The Jaseci Programming Paradigm and Runtime Stack: Building Scale-out Production Applications Easy and Fast
This addresses the barrier to entry for individuals and small teams in building complex scale-out applications, though it appears incremental as it builds on existing microservice and automation concepts.
The paper tackles the high complexity of developing and deploying scale-out production applications with diverse microservices by introducing Jaseci, a co-designed runtime system and programming language that automates data management and componentization, demonstrating benefits in performance and developer productivity through real-world AI applications.
Today's production scale-out applications include many sub-application components, such as storage backends, logging infrastructure and AI models. These components have drastically different characteristics, are required to work in collaboration, and interface with each other as microservices. This leads to increasingly high complexity in developing, optimizing, configuring, and deploying scale-out applications, raising the barrier to entry for most individuals and small teams. We developed a novel co-designed runtime system, Jaseci, and programming language, Jac, which aims to reduce this complexity. The key design principle throughout Jaseci's design is to raise the level of abstraction by moving as much of the scale-out data management, microservice componentization, and live update complexity into the runtime stack to be automated and optimized automatically. We use real-world AI applications to demonstrate Jaseci's benefit for application performance and developer productivity.