19 citations · 36 across the 7 of their papers we have counts for
12 papers
MSRL: Distributed Reinforcement Learning with Dataflow Fragments
Huanzhou Zhu, Bo Zhao, Gang Chen +6
Reinforcement learning (RL) trains many agents, which is resource-intensive and must scale to large GPU clusters. Different RL training algorithms offer different opportunities for…
Dropbear: Machine Learning Marketplaces made Trustworthy with Byzantine Model Agreement
Alex Shamis, Peter Pietzuch, Antoine Delignat-Lavaud +2
Marketplaces for machine learning (ML) models are emerging as a way for organizations to monetize models. They allow model owners to retain control over hosted models by using clou…
CTR: Checkpoint, Transfer, and Restore for Secure Enclaves
Yoshimichi Nakatsuka, Ercan Ozturk, Alex Shamis +2
Hardware-based Trusted Execution Environments (TEEs) are becoming increasingly prevalent in cloud computing, forming the basis for confidential computing. However, the security goa…
Pronto: Federated Task Scheduling
Andreas Grammenos, Evangelia Kalyvianaki, Peter Pietzuch
We present a federated, asynchronous, memory-limited algorithm for online task scheduling across large-scale networks of hundreds of workers. This is achieved through recent advanc…
The EuroSys 2020 Online Conference: Experience and lessons learned
Angelos Bilas, Dejan Kostic, Kostas Magoutis +4
The 15th European Conference on Computer Systems (EuroSys'20) was organized as a virtual (online) conference on April 27-30, 2020. The main EuroSys'20 track took place April 28-30,…
Faasm: Lightweight Isolation for Efficient Stateful Serverless Computing
Simon Shillaker, Peter Pietzuch
Serverless computing is an excellent fit for big data processing because it can scale quickly and cheaply to thousands of parallel functions. Existing serverless platforms isolate…