An Improved Genetic Optimization Algorithm for Scheduling Serverless Application Jobs in Public Cloud Environments

Due to the advantages of ease of management, high elasticity, and low prices, serverless computing has gained more and more momentum in recent years. Serverless computing allows developing and deploying applications in the form of Function-as-a-Service (FaaS). Application service providers can benefit greatly from deploying and running their applications in public serverless FaaS cloud platforms. Currently, public FaaS cloud platforms generally offer multiple computation resource configurations for application service providers to select for deploying and executing their functions with a pay-per-use billing. Application service providers can select a function instance with a higher processing capability to achieve a shorter job completion time, but it also incurs a higher cost, while service providers often have budget constraints for running their jobs. Thus, when scheduling serverless application jobs in public FaaS cloud platforms, application service providers need to trade off between the job completion time and the cost, which is generally an NP-hard problem. To address these issues, in this paper we propose an improved genetic optimization algorithm for scheduling serverless application jobs in public FaaS clouds, which can achieve the minimization of the job completion time within the given budgets. Specially, during the evolution process of our genetic scheduling algorithm, we use a greedy strategy to search for optimal schedules. Through experiments running on both synthetic and real-world data, we validate the efficiency and effectiveness of our proposed genetic optimization based scheduling algorithm in minimizing the job completion time within budget constraints.