基于云服务的企业移动应用研发及调度管理平台

随着数字化移动技术不断在企业推广应用,早期由于缺少技术手段统筹管理,各专业自行开展建设产生的“多、杂、乱”问题日趋明显,建设成本居高不下。文章提出了一种基于云服务的企业移动应用研发及调度管理平台,采用云服务方式设计并部署了开发平台层、应用管理层、调度控制层。开发平台层通过提供Hybrid研发引擎、组件库、模版库和模拟器等组件,提高了企业研发支撑能力;应用管理层通过提供用户管理、设备管理、应用商店、内容管理等模块,实现了规范化、集约化管理;调度控制层通过设计移动应用调度算法,实现了任务按匹配的资源进行科学合理调度,提高了资源利用率。试点应用情况表明,文章提出的平台框架满足了设计预期。

一类有时间窗口约束的多资源动态调度模型与方法

含时间窗口的多资源调度,是一个包括资源分配和时间窗口分配的两阶段优化过程。在初始调度方案执行过程中,由于新的任务需求的到达,需要对初始方案进行调整,以使整个调度方案最优。本文针对这种情况,分析了该问题中的主要约束条件,建立了含时间窗口的多资源动态调度模型,给出了一种启发式迭代修改求解方法;并以含时间窗口的多机调度问题为例,对模型和算法进行了验证。

Resource Scheduling Algorithm and Ecnomic Model in ForCES Networks

Resource scheduling algorithm for ForCES(Forwarding and Control Element Separation) networks need to meet the flexibility,programmability and scalability of node resources.DBC(Deadline Budget Constrain) algorithm relies on users select cost or time priority,then scheduling to meet the requirements of users.However,this priority strategy of users is relatively simple,and cannot adapt to dynamic change of resources,it is inevitable to reduce the QoS.In order to improve QoS,we refer to the economic model and resource scheduling model of cloud computing,use SAL(Service Level Agreement) as pricing strategy,on the basis of DBC algorithm,propose an DABP(Deadline And Budget Priority based on DBC) algorithm for ForCES networks,DABP combines both budget and time priority to scheduling.In simulation and test,we compare the task finish time and cost of DABP algorithm with DP(Deadline Priority) algorithm and BP(Budget Priority) algorithm,the analysis results show that DABP algorithm make the task complete with less cost within deadline,benifical to load balancing of ForCES networks.

Scheduling Multiple Orders per Job with Multiple Constraints on Identical Parallel Machines

With a comprehensive consideration of multiple product types, past-sequence-dependent （ p-s-d ） setup times, and deterioration effects constraints in processes of wafer fabrication systems, a novel scheduling model of multiple orders per job（MOJ） on identical parallel machines was developed and an immune genetic algorithm（IGA） was applied to solving the scheduling problem. A scheduling problem domain was described. A non-linear mathematical programming model was also set up with an objective function of minimizing total weighted earliness-tardlness penalties of the system. On the basis of the mathematical model, IGA was put forward. Based on the genetic algorithm （GA）, the proposed algorithm （IGA） can generate feasible solutions and ensure the diversity of antibodies. In the process of immunization programming, to guarantee the algorithm＇s convergence performance, the modified rule of apparent tardiness cost with setups （ATCS） was presented. Finally, simulation experiments were designed, and the results indicated that the algorithm had good adaptability when the values of the constraints＇ characteristic parameters were changed and it verified the validity of the algorithm.

Distributed fair queuing algorithm based on compensation coordination scheduling in WMN

This paper proposes a distributed fair queuing algorithm which is based on compensation coordi- nation scheduling in wireless mesh networks, considering such problems as the location-dependent competition and unfair channel bandwidth allocation among nodes. The data communication process requiring the establishment of compensation coordination scheduling model is divided into three periods： the sending period, the compensation period and the dormancy period. According to model parameters, time constraint functions are designed to limit the execution length of each period. The algorithms guarantee that the nodes complete fair transmission of network packets together in accordance with the fixed coordination scheduling rule of the model. Simulations and analysis demonstrate the effectiveness of the proposed algorithm in network throughput and fairness.

Reinforcement Learning Algorithm for Solving Load Commitment Problem Considering a General Load Model

Under the smart grid paradigm, in the near future all consumers will be exposed to variable pricing schemes introduced by utilities. Hence, there is a need to develop algorithms which could be used by the consumers to schedule their loads. In this paper, load scheduling problem is formulated as a LCP （load commitment problem）. The load model is general and can model atomic and non-atomic loads. Furthermore, it can also take into consideration the relative discomfort caused by delay in scheduling any load. For this purpose, a single parameter ＂uric＂ is introduced in the load model which captures the relative discomfort caused by delay in scheduling a particular load. Guidelines for choosing this parameter are given. All the other parameters of the proposed load model can be easily specified by the consumer. The paper shows that the general LCP can be viewed as multi-stage decision making problem or a MDP （Markov decision problem）. RL （reinforcement learning） based algorithm is developed to solve this problem. The efficacy of the algorithm is investigated when the price of electricity is available in advance as well as for the case when it is random. The scalability of the approach is also investigated.

Approach for earth observation satellite real-time and playback data transmission scheduling

The scheduling of earth observation satellites(EOSs)data transmission is a complex combinatorial optimization problem. Current researches mainly deal with this problem on the assumption that the data transmission mode is fixed, either playback or real-time transmission. Considering the characteristic of the problem, a multi-satellite real-time and playback data transmission scheduling model is established and a novel algorithm based on quantum discrete particle swarm optimization(QDPSO)is proposed. Furthermore, we design the longest compatible transmission chain mutation operator to enhance the performance of the algorithm. Finally, some experiments are implemented to validate correctness and practicability of the proposed algorithm.