Christina Delimitrou

TL;DR: This work presents Quasar, a cluster management system that increases resource utilization while providing consistently high application performance, over a wide range of workload scenarios, including combinations of distributed analytics frameworks and low-latency, stateful services.

TL;DR: Paragon is an online and scalable DC scheduler that is heterogeneity and interference-aware, derived from robust analytical methods and uses collaborative filtering techniques to quickly and accurately classify an unknown, incoming workload, by identifying similarities to previously scheduled applications.

TL;DR: This paper presents DeathStarBench, a novel, open-source benchmark suite built with microservices that is representative of large end-to-end services, modular and extensible, and uses it to study the architectural characteristics of microservices, their implications in networking and operating systems, their challenges with respect to cluster management, and their trade-offs in terms of application design and programming frameworks.

TL;DR: Seer is presented, an online cloud performance debugging system that leverages deep learning and the massive amount of tracing data cloud systems collect to learn spatial and temporal patterns that translate to QoS violations.

TL;DR: This work presents PARTIES, a QoS-aware resource manager that enables an arbitrary number of interactive, latency-critical services to share a physical node without QoS violations, and shows that Party improves throughput under QoS by 61% on average, compared to existing resource managers.