Liu, Cong

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Cong Liu is an Associate Professor in the departments of Computer Science and Computer Engineering. Dr. Liu's expertise is in Real-Time Systems and his research interests include:

  • Real-Time and Embedded Systems,
  • Cyber-Physical Systems,
  • Real-Time Operating Systems,
  • Energy-Efficient Heterogeneous Computing,
  • Cluster and Cloud Computing
  • Physical-World Attacking/Detecting/Mitigating Vulnerabilities in DNN-driven Autonomous Embedded Systems
  • Predictable GPGPU Computing
  • System-level Optimization in DNN-driven Autonomous Driving


Recent Submissions

Now showing 1 - 3 of 3
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    Extending Battery System Operation via Adaptive Reconfiguration
    (Assoc Computing Machinery, 2019-01-16) He, Liang; Kong, Linghe; Gu, Yu; Liu, Cong; He, Tian; Shin, Kang G.; Liu, Cong
    Large-scale battery packs are commonly used in applications such as electric vehicles (EVs) and smart grids. Traditionally, to provide stable voltage to the loads, voltage regulators are used to convert battery packs' output voltage to those of the loads' required levels, causing power loss especially when the difference between the supplied and required voltages is large or when the load is light. In this article, we address this issue via a reconfiguration framework for the battery system. By abstracting the battery system as a cell graph, we develop an adaptive reconfiguration algorithm to identify the desired system configurations based on real-time load requirements. Our design is evaluated via both prototype-based experiments, EV driving trace-based emulations, and large-scale simulations. The results demonstrate an extended system operation time of up to 5x, especially when facing severe cell imbalance.
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    SoH-Aware Reconfiguration in Battery Packs
    (Institute of Electrical and Electronics Engineers Inc.) He, L.; Yang, Z.; Gu, Y.; Liu, Cong; He, T.; Shin, K. G.; Liu, Cong
    Cell imbalance, a notorious but widely found issue, degrades the performance and reliability of large battery packs, especially for cells connected in series where their overall capacity delivery is dominated by the weakest cell. In this paper, we exploit the emerging reconfigurable battery packs to mitigate the cell imbalance via the joint consideration of system reconfigurability and State-of-Health (SoH) of cells. Via empirical measurements and validation, we observe that more capacity can be delivered when cells with similar SoH are connected in series during discharging. Based on this observation, we propose two SoH-aware reconfiguration algorithms focusing on fully and partially reconfigurable battery packs, and prove their (near) optimality in capacity delivery. We evaluate the proposed reconfiguration algorithms analytically, experimentally, and via emulations, showing 10%-60% improvement in capacity delivery when compared with SoH-oblivious approaches, especially when facing severe cell imbalance. © 2010-2012 IEEE.
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    Many Suspensions, Many Problems: A Review of Self-Suspending Tasks in Real-Time Systems
    (Springer New York LLC) Chen, J. -J; Nelissen, G.; Huang, W. -H; Yang, M.; Brandenburg, B.; Bletsas, K.; Liu, Cong; Richard, P.; Ridouard, F.; Audsley, N.; Rajkumar, R.; de Niz, D.; von der Brüggen, G.; Liu, Cong
    In general computing systems, a job (process/task) may suspend itself whilst it is waiting for some activity to complete, e.g., an accelerator to return data. In real-time systems, such self-suspension can cause substantial performance/schedulability degradation. This observation, first made in 1988, has led to the investigation of the impact of self-suspension on timing predictability, and many relevant results have been published since. Unfortunately, as it has recently come to light, a number of the existing results are flawed. To provide a correct platform on which future research can be built, this paper reviews the state of the art in the design and analysis of scheduling algorithms and schedulability tests for self-suspending tasks in real-time systems. We provide (1) a systematic description of how self-suspending tasks can be handled in both soft and hard real-time systems; (2) an explanation of the existing misconceptions and their potential remedies; (3) an assessment of the influence of such flawed analyses on partitioned multiprocessor fixed-priority scheduling when tasks synchronize access to shared resources; and (4) a discussion of the computational complexity of analyses for different self-suspension task models.

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