This paper studies the optimal policy for joint control of admission, routing, service, and jockeying in a queueing system consisting of two exponential servers in parallel.Jobs arrive according to a Poisson process.U...This paper studies the optimal policy for joint control of admission, routing, service, and jockeying in a queueing system consisting of two exponential servers in parallel.Jobs arrive according to a Poisson process.Upon each arrival, an admission/routing decision is made, and the accepted job is routed to one of the two servers with each being associated with a queue.After each service completion, the servers have an option of serving a job from its own queue, serving a jockeying job from another queue, or staying idle.The system performance is inclusive of the revenues from accepted jobs, the costs of holding jobs in queues, the service costs and the job jockeying costs.To maximize the total expected discounted return, we formulate a Markov decision process(MDP) model for this system.The value iteration method is employed to characterize the optimal policy as a hedging point policy.Numerical studies verify the structure of the hedging point policy which is convenient for implementing control actions in practice.展开更多
工业物联网(Industrial Internet of Things,IIoT)是物联网(Internet of Things,IoT)在制造系统中的实现,随着它的快速发展,许多应用需要处理来自分布式终端设备的大量数据,以确保工业物联网的系统性能.本文从工业物联网应用管理者的角...工业物联网(Industrial Internet of Things,IIoT)是物联网(Internet of Things,IoT)在制造系统中的实现,随着它的快速发展,许多应用需要处理来自分布式终端设备的大量数据,以确保工业物联网的系统性能.本文从工业物联网应用管理者的角度出发,考虑工业物联网应用需要及时响应,组件需要处理敏感数据的特点,提出在应用允许最大响应时间,隐私保护和数据中心资源限制的约束下,优化云边环境中工业物联网应用的组件部署代价.本文提出了基于遗传算法(Genetic Algorithm,GA)和粒子群优化算法(Particle Swarm Optimization,PSO)的多应用部署算法(Multi Application Deployment Algorithm based on GA and PSO,MADPG)来得到应用组件的部署策略.该算法基于粒子群算法设计了数据中心与组件相映射的编码方式,在粒子进化过程中动态的改变相关学习因子并使用遗传和变异两种操作来提高算法的局部和全局搜索能力.仿真实验表明,与其他策略相比,基于MADPG的部署策略能够在满足工业物联网应用的约束下有效降低工业物联网应用的组件部署代价.展开更多
基金supported by the National Social Science Fund of China (19BGL100)。
文摘This paper studies the optimal policy for joint control of admission, routing, service, and jockeying in a queueing system consisting of two exponential servers in parallel.Jobs arrive according to a Poisson process.Upon each arrival, an admission/routing decision is made, and the accepted job is routed to one of the two servers with each being associated with a queue.After each service completion, the servers have an option of serving a job from its own queue, serving a jockeying job from another queue, or staying idle.The system performance is inclusive of the revenues from accepted jobs, the costs of holding jobs in queues, the service costs and the job jockeying costs.To maximize the total expected discounted return, we formulate a Markov decision process(MDP) model for this system.The value iteration method is employed to characterize the optimal policy as a hedging point policy.Numerical studies verify the structure of the hedging point policy which is convenient for implementing control actions in practice.
文摘工业物联网(Industrial Internet of Things,IIoT)是物联网(Internet of Things,IoT)在制造系统中的实现,随着它的快速发展,许多应用需要处理来自分布式终端设备的大量数据,以确保工业物联网的系统性能.本文从工业物联网应用管理者的角度出发,考虑工业物联网应用需要及时响应,组件需要处理敏感数据的特点,提出在应用允许最大响应时间,隐私保护和数据中心资源限制的约束下,优化云边环境中工业物联网应用的组件部署代价.本文提出了基于遗传算法(Genetic Algorithm,GA)和粒子群优化算法(Particle Swarm Optimization,PSO)的多应用部署算法(Multi Application Deployment Algorithm based on GA and PSO,MADPG)来得到应用组件的部署策略.该算法基于粒子群算法设计了数据中心与组件相映射的编码方式,在粒子进化过程中动态的改变相关学习因子并使用遗传和变异两种操作来提高算法的局部和全局搜索能力.仿真实验表明,与其他策略相比,基于MADPG的部署策略能够在满足工业物联网应用的约束下有效降低工业物联网应用的组件部署代价.
