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     

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。     

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。     

移动计算环境中数据广播的启发式遗传调度算法

数据广播调度是移动计算环境中数据管理技术的研究热点之一.现有的广播调度算法在移动数据库环境中缺乏一定的实用性.在数据项长度可变的条件下,理论上分析了多盘数据广播的平均访问时间,并提出了一种启发式的遗传调度算法,在广播调度中引入贪婪变换的启发式求解策略.理论分析和实验结果表明,该算法在访问概率偏斜率较大和数据项长度不等的情况下,平均访问时间较小,能很好的适应于移动数据库环境.     

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。     

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。     

移动计算环境中数据广播访问时间优化算法

移动计算是近年来新兴的一个研究热点，具有极大的市场潜力和需求，数据广播是提高移动计算系统可伸缩性的一项重要技术，本文对无线移动计算环境中数据广播的平均访问时间优化进行了研究和实验，首先分析了平均访问时间的理论最小值，然后提出了向理论最小值逼近的NASA 算法，实验表明NASA算法具有良好的性能，优于MDS等其他调度方法。     

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。     

A~*算法在数据广播中的应用与实现

在移动数据库的数据广播中,如何最大限度地降低数据项的平均期望访问时间是一个重要的研究方向.介绍了两种传统的Flat算法和VFk算法,并分析了它们的特点和不足.在以上两种算法的基础上,引入人工智能理论中的A*算法并加以改进,同时结合α-β剪枝技术,使之更适合于解决数据广播调度问题.通过仿真实验表明,在降低平均访问时间方面,A*算法是个不错的选择.     

多数据项广播调度策略

多数据项广播是移动计算环境中一种重要的数据访问方式.为减少用户的平均访问时间和响应最多事务请求,提出了一种新的多数据项广播调度策略.调度策略分为两个阶段,第一阶段根据各事务包含的数据项数目和对重复申请数据项的处理选择事务请求;第二阶段对已选择的事务请求用QEM算法进行调度.实验结果表明,它比现有的QEM算法有更高事务调度成功率和更短的平均访问时间.     

Information delivery through broadcasting in satellite communication networks

Satellite broadcast is an important candidate for large-scale multimedia information distribution due to the inherent wide-range multicasting capability of satellites and the asymmetry of satellite communications (high bandwidth downlink, limited bandwidth uplink) that matches nicely the information flow asymmetry in multimedia applications. We consider a data broadcasting model that is encountered in most asymmetric satellite communication environments. The problem of scheduling the data broadcast such that average response time experienced by the users is low is considered. In a push-based system, where the users cannot place requests directly to the server and the broadcast schedule should be determined based solely on the access probabilities, we formulate a deterministic dynamic optimization problem, the solution of which provides the optimal broadcast schedule. Properties of the optimal solution are obtained and then we propose a suboptimal dynamic policy which achieves average response time close to the lower bound. In a pull-based system where the users may place requests about information items directly to the server, the scheduling can be based on the number of pending requests for each item. Suboptimal policies with good performance are obtained in this case as well. If a user has local memory, it can alleviate its access latency by selectively prefetching the items from the broadcast and storing them in the memory. A good memory management strategy can substantially reduce the user's access latency. An optimal memory management policy is identified, that minimizes the expected aggregate latency. Memory update strategies with limited look-ahead are presented as implementable approximations of the optimal policy as well. We also consider the problem of joint broadcast scheduling and user's cache management and propos a joint optimization scheme which can achieve the performance up to 40% better than the existing non-joint approach.     

Online Scheduling Sequential Objects with Periodicity for Dynamic Information Dissemination

The scalability of data broadcasting has been manifested by prior studies on the base of the traditional data management systems where data objects, mapped to a pair of state and value in the database, are independent, persistent, and static against simple queries. However, many modern information applications spread dynamic data objects and process complex queries for retrieving multiple data objects. Particularly, the information servers dynamically generate data objects that are dependent and can be associated into a complete response against complex queries. Accordingly, the study in this paper considers the problem of scheduling dynamic broadcast data objects in a clients-providers-servers system from the standpoint of data association, dependency, and dynamics. Since the data broadcast problem is NP-hard, we derive the lower and the upper bounds of the mean service access time. In light of the theoretical analyses, we further devise a deterministic algorithm with several gain measure functions for the approximation of schedule optimization. The experimental results show that the proposed algorithm is able to generate a dynamic broadcast schedule and also minimize the mean service access time to the extent of being very close to the theoretical optimum.     

