DQN-Scheduler：云计算中微服务调度的多目标优化框架
DQN-Scheduler: A Multi-Objective Optimization Framework for Scheduling Microservices in Cloud Computing
- The University of Western Australia（西澳大利亚大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出DQN-Scheduler，一种基于强化学习的多目标优化框架，用于云环境中微服务调度，同时优化资源利用率、负载均衡、延迟、可靠性和可用性，实验证明其优于基准算法。
AI中文摘要：
云计算已成为一种信息技术解决方案，为企业和个人提供软件和基础设施服务。按需付费模式增加了对云的需求。海量的资源、多样化的服务以及灵活的定价吸引了人们的关注。此外，微服务已成为构建软件的一种新方式，应用程序被开发为松散依赖的任务。同时，容器技术通过为这种架构提供平台，推动了微服务的普及。容器和微服务提高了云应用程序的灵活性和可扩展性。微服务主要有两种类型：批处理服务和在线服务，大多数应用程序属于在线服务类别。调度微服务具有挑战性，因为它需要仔细管理资源利用率、负载均衡、网络延迟、可靠性和可用性。在本研究中，我们引入了DQN-Scheduler，一种新颖的基于强化学习的智能体，旨在优化云环境中的微服务调度。我们的方法旨在同时优化多个调度目标，如资源利用率、负载均衡、延迟、可靠性和可用性。据我们所知，这是第一个同时处理所有这些目标的框架。DQN-Scheduler与领域内的基准算法进行了测试。实验结果表明，DQN-Scheduler优于基准算法。
英文摘要：
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.
