We have received an i-Scale award for the project Streaming GNN Inference for Real-time Anomaly Detection on Microservice Graphs. The project will explore leveraging Graph Neural Networks to enhance security monitoring for cloud-native microservice environments. Many thanks to the i-Scale Industry Advisory Board!
“ORQ: Scaling Complex Multiparty Computations to Large Private Datasets” was invited by ACM TOCS for extended publication through recommendation by the Chairs of SOSP 2025!
Congratulations to Muhammad Faisal who successfully completed his Dissertation Prospectus milestone today! We look forward to your defense.
Talia and Yuhang presented a poster and short talk on “Scaling GNN Sampling on Large-Scale Graphs with io_uring” at the USENIX FAST’25 Work-in-Progress Report session, in Santa Clara, CA.
CASPLab students participated in the North East Database Day 2025 conference with two posters: 1. Zikun Wang, Yuanli Wang, Lei Huang, Sakshi Sharma, John Liagouris, Vasiliki Kalavri. “Zero-downtime reconfiguration mechanisms for dynamic data stream processing” 2. Han Dong, Yuanli Wang, Jonathan Appavoo, Vasiliki Kalavri. “Co-optimizing Performance and Power in Stream Processing Systems”
Talia Chen participated in the Student Research Competition of SOSP’24, undergraduate category, with a poster of her work, “Scaling GNN Sampling on Large-Scale Graphs with io_uring”. Talia made it to the final round, where she gave a short presentation in front of the judges and SOSP attendees. Congratulations, Talia!
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 […]
The latest Apache Flink Kubernetes Operator release includes an autoscaler component based on the OSDI’18 paper “Three steps is all you need: fast, accurate, automatic scaling decisions for distributed streaming dataflows” co-authored by Vasiliki Kalavri and John Liagouris. We are glad to see our research impact and very grateful to the Flink community for seeing […]
Our paper “Learning on streaming graphs with experience replay” has been accepted to appear at the 2022 ACM/SIGAPP Symposium on Applied Computing (SAC’22). This is a collaboration with Massimo Perini (University of Edinburgh), Giorgia Ramponi (ETH Zurich), and Paris Carbone (KTH Royal Institute of Technology). See the preprint pdf here.
Showan’s submission “Toward Workload-Aware State Management in Streaming Systems” was accepted for presentation at the 15th EuroSys Doctoral Workshop (EuroDW 2021). Here’s the abstract: Modern streaming systems rely on persistent KV stores to perform stateful processing on data streams. Although the choice of the state store is crucial for the system’s performance, there has been […]