A Case Study: Using Genetic Algorithm for Job Scheduling Problem
This work addresses resource optimization for developers in DevOps environments, but it is incremental as it applies an existing method to a specific domain.
The study tackled the job scheduling problem in complex DevOps pipelines by using a genetic algorithm to automatically determine job priorities and allocate machine resources, resulting in improved makespan and reduced machine usage compared to existing methods.
Nowadays, DevOps pipelines of huge projects are getting more and more complex. Each job in the pipeline might need different requirements including specific hardware specifications and dependencies. To achieve minimal makespan, developers always apply as much machines as possible. Consequently, others may be stalled for waiting resource released. Minimizing the makespan of each job using a few resource is a challenging problem. In this study, it is aimed to 1) automatically determine the priority of jobs to reduce the waiting time in the line, 2) automatically allocate the machine resource to each job. In this work, the problem is formulated as a multi-objective optimization problem. We use GA algorithm to automatically determine job priorities and resource demand for minimizing individual makespan and resource usage. Finally, the experimental results show that our proposed priority list generation algorithm is more effective than current priority list producing method in the aspects of makespan and allocated machine count.