Burner: Recipe Automatic Generation for HPC Container Based on Domain Knowledge Graph

Abstract

As one of the emerging cloud computing technologies, containers are widely used in academia and industry. The cloud computing built by the container in the high performance computing (HPC) center can provide high-quality services to users at the edge. Singularity Definition File and Dockerfile (we refer to such files as recipes) have attracted wide attention due to their encapsulation of the application running environment in a container. However, creating a recipe requires extensive domain knowledge, which is error-prone and time-consuming. Accordingly, more than 34% of Dockerfiles in Github cannot successfully build container images. The crucial points about recipe creation include selecting the entities (base images and packages) and determining their relationships (correct installation order for transitive dependencies). Since the relationships between entities can be expressed accurately and efficiently by the knowledge graph, we introduce knowledge graph to generate high-quality recipes automatically. This paper proposes an automatic recipe generation system named Burner, enabling users with no professional computer background to generate the recipes. We first develop a toolset including a recipe parser and an entity-relationship miner. Our two-phase recipe parsing method can perform abstract syntax tree (AST) parsing more deeply on the recipe file to achieve entity extraction; the parsing success rate (PSR) of the two-phase parsing method is 10.1% higher than the one-phase parsing. Then, we build a knowledge base containing 2,832 entities and 62,614 entity relationships, meeting the needs of typical HPC applications. In the test of image build, the singularity image build success rate reaches 80%. Compared with the ItemCF recommendation method, our recommendation method TB-TFIDF achieves a performance improvement by up to 50.86%.