The Jaseci Programming Paradigm and Runtime Stack: Building Scale-Out Production Applications Easy and Fast

Jason Mars,Yiping Kang, Roland Daynauth, Baichuan Li, Ashish Mahendra,Krisztian Flautner,Lingjia Tang

IEEE COMPUTER ARCHITECTURE LETTERS(2023)

引用 0|浏览19
暂无评分
摘要
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.
更多
查看译文
关键词
Serverless computing,artificial intelligence,warehouse-scale computing,runtimes,programming languages
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要