Dmitry Basin

Learn More
Task pools have many important applications in distributed and parallel computing. Pools are typically implemented using concurrent queues, which limits their scalability. We introduce CAFÉ, Contention and Fairness Explorer, a scalable and wait-free task pool which allows users to control the trade-off between fairness and contention. The main idea behind(More)
Modern big data processing platforms employ huge in-memory key-value (KV-) maps. Their applications simultaneously drive high-rate data ingestion and large-scale analytics. These two scenarios expect KV-map implementations that scale well with both real-time updates and massive atomic scans triggered by range queries. However, today's state-of-the art(More)
Modern big data processing platforms employ huge in-memory key-value (KV) maps. Their applications simultaneously drive high-rate data ingestion and large-scale analytics. These two scenarios expect KV-map implementations that scale well with both real-time updates and large atomic scans triggered by range queries. We present KiWi, the first atomic KV-map(More)
Data centers, and particularly the massive ones that support cloud computing, e-commerce, social networking and other large-scale functionality, necessarily replicate data. Our basic premise is that since updates to replicated data can be thought of as reliable multicasts, data center multicast is a potentially important technology. Nonetheless, a series of(More)
Task pools have many important applications in distributed and parallel computing. Pools are typ-<lb>ically implemented using concurrent queues, which limits their scalability. We introduce CAFÉ, Con-<lb>tention and Fairness Explorer, a scalable and wait-free task pool which allows users to control the<lb>trade-off between fairness and contention. The main(More)
  • 1