基于请求的多信道多数据项广播调度算法*

通过改进多信道单数据项请求的数据广播调度算法中的两层调度策略,提出了信道分配+QEM的算法;实现了用多信道广播来广播多数据项的请求;通过减少数据访问冲突和信道跳转减少了访问时间。分析证明此方法是可行和有效的。     

移动环境下多数据项请求的广播策略研究*

提出了一种移动环境下的多信道试探广播策略MCHM（multiple channel heuristic method）。该广播策略在多信道广播中采用高效的数据调度算法,在不重复广播的情况下,消除了多信道广播中多数据请求的访问冲突,大大减少了移动客户机的访问时间,提高了数据广播的性能。     

基于贪心策略的多信道数据广播调度算法

为减少多信道数据广播环境中的多信道平均延迟时间,提出一种基于贪心策略的多信道数据广播调度算法,将数据项合理地分配到各信道,最小化多信道数据项平均访问时间,在每个信道内采用近似最优的Log-time算法。实验结果表明,在不同的系统环境下,该算法都能够达到近似最优的性能。     

数据广播调度自适应信道划分与分配方法

随着移动网络的不断发展,移动终端设备的计算能力与日俱增,越来越多的用户倾向于通过移动网络获取信息资源,这使得实时按需数据广播面临新的挑战：（1）数据内容和规模的多样化;（2）用户请求的实时性与需求多样性使得热点数据增加,直接导致广播数据总量的剧增;（3）用户对服务质量和水平的要求越来越高.当前的研究成果主要集中在固定信道模型和算法上,一定程度上忽略了当前数据广播调度环境的变化.固定信道存在如下问题：（1）局限于特定的网络,缺乏通用性;（2）信道大小、个数不能随着网络环境的变化而自动调整,降低了广播效率.基于以上考虑,对实时按需数据广播调度的自适应信道划分和分配进行研究,提出一种自适应信道划分与分配方法OCSM （optimized channel split method）,其根据数据请求特征的不同,实时自适应地调整信道个数和大小,从而提高系统敏感性、鲁棒性以及广播效率.该方法包括：（1）广播数据均衡聚类算法WASC （weight average and size clusteralgorithm）,其挖掘数据特征,为信道划分提供依据;（2）数据项广播优先级评定算法R×W/SL,其实时评定数据项调度优先级;（3）信道划分与分配算法CSA （channel split algorithm）.实验包括两个方面：（1）确定不同数据项大小和请求截止期分布下的信道划分策略,并分析聚类算法中聚类距离K在不同情况下的最佳取值以及最佳信道划分;（2）验证自适应信道划分与分配策略的有效性,并通过对比实验验证在不同情况下OCSM的有效性.实验结果表明：OCSM优于其他调度算法,并具有较强的自适应.     

多数据项请求的多信道并行广播调度算法

为减少数据广播中用户请求的平均访问时间、提高广播性能,提出了一种新的基于减少数据访问冲突和应用重复广播技术的广播调度算法。该算法有效减少了多信道并行广播中多数据项请求的访问冲突,对热点数据项采取重复广播技术,极大地减少了对热点数据项请求的访问时间。仿真实验结果表明,该算法有效地降低了平均访问时间,提高了数据广播性能,特别是在访问概率偏斜率较大时具有更好的性能。     

无线数据广播中变长数据项偏斜调度算法

针对数据广播中用户访问时间最小化的问题,提出一种变长数据项的偏斜广播调度脱机算法。该算法在数据广播的平方根规则基础上,确定每个数据项的广播频率和在信道上的平均实例间隔,以此为依据选择生成周期广播序列。实验结果表明,该算法获得的平均访问时间接近于理论最优值,平均误差率在6%左右。     

移动计算环境中的自适应混合广播

提出一种自适应混合广播算法。在周期广播数据分配时采用多信道非均匀分配,使高访问率的数据获得高广播频率。在确定周期广播数据和联机请求数据个数时考虑联机请求信道响应时间和访问率之间的关系,从而在少量比较次数后获得数据最佳分割点。实验结果表明,该算法可以根据系统负载和用户访问模式的变化动态调节信道和数据的分配,性能优于纯广播和纯基于请求的广播,访问时间少于现有的混合数据广播方式。     

