The following paper was accepted for presentation at NSDI 2026: Slowpoke: End-to-end Throughput Optimization Modeling for Microservice Applications. Yizheng Xie, Di Jin, Oğuzhan Çölkesen, Vasiliki Kalavri, John Liagouris, Nikos Vasilakis. The 23rd USENIX Symposium on Networked Systems Design and Implementation (NSDI ’26, to appear).
The following paper was accepted at SOSP 2025: ORQ: Complex analytics on private data with strong security guarantees. Eli Baum, Sam Buxbaum, Nitin Mathai, Muhammad Faisal, Vasiliki Kalavri, Mayank Varia, John Liagouris. The 31st Symposium on Operating Systems Principles (SOSP 2025).
The following paper was accepted for presentation at VLDB’25 (industrial track): Disaggregated State Management in Apache Flink 2.0. Yuan Mei, Zhaoqian Lan, Lei Huang, Yanfei Lei, Han Yin, Rui Xia, Kaitian Hu, Paris Carbone, Vasiliki Kalavri, Feng Wang. Proceedings of the VLDB Volume 18 (VLDB’25).
The following paper was accepted for presentation at ACM HotStorage’25: RingSampler: GNN sampling on large-scale graphs with io_uring. Qixuan Chen, Yuhang Song, Melissa Martinez, Vasiliki Kalavri. 17th ACM Workshop on Hot Topics in Storage and File Systems (HotStorage’25).
The following paper was accepted for presentation at the GRADES-NDA’25 workshop (co-located with SIGMOD’25): Bridging GNN Inference and Dataflow Stream Processing: Challenges and Opportunities. by Naima Abrar Shami and Vasiliki Kalavri. 8th Joint Workshop on Graph Data Management Experiences & Systems (GRADES) and Network Data Analytics (NDA) (GRADES-NDA’25).
Our paper “CAPSys: Contention-aware task placement for data stream processing” was accepted at EuroSys’25! We performed an empirical evaluation study to show that task placement not only significantly affects streaming query performance but also the convergence and accuracy of auto-scaling controllers. To address this issue, we propose CAPSys, an adaptive resource controller for dataflow stream […]
Members of the CASP Lab will present two recent works at the upcoming ACM SIGMOD’24 conference, in Santiago, Chile: QueryShield: Cryptographically Secure Analytics in the Cloud, was accepted at the SIGMOD’24 Demos track. In situ neighborhood sampling for large-scale GNN training, was accepted at the Data Management on New Hardware (DaMoN’24) workshop.
Our paper “Crayfish: Navigating the Labyrinth of Machine Learning Inference in Stream Processing Systems” was accepted for presentation at the 27th International Conference on Extending Database Technology (EDBT ’24). We contribute a principled benchmarking framework to help navigate the chaos in streaming ML inference.
Our paper “TVA: A multi-party computation system for secure and expressive time series analytics”, authored by Muhammad Faisal, Jerry Zhang, John Liagouris, Vasiliki Kalavri, and Mayank Varia, was accepted for publication at USENIX Security ’23. TVA is a multi-party computation system for secure analytics on secret-shared time series data that achieves high expressivity, by enabling […]
Our paper, “Secrecy: Secure collaborative analytics in untrusted clouds”, by John Liagouris, Vasiliki Kalavri, Muhammad Faisal, and Mayank Varia, has been accepted for presentation at NSDI’23! Secrecy is a system for privacy-preserving collaborative analytics as a service. Secrecy allows multiple data holders to contribute their data towards a joint analysis in the cloud, while keeping […]