作者：
Li, ZhihuaJiangnan Univ
Sch Internet Things Engn Dept Comp Sci & Technol Wuxi 214122 Jiangsu Peoples R China
In Cloud Computing (CC), the cost for computation and energy is less by current cloud data centers because it exploits virtualization for an effective resource management. The Virtual Machine (VM) migration authorizes...
详细信息
In Cloud Computing (CC), the cost for computation and energy is less by current cloud data centers because it exploits virtualization for an effective resource management. The Virtual Machine (VM) migration authorizes virtualization because it mitigates the difficulties of dynamic workload by repositioning VMs within cloud data centers. Through VM migration many goals of resource management are attained like load balancing, power management, fault tolerance, and system maintenance. The overload threshold is one of the key criterions to determine whether a host is overloaded or not. Achieving desired balance in guaranteeing quality of service, improving resource utilization and degrading energy consumption in data centers is the expected results of any overload threshold selection strategies. But, it is difficult due to the stochastic resource demands of VMs. In this paper, to address this problem, the overload threshold selection is modelled as a Markov decision process. With the solution of the improved Bellman optimality equation by the value iteration method, the optimization model is resolved, and the optimum overload threshold is adaptively selected. The hybrid processes are summarized as the Markov decision processes based adaptive overload threshold selection algorithm. Validations and comparisons are performed to illustrate its effectiveness and efficiency.
In this paper,we propose a framework for studying optimal agency execution strategies in a Limit Order Book (LOB) under a Markov-modulated market *** Almgren-Chriss's market impact model [1] is extended to a more ...
详细信息
In this paper,we propose a framework for studying optimal agency execution strategies in a Limit Order Book (LOB) under a Markov-modulated market *** Almgren-Chriss's market impact model [1] is extended to a more general situation where multiple venues are available for investors to submit *** the assumption of risk-neutrality,a compact recursive formula is derived,using the value iterative method,to calculate the optimal agency execution *** original optimal control problem is then converted to a constrained quadratic optimization problem,which can be solved by using the Quadratic Programming (Qp) *** examples are given to illustrate the efficiency and effective of our proposed methods.
The authors study the issues on distributed geographical packet forwarding in wireless sensor and actuator networks (WSANs) using a stochastic optimal control approach. First, a distributed geographic-informed forward...
详细信息
The authors study the issues on distributed geographical packet forwarding in wireless sensor and actuator networks (WSANs) using a stochastic optimal control approach. First, a distributed geographic-informed forwarding (DGIF) scheme is proposed that defines a set of distributed routing policies. Then, the distributed WSAN packet forwarding problem is modelled and analysed from the perspective of stochastic optimal control. The WSAN is viewed as a controlled stochastic system. The routing procedure is determined by the routing policy and system disturbance (e. g. the position uncertainty of remote nodes) jointly. An improved value iteration method is presented to accelerate the convergence of the optimal routing strategy. The reliability-driven routing algorithm (called DGIF-RRP) for emergency applications and the quality-of-serviceaware routing algorithm (called DGIF-QRP) for real-time applications are proposed. Simulations are carried out to evaluate the proposed routing algorithms. The results show that DGIF-RRP and DGIF-QRP significantly outperform two enhanced versions of the Dijkstra's algorithm in emergency and real-time applications, respectively.
Traffic flow predictions are central to a wealth of problems in transportation. Path choice models can be used for this purpose, and in state-of-the-art models-so-called recursive path choice (RPC) models-the choice o...
详细信息
Traffic flow predictions are central to a wealth of problems in transportation. Path choice models can be used for this purpose, and in state-of-the-art models-so-called recursive path choice (RPC) models-the choice of a path is formulated as a sequential arc choice process using undiscounted Markov decision process (MDP) with an absorbing state. The MDP has a utility maximization objective with unknown parameters that are estimated based on data. The estimation and prediction using RPC models require repeatedly solving value functions that are solutions to the Bellman equation. Although there are several examples of successful applications of RPC models in the literature, the convergence of the value iteration method has not been studied. We aim to address this gap. For the two closed-form models in the literature-recursive logit (RL) and nested recursive logit (NRL)-we study the convergence properties of the value iteration method. In the case of the RL model, we show that the operator associated with the Bellman equation is a contraction under certain assumptions on the parameter values. On the contrary, the operator in the NRL case is not a contraction. Focusing on the latter, we study two algorithms designed to improve upon the basic value iteration method. Extensive numerical results based on two real data sets show that the least squares approach we propose outperforms two value iteration methods.
The design of optimum rolling schedules for a rolling mill involves tradeoffs between conflicting measures of effectiveness. Important measures of effectiveness include productivity, quality of the final product, powe...
详细信息
The design of optimum rolling schedules for a rolling mill involves tradeoffs between conflicting measures of effectiveness. Important measures of effectiveness include productivity, quality of the final product, power consu mption, smoothness of plant operation etc. In this paper the scheduling of a reversing coiler tension rolling mill is formulated as a multicriteria dynamic programming (MDP) problem. Using an efficient algorithm developed for MDP, the optimum design problem is solved numerically by means of computer for the mill considered. The optimal schedules obtained have been adopted in real operation and the effectiveness of the mill production has been improved remarkably.
暂无评论