非对称网络环境中数据广播的启发式多盘调度算法

在以无线网络为代表的非对称网络环境中,数据广播是一种有效的数据访问方式。针对非均匀的访问概率分布,我们分析了数据广播访问时间的最优值,并提出了一种启发式多盘调度算法（ＨＭＤ）,该算法能够根据给定的数据项访问概率分布,自动生成广播调度。 欠的理论分析和实验结果表明,ＨＭＤ算法是一种高效的数据广播调度算法,具有接近于理论最优值的性能,并且具有良好的可操作性。     

一种基于动态窗口的虚拟信道通用调度算法

针对虚拟信道调度算法的通用设计问题,设计了独占式轮转和顺序式轮转两种全同步调度算法、抢占式优先和非抢占式优先两种全异步调度算法、以及独占式混合和顺序式混合两种同步/异步混合调度算法,进一步实现了基于动态窗口的虚拟信道通用调度算法,通用算法采用双层调度模型,通过参数配置可实现8种调度策略。实践表明,通用算法既能满足同步数据固定时隙要求,又能适应异步数据动态调整要求,还能满足应急数据及时发送要求,窗口边界和信道边界可动态调整,减少了信道资源浪费,具有广泛通用性和良好适应性。     

无线网络冲突感知广播调度算法*

文章在协议干扰模型下,通过证明、计算、仿真实验等方法,提出了一个冲突感知广播调度算法（简称CBS算法）,以解决无线网络中的广播调度延迟问题。研究结果表明,CBS算法能够保证节点在广播调度过程中不会相互干扰,避免冲突产生;只需发送少量消息,就能完成网络广播调度;能够同时调度广播树中多个层的节点进行传输,有效地提高了网络信道的利用率。仿真实验结果表明,CBS算法能在较短的时间内完成广播调度,有效地降低了广播延迟。     

移动实时环境下一种改进的广播调度算法

在网络带宽不对称的移动实时环境中,数据广播是一种有效的数据访问方式。针对这种网络特性,分析了现今已经存在的某些广播调度算法。针对UFO算法,分别提出了SBS算法和CRS算法,它们从服务器、移动客户端两个方面进行了改进。两种算法可以根据给定的数据项访问概率分布,自动生成广播调度。通过理论分析和实验结果表明,该算法不会产生事务重启,并且可以有效减少数据的访问时间,使用户访问数据广播的平均等待时间最小。     

Scheduling time‐critical requests for multiple data objects in on‐demand broadcast

On‐demand broadcast is an effective data dissemination approach in mobile computing environments. Most of the recent studies on on‐demand data broadcast assume that clients request only a single‐data‐object at a time. This assumption may not be practical for the increasingly sophisticated mobile applications. In this paper, we investigate the scheduling problem of time‐critical requests for multiple data objects in on‐demand broadcast environments and observe that existing scheduling algorithms designed for single‐data‐object requests perform unsatisfactorily in this new setting. Based on our analysis, we propose new algorithms to improve the system performance. Copyright © 2010 John Wiley & Sons, Ltd.     

数据广播环境下实时查询处理

现有的实时数据广播研究中,只考虑了具有截止期约束的单个数据请求的调度问题,而支持实时查询处理的数据广播技术尚未得到足够的关注。该文研究在on-demand数据广播环境下,如何有效地处理实时查询问题,提出一种新的数据调度算法(QSA)。通过仿真实验与目前最为有效的数据请求调度算法SIN进行了比较,结果显示QSA具有更低的错过截止期比率,在最好的情况下,QSA比SIN降低了17.45%。     

On-demand broadcast for multiple-item requests in a multiple-channel environment

On-demand broadcast is an effective approach to disseminating data in mobile computing environments. Substantial efforts have been devoted to improving the scheduling efficiency of on-demand broadcast. Previous studies focused mainly on the case of scheduling single-item requests in single-channel environments. However, requesting multiple dependent data items is common in many advanced applications such as electronic stock trading and traffic information enquiry services. In addition, multi-channel architectures are widely deployed in data broadcast systems. In this work, we investigate the issues arising in scheduling multi-item requests in multi-channel on-demand broadcast environments. Two problems, namely, the request starvation problem and the bandwidth utilization problem are identified in existing algorithms. To tackle the observed problems, an innovative algorithm is proposed. Results from our simulation study demonstrate the superiority of the proposed algorithm.     

