Resilient Composition for Fault-Tolerant Cloud Applications

Abstract:

As the cloud evolves in capability, it has also become increasingly complex and difficult to program. New abstractions are necessary to ensure next-generation cloud applications are correct, simple, and efficient. In this talk, I will describe Resilient Composition, a new abstraction that ensures fault-tolerance in applications composed from independent, distributed components. The key insight is to rely on atomic, fault-tolerant “steps” that span component operations and messages. I will present DARQ, an efficient execution engine for such steps, and Distributed Speculative Execution, a transparent optimization that dramatically reduces overhead of Resilient Composition. Together, these solutions represent an important step towards a more declarative cloud, where strong primitives separate user applications from their underlying infrastructure, paving a way for simplicity and efficiency through automation.

Bio:

Tianyu Li is an incoming Assistant Professor at the University of Wisconsin-Madison. His research interests span distributed systems and database systems, with an emphasis on cloud-native applications. His current work focuses on designing the next generation of cloud runtime that will allow for flexibility and scalability without sacrificing fault-tolerance or performance. Previously, Tianyu obtained his PhD from MIT, and his BS/MS from CMU.

Vectors and Agents with Oracle AI Database

Abstract:

This presentation will explore how Oracle AI Database integrates advanced AI capabilities directly into its core data engine. First we’ll examine the architecture and mechanics of AI vector search in Oracle AI Database, covering the details on in-database vector generation, vector storage, indexing strategies, similarity search algorithms, and more. Next we’ll demonstrate how intelligent agents can be built near the database, using Oracle’s upcoming Agent Factory container, to orchestrate reasoning, retrieval-augmented generation, and workflow automation. Together these capabilities illustrate how Oracle AI Database can serve not only as a repository for enterprise data searchable using built-in vector search capabilities, but also as a tightly coupled platform for deploying AI-powered agents for building sophisticated, enterprise-grade solutions.

Bio:

Shasank Chavan is the GVP of Applied AI and Data Technologies in the Oracle Database organization.  His team built a number of highly visible, performance-critical data engine features including In-Memory Column Store, AI Vector Search, and Agent Factory with pre-built state-of-the-art agents like Knowledge Assistants and NL2SQL.  He earned his BS/MS from University of California, San Diego, and has 60+ patents in areas generally involving systems.