基于网格技术的分布式VoD系统设计

基于网格CDN设计了VoD系统,该系统以标准的接口支持CDN服务节点的动态加盟.提出了基于就近性、内容分布和节点服务能力的三维全局视图进行调度的全局负载均衡策略,采用了集群作为VoD服务节点,降低了成本,对其内部负载均衡策略进行了设计,提高其QoS.     

VoD群集系统的设计与实现

提出了基于数字电视VoD群集系统的体系结构，实现了数据存储、视频服务器调度、视频播出、数据缓存和各功能模块的进程通信，并应用于视频点播系统。     

P2PVoD系统中基于节点服务能力的数据调度算法

P2P流媒体系统的数据调度算法直接影响系统整体性能和服务质量,一直是P2P流媒体系统研究的核心和热点问题。本文在给出了节点服务能力的定义和计算方法后,结合BT中的RarestFirst策略和VoD业务特点,提出了一种适合P2PVoD系统的基于节点服务能力的推拉相结合的数据调度算法。本算法考虑了节点的异构性和流媒体业务的时问敏感性,充分利用了推拉两种调度模式的优点,能更好的利用节点资源,减轻服务器负载,减小系统传输延迟。     

基于门限多播的补丁算法在VoD中的应用

对现有的3类流调度算法进行了分析,在补丁流算法的基础上提出了基于门限多播的补丁流算法,减少了用于多播的常规流数目,如果系统使用周期广播技术播出热门的流行节目,对不流行节目使用基于门限多播的补丁流技术,那么VoD系统的整体效率将得到提高.     

云模型优化的QPSO算法在大数据云存储平台业务调度中的应用

QPSO算法作为最优秀的群体智能算法之一,有着较强的寻优能力,将QPSO算法用于云计算平台资源调度策略的寻优,有着明显的优势。通过对QPSO算法在云计算中的应用研究,为云计算平台效率的提升提供有效方法。文章对云模型优化的QPSO算法在大数据云存储平台业务调度中的应用进行分析与研究。     

基于启发式调度的OpenFlow网络规则一致更新方案

针对OpenFlow网络在状态转换过程中会暂时性出现转发回路、路由黑洞和违反访问控制策略等问题,提出了一种基于启发式调度的规则一致更新方案.首先,设计基于谓词的更新分解算法,利用并行网络属性验证技术得到子更新依赖图;其次,采用任务图生成算法对子更新依赖图进行分割,降低更新调度的复杂度;之后,设计启发式更新调度算法,采用规则增删操作交替执行策略,减少交换机的规则存储开销,并通过建立更新实施和监听并发执行机制,提升更新效率.仿真实验从更新时间开销和更新过程中交换机规则存储开销两方面验证了方案的有效性.     

视频监控中多磁盘存储调度算法的研究

在视频监控应用系统中,视频信息存储需要大量磁盘存储空间,需要采用设计合适的机制进行视频信息的存储调度,既可以进行多路同时存储又保证存储效率。提出了基于磁盘剩余空间和被访问频度的多磁盘存储调度算法以及定时检测增加磁盘剩余空间的算法。     

粒子群优化算法在网格工作流调度中的应用

为了提高网格工作流管理系统的性能,将粒子群优化算法（PSO）引入到网格工作流的调度策略中．分析算法的基本原理,根据网格工作流调度的问题对其进行变形,提出基于粒子群优化算法的网格工作流调度策略,并与基于Dijkstra的网格工作流调度算法进行对比实验．实验数据表明,粒子群优化算法在网格工作流调度中的性能较好．     

移动网格可分任务调度研究

解决独立、等长度任务在性能异构且随时间动态变化的一组固定和移动资源上的调度问题,其中移动设备具有间歇连接性.提出动态循环调度DCS算法,用环结构组织任务,调度过程体现了重调度策略和复制策略.对算法进行性能分析和实验验证,表明算法性能和移动资源的间歇连接特征、任务运行跨度、移动资源数量、任务长度等因素相关.     

