Title: Scale-Out ccNUMA: Embracing Skew in Distributed Key-Value Stores
Abstract: Key-value stores (KVS’s) underpin many of today’s cloud services. For scalability and performance, state-of-the-art KVS systems distribute the dataset across a pool of servers, each of which holds a shard of data in memory and serves queries for the data in the shard. An important performance bottleneck that a KVS design must address is the load imbalance caused by skewed popularity distributions, whereby the “hot” items are accessed much more frequently than the rest of the dataset. Despite recent work on skew mitigation, existing approaches are limited in their efficacy when it comes to high-performance in-memory KVS deployments.
In this talk, I will discuss our recent work on skew mitigation for distributed in-memory KVS’s. We embrace popularity skew as a performance opportunity by aggressively caching popular items at all nodes of the KVS. The main challenges for such a design is maintaining the caches consistent while avoiding serialization points that can become a performance bottleneck at high load. I will describe our fully de-centralized caching architecture and the cache-coherence-inspired protocol used to keep the distributed caches consistent.
Boris Grot is an Associate Professor in the School of Informatics at the University of Edinburgh. His research seeks to address efficiency bottlenecks and capability shortcomings of processing platforms for data-intensive applications. Boris is a member of the MICRO Hall of Fame and a recipient of various awards for several of his publications, including an IEEE Micro Top Pick and the Best Paper Award at HPCA 2019. Boris holds a PhD in Computer Science from The University of Texas at Austin and had spent two years as a post-doctoral researcher at EPFL.
Coffee and cake will be served
Link to webcast will be announced (change of supplier ongoing)