非对称网络环境中数据广播的带索引多盘调度算法

在以无线网络为代表的非对称网络环境中,数据广播是一种有效的数据访问方式。针对非均匀的访问概率分布,为了节约移动计算机接听数据广播的电源消耗,同时兼顾访问时间的限制,文中提出了两种带索引多盘调度算法,即复制索引算法（ＩＭＤ－ＲＩ）和多路复用算法（ＩＭＤ－ＭＵＸ）。理论分析和实验结果表明,这两种算法通过在广播信息中加入索引信息,能够有效减少数据广播的调谐时间,同时仍保持较低的访问时间,因此更具有竞争性     

无人机平台非对称广播链路的动态优先权调度算法

战场数据分发系统需要传输不同长度的信息和数据,数据容量相差很大,采用动态优先权调度算法在系统平均延时、长短信息传输的公平性两个方面可以同时达到很好的性能。该调度算法适合于有优先级控制的广播链路。     

Time-critical on-demand data broadcast: algorithms, analysis, and performance evaluation

On-demand broadcast is an effective wireless data dissemination technique to enhance system scalability and deal with dynamic user access patterns. With the rapid growth of time-critical information services in emerging applications, there is an increasing need for the system to support timely data dissemination. This paper investigates online scheduling algorithms for time-critical on-demand data broadcast. We propose a novel scheduling algorithm called SIN-/spl alpha/ that takes the urgency and number of outstanding requests into consideration. An efficient implementation of SIN-/spl alpha/ is presented. We also analyze the theoretical bound of request drop rate when the request arrival rate rises toward infinity. Trace-driven experiments show that SIN-/spl alpha/ significantly outperforms existing algorithms over a wide range of workloads and approaches the analytical bound at high request rates.     

On the performance of real-time multi-item request scheduling in data broadcast environments

On-demand broadcast is an effective wireless data dissemination technique to enhance system scalability and capability to handle dynamic data access patterns. Previous studies on time-critical on-demand data broadcast were conducted under the assumption that each client requests only one data item at a time. With the rapid growth of time-critical information dissemination services in emerging applications, there is an increasing need for systems to support efficient processing of real-time multi-item requests. Little work, however, has been done. In this paper, we study the behavior of six representative single-item request based scheduling algorithms in time-critical multi-item request environments. The results show that the performance of all algorithms deteriorates when dealing with multi-item requests. We observe that data popularity, which is an effective factor to save bandwidth and improve performance in scheduling single-item requests, becomes a hindrance to performance in multi-item request environments. Most multi-item requests scheduled by these algorithms suffer from a starvation problem, which is the root of performance deterioration. Based on our analysis, a novel algorithm that considers both request popularity and request timing requirement is proposed. The performance results of our simulation study show that the proposed algorithm is superior to other classical algorithms under a variety of circumstances.     

Scheduling on-demand broadcast with timing constraints

In a distributed system, broadcasting is an efficient way to dispense data in certain highly dynamic environments. While there are several well-known on-line broadcast scheduling strategies that minimize wait time, there has been little research that considers on-demand broadcasting with timing constraints. One application which could benefit from a strategy for on-demand broadcast with timing constraints is a real-time database system. Scheduling strategies are needed in real-time databases that identify which data item to broadcast next in order to minimize missed deadlines. The scheduling decisions required in a real-time broadcast system allow the system to be modeled as a Markov Decision Process (MDP). In this paper, we analyze the MDP model and determine that finding an optimal solution is a hard problem in PSPACE. We propose a scheduling approach, called Aggregated Critical Requests (ACR), which is based on the MDP formulation and present two algorithms based on this approach. ACR is designed for timely delivery of data to clients in order to maximize the reward by minimizing the deadlines missed. Results from trace-driven experiments indicate the ACR approach provides a flexible strategy that can outperform existing strategies under a variety of factors.