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。     

MULS: A General Framework of Providing Multilevel Service Quality in Sequential Data Broadcasting

In recent years, data broadcasting becomes a promising technique to design a mobile information system with power conservation, high scalability and high bandwidth utilization. In many applications, the query issued by a mobile client corresponds to multiple items which should be accessed in a sequential order. In this paper, we study the scheduling approach in such a sequential data broadcasting environment. Explicitly, we propose a general framework referred to as MULS (standing for MUlti-Level Service) for an information system. There are two primary stages in MULS: on-line scheduling and optimization procedure. In the first stage, we propose an On- Line Scheduling algorithm (denoted by OLS) to allocate the data items into multiple channels. As for the second stage, we devise an optimization procedure SCI, standing for Sampling with Controlled Iteration, to enhance the quality of broadcast programs generated by algorithm OLS. Procedure SCI is able to strike a compromise between effectiveness and efficiency by tuning the control parameters. According to the experimental results, we show that algorithm OLS with procedure SCI outperforms the approaches in prior works prominently in both effectiveness (i.e., the average access time of mobile users) and efficiency (i.e., the complexity of the scheduling algorithm). Therefore, by cooperating algorithm OLS with procedure SCI, the proposed MULS framework is able to generate broadcast programs with flexibility of providing different service qualities under different requirements of effectiveness and efficiency: in the dynamic environment in which the access patterns and information contents change rapidly, the parameters used in SCI will perform online scheduling with satisfactory service quality. As for the static environment in which the query profile and the database are updated infrequently, larger values of parameters are helpful to generate an optimized broadcast program, indicating the advantageous feature of MULS.     

On the Optimal Robot Routing Problem in Wireless Sensor Networks

In recent years, data broadcasting has become a promising technique to design a mobile information system with power conservation, high scalability, and high bandwidth utilization. In many applications, the query issued by a mobile client corresponds to multiple items that should be accessed in a sequential order. In this paper, we study the scheduling approach in such a sequential data broadcasting environment. Explicitly, we propose a general framework referred to as MULS (standing for MUltiLevel Service) for an information system. There are two primary stages in MULS: online scheduling (OLS) and optimization procedure. In the first stage, we propose an OLS algorithm to allocate the data items into multiple channels. As for the second stage, we devise an optimization procedure, called sampling with controlled iteration (SCI), to enhance the quality of broadcast programs generated by algorithm OLS. Procedure SCI is able to strike a compromise between effectiveness and efficiency by tuning the control parameters. According to the experimental results, we show that algorithm OLS with procedure SCI outperforms the approaches in prior works prominently in both effectiveness (that is, the average access time of mobile users) and efficiency (that is, the complexity of the scheduling algorithm). Therefore, by cooperating algorithm OLS with procedure SCI, the proposed MULS framework is able to generate broadcast programs with the flexibility of providing different service qualities under different requirements of effectiveness and efficiency: in the dynamic environment in which the access patterns and information contents change rapidly, the parameters used in SCI will perform OLS with satisfactory service quality. As for the static environment in which the query profile and the database are updated infrequently, larger values of parameters are helpful to generate an optimized broadcast program, indicating the advantageous feature of MULS.     

Disseminating dependent data in wireless broadcast environments

In wireless mobile environments, data broadcasting is an effective approach to disseminate information to mobile clients. In some applications, the access pattern of all the data can be represented by a weighted DAG. In this paper, we explore how to efficiently generate the broadcast schedule in a wireless environment for the data set having a weighted DAG access pattern. Such a broadcast schedule not only minimizes the access latency but also is a topological ordering of the DAG. Minimized access latency ensures the quality of service (QoS). We prove that it is NP-hard to find an optimal broadcast schedule and provide some heuristics. After giving an analysis for these heuristics on the latency and complexity, we implement all the proposed heuristics to compare their performance. Recommended by: Sunil Prabhakar