一种新的基于多因素多级模糊综合评判的更新算法

VoD作为交互检索型多媒体业务的典型代表 ,日益为人们所了解和接受。但是 ,网络结构的复杂以及使用上的价格昂贵成为制约VoD发展推广的重要因素。本文提出将宽带智能网的思想引入VoD ,给VoD业务带来了更大的灵活性。同时 ,在VoD体系中引入本地存储器 ,以降低成本和价格。进一步 ,为了提高用户访问的命中率 ,提出了一种基于多因素多级模糊综合评判的更新算法 ,该算法综合了影响用户访问命中率的多种因素 ,并根据其中的模糊问题采用模糊数学的方法加以分析解决。仿真表明 ,该方法有效地提高了访问节目的命中率。     

Scheduling Efficiency of Distributed Greedy Scheduling Algorithms in Wireless Networks

We consider the problem of distributed scheduling in wireless networks subject to simple collision constraints. We define the efficiency of a distributed scheduling algorithm to be the largest number (fraction) such that the throughput under the distributed scheduling policy is at least equal to the efficiency multiplied by the maximum throughput achievable under a centralized policy. For a general interference model, we prove a lower bound on the efficiency of a distributed scheduling algorithm by first assuming that all of the traffic only uses one hop of the network. We also prove that the lower bound is tight in the sense that, for any fraction larger than the lower bound, we can find a topology and an arrival rate vector within the fraction of the capacity region such that the network is unstable under a greedy scheduling policy. We then extend our results to a more general multihop traffic scenario and show that similar scheduling efficiency results can be established by introducing prioritization or regulators to the basic greedy scheduling algorithm     

并行多机调度问题的一种基于组合规则的遗传算法

本文对最小化完工时间并行多机调度问题提出了一种基于组合规则的遗传算法.用遗传算法来优化调度策略,使得在不同的调度阶段,可采用不同的调度规则以提高算法性能,并用计算实例表明了该遗传算法优于基于机器编码的模拟退火算法和遗传算法,并能适用于大规模并行多机调度问题,算法计算量小,鲁棒性强.     

云计算中基于能耗比例模型的虚拟机调度算法

针对资源虚拟化环境中的混合型负载调度问题,提出一种基于能耗比例模型的虚拟机调度算法.该算法利用处理器的"性能计数器"机制来评估各个虚拟机的近期能耗状态,并采用"最近最小能耗比例优先"的策略进行调度.理论分析给出了该算法的有效性证明和相关特性.实验结果显示,当系统面对混合型负载时,基于能耗比例模型的调度算法在"调度偏差"和"相对能效"两方面明显优于现有的虚拟机调度算法.     

Distributed scheduling based on polling policy with maximal spatial reuse in multi-hop WMNs

The scheduling algorithm based on the three-way handshaking scheme in IEEE 802.16d-2004 standard has some serious problems because of the complexity of the algorithm and low scheduling efficiency.To enhance the scheduling efficiency and improve the performance of multi-hop wireless mesh networks (WMNs), one distributed scheduling algorithm that can maximize the spatial and time reuse with an interference-based network model is proposed.Compared to the graph-based network model, the proposed network model can achieve a better throughput performance with maximal spatial reuse.Furthermore, this proposed scheduling algorithm also keeps fairly scheduling to all links, with a priority-based polling policy.Both the theoretical analysis and simulation results show that this proposed distributed scheduling algorithm is simple and efficient.     

A multiprocessor-oriented power-conscious scheduling algorithm for periodic tasks

With the rapid development of advanced technology in VLSI circuit designs, many processors could provide dynamic voltage scaling (DVS) to save power consumption when the supply voltage is allowed to be lower. In this paper, we propose a multiprocessor-oriented power-conscious scheduling algorithm for the real-time periodic tasks with task migration constrained scheme. We classify periodic tasks into fixed tasks and migration tasks, and limit the number of migration tasks and the number of destination processors which execute migration tasks. The proposed algorithm is made up of two steps. Firstly, choosing a processor to sort all of the periodic tasks in a non-increasing order according to task utilization, afterwards, allocating them to other processors. Secondly, scheduling the migration tasks with a virtual execution windows policy, and then scheduling the fixed tasks with EDF algorithm. The experiment results show that compared with arbitrary task migration policy and no task migration allowed policy, the power consumption in multiprocessor real-time periodic tasks scheduling is lowered significantly with the proposed algorithm.     

