Website: https://papoc-workshop.github.io/2021/Slack: #workshop-papoc
YouTube: https://www.youtube.com/playlist?list=PLzDuHU-z7gNgh7anHW2UO8Vrm_FhIoJ3FOrganizers: Heidi Howard (University of Cambridge, UK), Roberto Palmieri (Lehigh University, USA)
Schedule (all times are in British Summer Time)Opening & Session 1: 1:30 PM - 2:30 PM- Certified Mergeable Replicated Data Types
- Array CRDTs Using Delta-Mutations
- Access Control Conflict Resolution in Distributed File Systems using CRDTs
- Improving the Reactivity of Pure Operation-Based CRDTs
Session 2: 2:30 PM - 3:30 PM- Towards the Synthesis of Coherence/Replication Protocols from Consistency Models via Real-Time Orderings
- Totally-Ordered Prefix Parallel Snapshot Isolation
- Advanced Domain-Driven Design for Consistency in Distributed Data-Intensive Systems
- SCEW: Programmable BFT-Consensus with Smart Contracts for Client-Centric P2P Web Applications
Session 3: 3:30 PM - 4:30 PM- Convergent Causal Consistency for Social Media Posts
- Cambria: Schema Evolution in Distributed Systems with Edit Lenses
- Read-Write Quorum Systems Made Practical
Lightning Talks: 4:30 PM - 5:00 PMKeynote by Peter Alvaro (Joint with LADIS): 5:00 PM - 6:00 PMWhat not where: Sharing in a world of distributed, persistent memory
Abstract:
A world of distributed, persistent memory is on its way. Our programming models traditionally operate on short-lived data representations tied to ephemeral contexts such as processes or computers. In the limit, however, data lifetime is infinite compared to these transient actors. We discuss the implications for programming models raised by a world of large and potentially persistent distributed memories, including the need for explicit, context-free, invariant data references. We present a novel operating system that uses wisdom from both storage and distributed systems to center the programming model around data as the primary citizen, and reflect on the transformative potential of this change for infrastructure and applications of the future. We focus in particular on the landscape of data sharing and the consequences of globally-addressable persistent memory on existing consistency models and mechanisms.
Bio:
Peter Alvaro is an Assistant Professor of Computer Science at the University of California Santa Cruz, where he leads the Disorderly Labs research group
disorderlylabs.github.io. His research focuses on using data-centric languages and analysis techniques to build and reason about data-intensive distributed systems, in order to make them scalable, predictable and robust to the failures and nondeterminism endemic to large-scale distribution. Peter earned his PhD at UC Berkeley, where he studied with Joseph M. Hellerstein. He is a recipient of the NSF CAREER Award, the Facebook Research Award, the USENIX ATC Best Presentation Award, and the UCSC Excellence in Teaching Award.
