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。     

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.     

An energy efficient re-access scheme for data caching in data broadcast of a mobile computing environment

Data caching is used to improve the response time and the power consumption of a mobile client in a mobile computing environment. To enhance the performance of data caching, one needs to improve the hit ratio and to reduce the cost in processing a cache miss. In a mobile computing environment, a cached data item of a mobile client needs to remain up-to-date with respect to its corresponding data item in the server. A cached data item which is out of date is called a cached invalidated data item. Accessing a cached invalidated data item can be regarded as processing a cache miss. To access a cached invalidated data item, a mobile client needs to download the new content of the data item from the broadcast channel. This operation is called a re-access operation in this paper. Re-accessing a cached invalidated data item incurs large tuning time overhead. In this paper, we propose a re-access scheme that reduces this overhead by allowing a mobile client to access a cached invalidated data item from the broadcast channel without accessing indices. We analyze the performance of the proposed scheme and validate the analysis through experiments. The experiments showed that the proposed scheme significantly reduces the tuning time of a mobile client. Furthermore, the proposed scheme is robust in the sense that it allows changes on the broadcast structure in data broadcasting.     

Dynamic priority scheduling of periodic queries in on-demand data dissemination systems

As an important wireless data broadcast technique, on-demand broadcast has been widely used for dynamic and large-scale data dissemination. An important class of emerging data broadcast applications requires monitoring multiple data items continuously in order to support data-driven decision making. Since wireless bandwidth is a precious shared medium, an important problem to solve is how to disseminate data to periodic queries, so that all the requests can be satisfied while the bandwidth consumption is minimized. In this paper, we first propose a new real-time scheduling algorithm called EDFS, which is a variant of the classic EDF [24] algorithm. Based on EDFS, we propose a novel on-line broadcast scheduling algorithm, called EDFS-BS. To our best knowledge, EDFS-BS is the first dynamic priority based broadcast scheduling algorithm that can be utilized to satisfy the timing constraints of periodic queries. We also propose a bandwidth utilization based schedulability test for EDFS-BS, which is used to ensure timing predictability of a periodic query set. Extensive experiments have been conducted to compare EDFS-BS versus existing solutions with comparable quality. The results show that EDFS-BS outperforms them considerably in terms of wireless bandwidth consumption and query service ratio.     

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.     

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。     

Tree-based data retrieval algorithm for multi-item request with deadline in wireless networks

The data retrieval problem in wireless communication has attracted increasing attentions in many applications such as electronic information sharing and e-business. However, most existing studies do not consider how to retrieve the requested data items in their deadlines. In this paper, we discuss the so-called deadline-based multi-item data retrieval problem, which aims at finding an access pattern for retrieving requested data items in certain deadlines such that the access latency and data miss rate are minimized. We propose two algorithms, named EDFS and UR, to deal with the above problem. The EDFS algorithm gives priority to retrieve the requested data item with the least deadline, while the UR algorithm introduces the urgent rate that is computed by the broadcast time and deadline of requested data item for further reducing the access latency and data miss ratio. Moreover, we design a tree-based data retrieval algorithm that finds the longest downloading sequence of data items in one broadcast cycle and is applied to EDFS and UR for finding data retrieval sequence of requested data items. The efficiency of our proposed schemes has been validated by the extensive experiments.     

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.     

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。     

An efficient nonuniform index in the wireless broadcast environments

Data broadcast is an efficient dissemination method to deliver information to mobile clients through the wireless channel. It allows a huge number of the mobile clients simultaneously access data in the wireless environments. In real-life applications, more popular data may be frequently accessed by clients than less popular ones. Under such scenarios, Acharya et al.’s Broadcast Disks algorithm (BD) allocates more popular data appeared more times in a broadcast period than less popular ones, i.e., the nonuniform broadcast, and provides a good performance on reducing client waiting time. However, mobile devices should constantly tune in to the wireless broadcast channel to examine data, consuming a lot of energy. Using index technologies on the broadcast file can reduce a lot of energy consumption of the mobile devices without significantly increasing client waiting time. In this paper, we propose an efficient nonuniform index called the skewed index, SI, over BD. The proposed algorithm builds an index tree according to skewed access patterns of clients, and allocates index nodes for the popular data more times than those for the less popular ones in a broadcast cycle. From our experimental study, we have shown that our proposed algorithm outperforms the flexible index and the flexible distributed index.     