Control of wireless networks with flow level dynamics under constant time scheduling

We consider a network control problem for wireless networks with flow level dynamics under the general k-hop interference model. In particular, we investigate the control problem in low load and high load regimes. In the low load regime, we show that the network can be stabilized by a regulated maximal scheduling policy considering flow level dynamics if the offered load satisfies a constraining bound condition. Because maximal scheduling is a general scheduling rule whose implementation is not specified, we propose a constant-time and distributed scheduling algorithm for a general k-hop interference model which can approximate the maximal scheduling policy within an arbitrarily small error. Under the stability condition, we show how to calculate transmission rates for different user classes such that the long-term (time average) network utility is maximized. This long-term network utility captures the real network performance due to the fact that under flow level dynamics, the number of users randomly change so instantaneous network utility maximization does not result in useful network performance. Our results imply that congestion control is unnecessary when the offered load is low and optimal user rates can be determined to maximize users’ long-term satisfaction. In the high load regime where the network can be unstable under the regulated maximal scheduling policy, we propose a cross-layer congestion control and scheduling algorithm which can stabilize the network under arbitrary network load. Through extensive numerical analysis for some typical networks, we show that the proposed scheduling algorithm has much lower overhead than other existing queue-length-based constant-time scheduling schemes in the literature, and it achieves performance much better than the guaranteed bound.     

Scheduling Rate-Constrained Flows with Dynamic Programming Priority in Time-Triggered Ethernet

Time-triggered (TT), Rate-constrained (RC) and Best-eff ort (BE) traffics are included in Time-triggered ethernet (TTEthernet). For RC messages trans-mission is aff ected by TT messages, traditional schedul-ing policy cannot be well applied in TTEthernet. Dynamic programming priority (DPP) algorithm combines priority policy and dynamic programming algorithm for scheduling RC flows. The time slice for RC flows transmission is got by SMT solver YICES; RC flows are classified to different groups according to the priorities; Higher priority pack-ets in one time slice are scheduled using First input first output (FIFO) policy and lower priority packets are sched-uled by Dynamic programming policy. DPP policy guaran-tees different real-time requirements of heterogeneous RC flows, and make the best of time slice resource in aviation industries. The upper bound End-End of three methods and algorithm feasibility is analyzed. Simulation in avia-tion shows that DPP policy can obtain better real-time performance than other scheduling algorithms.     

一种分布式并行I/O中新型动态数据调度算法

在研究并行I／O数据调度策略的基础上，提出了一种应用于分布式计算系统中的二次调度自主维护负载平衡的动态I／O调度算法（DIO_TSMB），实验结果表明了算法的有效性，最后指出了并行I／O数据调度的发展趋势。     

Three Scheduling Schemes for Amplify-and-Forward Relay Environments

This letter deals with the scheduling in basic amplify-and-forward relay environments, which are characterized by one source and two destinations. The scheduling strategy is required in order to decide which node will be used as a relay. The proposed schemes are the classical Round Robin, and a channel-based scheduling policy which requires a partial feedback from the channel. This scheme is implemented in a centralized and a distributed version. Simulations and analytical results show that the distributed algorithm outperforms the Round Robin and is 3 dB worse than the centralized algorithm, due to possible collisions or outage scheduling. This algorithm provides the best trade-off between performance and complexity and is attractive for practical implementations     

基于任务生灭过程模型的边缘计算批处理调度算法分析与设计

移动边缘计算技术为低时延要求、资源敏感的计算任务需求提供解决方案,通过研究任务请求特征以提高调度算法效率是边缘计算的重要研究方向。不同于现有研究将任务请求特征建模为单一随机变量的做法,提出基于任务请求生灭过程模型的边缘计算架构,将求解最优调度决策的过程建模为无限期平均成本马尔可夫决策过程。在使用贝尔曼方程分析问题的过程中,利用任务的生灭特性对未来的请求到达做出估计以判断当前决策对未来系统时延能耗成本的影响,进而辅助确定当前状态的最优决策,并结合任务相关性感知提出批处理任务调度控制算法。所提算法根据生灭状态信息对策略迭代的状态空间和决策空间进行剪枝以降低策略改进的复杂度,突破了策略迭代算法的复杂度瓶颈。仿真结果表明,所提算法相较于传统的策略迭代算法具有明显的低复杂度优势,且能在不同系统条件下保持低时延、能耗成本。