Reinforcement learning improves cloud microservice scheduling efficiency
DQN-Scheduler: A Multi-Objective Optimization Framework for Scheduling Microservices in Cloud Computing
Distributed, Parallel, and Cluster Computing
Summary
Scheduling many small cloud services is tricky because it needs to balance many things like using resources well, spreading work evenly, and keeping delays low. The authors created a new smart method called DQN-Scheduler that learns how to do this balancing act better using AI techniques. Their system tries to handle many goals at the same time, which hasn’t been done before. They tested it against other known methods and found it works better at scheduling cloud microservices.
What this means in practice
- •For cloud infrastructure teams: Schedule microservices in cloud environments to improve overall resource use and reduce delays using AI-based optimization.
- •For devops engineers: Automate balancing workloads of online microservices to enhance reliability and application performance in containerized setups.
Authors
Abdullah Alelyani, Amitava Data, Ghulam Mubasher
Abstract
Cloud computing has emerged as an information technology solution, providing software and infrastructure solutions for companies and individuals. The pay-as-you-go approach has increased demands for the cloud. The massive range of resources, the variety of services, and flexible pricing grab attention. In addition, microservices have emerged as a new way of building software, with applications developed as loosely dependent tasks. Additionally, container technology has boosted the popularity of microservices by offering a platform for this type of architecture. Containers and microservices improve the flexibility and scalability of cloud applications. There are two primary types of microservices: batch and online services, with the majority of applications falling into the online service category. Scheduling microservices is challenging because it requires careful management of resource utilization, load balancing, network latency, reliability, and availability. In this study, we introduce the DQN-Scheduler, a novel reinforcement learning-based agent designed to optimize microservice scheduling in cloud environments. Our approach aims to optimize multiple scheduling objectives simultaneously, such as resource utilization, load balancing, latency, reliability, and availability. To our knowledge, this is the first framework to address all these objectives simultaneously. The DQN-Scheduler was tested against benchmark algorithms in the field. The experimental results demonstrate that the DQN-Scheduler outperforms benchmark algorithms.