCAD incurs a significant reduction in request latencies, even when faced with prediction errors.
In software-defined networking (SDN) systems, it is a common practice to adopt a multi-controller design and control devolution techniques to improve the performance of the control plane. However, in such systems, the decision-making for joint switch-controller association and control devolution often involves various uncertainties, e.g., the temporal variations of controller accessibility, and computation and communication costs of switches. In practice, statistics of such uncertainties are unattainable and need to be learned in an online fashion, calling for an integrated design of learning and control. In this paper, we formulate a stochastic network optimization problem that aims to minimize time-average system costs and ensure queue stability. By transforming the problem into a combinatorial multi-armed bandit problem with long-term stability constraints, we adopt bandit learning methods and optimal control techniques to handle the exploration-exploitation tradeoff and long-term stability constraints, respectively. Through an integrated design of online learning and online control, we propose an effective Learning-Aided Switch-Controller Association and Control Devolution (LASAC) scheme. Our theoretical analysis and simulation results show that LASAC achieves a tunable tradeoff between queue stability and system cost reduction with a sublinear time-averaged regret bound over a finite time horizon.

Lab head

Ziyu Shao
Department
  • School of Information Science and Technology

Members (3)

Xi Huang
  • Shenzhen Institute of Artificial Intelligence and Robotics for Society
Xin Gao
  • ShanghaiTech University
Bian Simeng
  • ShanghaiTech University
Junge Zhu
Junge Zhu
  • Not confirmed yet
Yijia Chang
Yijia Chang
  • Not confirmed yet