Performance and Security of Large-Scale Online Services
IoT Stream Processing
Wang, Q., Gu, X., & Pu, C. (2024, October). A Study of Response Time Instability of
Microservices at High Resource Utilization in the Cloud. In 2024 IEEE 6th International
Conference on Cognitive Machine Intelligence (CogMI) (pp. 111-116). IEEE.
Alam, M. R., Wei, J., Sajid, M. S. I., Wang, Q., & Fu, C. (2024, October). Moving
from the Developer Machine to IoT Devices: An Empirical Study. In 2024 IEEE Secure
Development Conference (SecDev) (pp. 140-152). IEEE.
Gu, X., Wang, Q., Yan, Q., Liu, J., & Pu, C. (2024, July). Sync-millibottleneck attack
on microservices cloud architecture. In Proceedings of the 19th ACM ASIA Conference
on Computer and Communications Security (pp. 799-813).
Gu, X., Wang, Q., Liu, J., & Wei, J. (2024, June). Grunt attack: Exploiting execution
dependencies in microservices. In 2024 54th Annual IEEE/IFIP International Conference
on Dependable Systems and Networks (DSN) (pp. 115-128). IEEE.
Ching, C. W., Chen, X., Kim, T., Ji, B., Wang, Q., Da Silva, D., & Hu, L. (2024, April).
Totoro: A scalable federated learning engine for the edge. In Proceedings of the Nineteenth
European Conference on Computer Systems (pp. 182-199).
Liu, J., Wang, Q., Zhang, S., Hu, L., & Da Silva, D. (2023, November). Sora: A latency
sensitive approach for microservice soft resource adaptation. In Proceedings of the
24th International Middleware Conference (pp. 43-56).
Gu, X., Liu, J., & Wang, Q. (2023, November). A BlackBox Approach to Profile Runtime
Execution Dependencies in Microservices. In 2023 IEEE 9th International Conference
on Collaboration and Internet Computing (CIC) (pp. 116-120). IEEE.
Liu, J., Zhang, S., & Wang, Q. (2023, October). μConAdapter: Reinforcement Learning-based
Fast Concurrency Adaptation for Microservices in Cloud. In Proceedings of the 2023
ACM Symposium on Cloud Computing (pp. 427-442).
Ilhan, F., Su, G., Wang, Q., & Liu, L. (2023, July). Scalable Federated Learning with
System Heterogeneity. In 2023 IEEE 43rd International Conference on Distributed Computing
Systems (ICDCS) (pp. 1037-1040). IEEE.
Zhang, S., Wang, Q., Kanemasa, Y., Michaelis, J., Liu, J., & Pu, C. (2022, November).
ShadowSync: latency long tail caused by hidden synchronization in real-time LSM-tree
based stream processing systems. In Proceedings of the 23rd ACM/IFIP International
Middleware Conference (pp. 281-294).
2023: Best Paper Award in the ACM/IFIP Middleware 2023, Title: Sora: A Latency Sensitive
Approach for Microservice Soft Resource Adaption.
2019: Outstanding Services Award from the Services Conference Federation (SCF).
2018: LSU Tiger Athletic Foundation Undergraduate Teaching Award.
2018: Best Paper Award at the 11th International Conference on Cloud Computing (IEEE).
2016: NSF Research Initiation Initiative (CRII) Award.
2025: Source: National Science Foundation (NSF), Title: CC* Strategy-Campus: Adaptive
Spatiotemporal Data Ecosystem for Intelligent Integration of Cloud/Edge Involving
Human Agents, PI: Qingyang Wang, $99,999.
2020-2024: Source: NSF Division of Computer Network and System (CNS), Title: EAGER:
Subtle and Harmful: Millisecond-scale Viral Denial of Service (VDoS) Attacks, PI:
Qingyang Wang, $200,000.
2018-2020: Source: Subcontract from Georgia Tech, Collaboration with Fujitsu Labs
in Japan, PI: Qingyang Wang, $30,526.
2016-2019: Source: NSF Computer System Research Program (CSR), Title: CRII:CSR: an
Asynchronous Design to Reduce the Long-tail Latency of N-Tier App, PI: Qingyang Wang,
$175,000.
2015-2019: Source: National Science Foundation (NSF), Title: CyberSEES: Type 2: A
Coastal Resilience Collaboratory: Cyber-enabled Discoveries for Sustainable Deltaic
Coasts, SP: Qingyang Wang, Total Amount: $1,199,154, Dr. Wang's Portion: $43,920.
2017-2018: Source: Subcontract from Georgia Tech, Collaboration with Fujitsu Labs
in Japan, PI: Qingyang Wang, $15,263.
2016-2017: Source: Subcontract from Georgia Tech, Collaboration with Fujitsu Labs
in Japan, and LSU, Title: Experimental Study of Distributed Systems Performance Cloud
Applications Management, PI: Qingyang Wang, Software License from Fujitsu Labs in
Japan worth $30,000 for collaborative research.
2015-2018: Source: Louisiana Board of Regents (RCS), Title: A Study of Very Short
Bottlenecks: Understanding and Reducing Latency Long-Tail Problem of n-Tier Web Applications
in Cloud, PI: Qingyang Wang, $162,249.
2018-2023: Source: National Science Foundation (NSF), Title: Louis Stokes Regional
Center of Excellence: Center for Promotion of Academic Careers through Motivational
Opportunities to Develop Emerging Leaders in STEM (LS-PAC MODELS), SI: Qingyang Wang,
Total Amount: $2.5M, Dr. Wang's Portion: $75,000.
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