Yu Lei, Yu Philip S, Duan Yucong, Qiao Hongyu
Department of Computer Science, Inner Mongolia University, Hohhot, China.
Department of Computer Science, University of Illinois at Chicago (UIC), Chicago, IL, United States.
Front Genet. 2022 Oct 10;13:964784. doi: 10.3389/fgene.2022.964784. eCollection 2022.
With the vigorous development of Internet technology, applications are increasingly migrating to the cloud. Cloud, a distributed network environment, has been widely extended to many fields such as digital finance, supply chain management, and biomedicine. In order to meet the needs of the rapid development of the modern biomedical industry, the biological cloud platform is an inevitable choice for the integration and analysis of medical information. It improves the work efficiency of the biological information system and also realizes reliable and credible intelligent processing of biological resources. Cloud services in bioinformatics are mainly for the processing of biological data, such as the analysis and processing of genes, the testing and detection of human tissues and organs, and the storage and transportation of vaccines. Biomedical companies form a data chain on the cloud, and they provide services and transfer data to each other to create composite services. Therefore, our motivation is to improve process efficiency of biological cloud services. Users' business requirements have become complicated and diversified, which puts forward higher requirements for service scheduling strategies in cloud computing platforms. In addition, deep reinforcement learning shows strong perception and continuous decision-making capabilities in automatic control problems, which provides a new idea and method for solving the service scheduling and resource allocation problems in the cloud computing field. Therefore, this paper designs a composite service scheduling model under the containers instance mode which hybrids reservation and on-demand. The containers in the cluster are divided into two instance modes: reservation and on-demand. A composite service is described as a three-level structure: a composite service consists of multiple services, and a service consists of multiple service instances, where the service instance is the minimum scheduling unit. In addition, an improved Deep Q-Network (DQN) algorithm is proposed and applied to the scheduling algorithm of composite services. The experimental results show that applying our improved DQN algorithm to the composite services scheduling problem in the container cloud environment can effectively reduce the completion time of the composite services. Meanwhile, the method improves Quality of Service (QoS) and resource utilization in the container cloud environment.
随着互联网技术的蓬勃发展，应用程序越来越多地迁移到云端。云作为一种分布式网络环境，已广泛扩展到数字金融、供应链管理和生物医药等众多领域。为满足现代生物医药产业快速发展的需求，生物云平台是医疗信息整合与分析的必然选择。它提高了生物信息系统的工作效率，还实现了生物资源可靠可信的智能处理。生物信息学中的云服务主要用于生物数据的处理，如基因的分析与处理、人体组织器官的检测与探测以及疫苗的存储与运输。生物医药公司在云端形成数据链，它们相互提供服务并传输数据以创建复合服务。因此，我们的动机是提高生物云服务的流程效率。用户的业务需求变得复杂多样，这对云计算平台中的服务调度策略提出了更高要求。此外，深度强化学习在自动控制问题中展现出强大的感知和持续决策能力，为解决云计算领域的服务调度和资源分配问题提供了新思路和方法。因此，本文设计了一种在容器实例模式下混合预留和按需模式的复合服务调度模型。集群中的容器分为预留和按需两种实例模式。复合服务被描述为一种三级结构：一个复合服务由多个服务组成，一个服务由多个服务实例组成，其中服务实例是最小调度单元。此外，提出了一种改进的深度Q网络（DQN）算法并将其应用于复合服务的调度算法中。实验结果表明，将我们改进的DQN算法应用于容器云环境中的复合服务调度问题，能够有效减少复合服务的完成时间。同时，该方法提高了容器云环境中的服务质量（QoS）和资源利用率。
