Scheduling method with adaptive learning for microservice workflows with hybrid resource provisioning

More and more attention has been paid to microservices from traditional monolithic workflows. How to rent on-demand and spot instances for microservice tasks in cloud computing is crucial to save rental costs for customers. According to the out-of-bid failure risks of spot instances and application deadline constraints, it is challenging to adaptively determine the number of on-demand and spot instances, allocate microservice tasks to the rented instances, and reschedule interrupted tasks. In this paper, we consider the problem of scheduling microservice workflows with hybrid resource provisioning. An adaptive-learning based scheduling algorithmic framework is proposed to intelligently sequence, allocate and online adjust tasks as well as monitor spot instance. Strategies for each component are developed. Components and parameter values are statistically calibrated over a comprehensive set of random instances. The proposed algorithm is compared to modified classical algorithms for similar problems. Experimental results demonstrate the effectiveness of the proposal for the considered problem.