Effective Generation of Data Broadcast Schedules with Different Allocation Numbers for Multiple Wireless Channels

Existing methods of scheduling data items over multiple wireless broadcast channels focus on the assignment of a data item to a channel. However, data items are not allocated more than once per broadcast cycle to a single channel. Our scheme considers the numbers of copies of a data item that should be allocated in the context of the channel assignment problem and aims to reduce the average data access time by allocating a popular data item more than once per cycle to the channel to which it is assigned. The number of times that each data item is allocated reflects its access probability. Simulation results show that our scheme reduces the average expected delay, especially when there are few channels.     

基于上下文项目评分分裂的协同过滤推荐

上下文感知推荐系统的主要任务是利用上下文信息进一步提高推荐系统的推荐精度和用户满意度。提出了一种基于上下文项目评分分裂的推荐方法。该方法首先依据项目分裂判别标准对多维度上下文信息下的项目进行分裂,然后根据分裂结果并通过上下文维度进行聚类。在此基础上,利用协同过滤推荐算法进行未知评分预测。最后,面向不同的项目分裂标准,在LDOS-CoMoDa数据集上进行仿真对比实验。实验结果表明,相对于其他推荐算法,该方法有效提升了推荐精度,达到了提高推荐质量效果的目的。     

Exploiting data mining techniques for broadcasting data in mobile computing environments

Mobile computers can be equipped with wireless communication devices that enable users to access data services from any location. In wireless communication, the server-to-client (downlink) communication bandwidth is much higher than the client-to-server (uplink) communication bandwidth. This asymmetry makes the dissemination of data to client machines a desirable approach. However, dissemination of data by broadcasting may induce high access latency in case the number of broadcast data items is large. We propose two methods aiming to reduce client access latency of broadcast data. Our methods are based on analyzing the broadcast history (i.e., the chronological sequence of items that have been requested by clients) using data mining techniques. With the first method, the data items in the broadcast disk are organized in such a way that the items requested subsequently are placed close to each other. The second method focuses on improving the cache hit ratio to be able to decrease the access latency. It enables clients to prefetch the data from the broadcast disk based on the rules extracted from previous data request patterns. The proposed methods are implemented on a Web log to estimate their effectiveness. It is shown through performance experiments that the proposed rule-based methods are effective in improving the system performance in terms of the average latency as well as the cache hit ratio of mobile clients.     

基于加权二部图的个性化推荐算法

针对网络推断(NBI)算法的二部图实现算法忽略二部图权重而导致实际评分值高的项目没有得到优先推荐这一问题,提出加权网络推断(WNBI)算法的加权二部图实现算法。该算法以项目的评分作为二部图中用户与项目的边权,按照用户-项目间边权占该节点权重和的比例分配资源,从而实现评分值高的项目得到优先推荐。通过在数据集MovieLens上的实验表明,相比NBI算法,WNBI算法命中高评分值项目数目增多,同时在推荐列表长度小于20的情况下,命中项目的数量和命中高评分项目数量均有明显增加。     

Optimized Periodic Broadcast of Nonlinear Media

Conventional video consists of a single sequence of video frames. During a client's playback period, frames are viewed sequentially from some specified starting point. The fixed frame ordering of conventional video enables efficient scheduled broadcast delivery, as well as efficient near on-demand delivery to large numbers of concurrent clients through use of periodic broadcast protocols in which the video file is segmented and transmitted on multiple channels. This paper considers the problem of devising scalable protocols for near on-demand delivery of “nonlinear” media files whose content may have a tree or graph, rather than linear, structure. Such media allows personalization of the media playback according to individual client preferences. We formulate a mathematical model for determination of the optimal periodic broadcast protocol for nonlinear media with piecewise-linear structures. Our objective function allows differing weights to be placed on the startup delays required for differing paths through the media. Studying a number of simple nonlinear structures we provide insight into the characteristics of the optimal solution. For cases in which the cost of solving the optimization model is prohibitive, we propose and evaluate an efficient approximation algorithm.      

A scalable low-latency cache invalidation strategy for mobile environments

Caching frequently accessed data items on the client side is an effective technique for improving performance in a mobile environment. Classical cache invalidation strategies are not suitable for mobile environments due to frequent disconnections and mobility of the clients. One attractive cache invalidation technique is based on invalidation reports (IRs). However, the IR-based cache invalidation solution has two major drawbacks, which have not been addressed in previous research. First, there is a long query latency associated with this solution since a client cannot answer the query until the next IR interval. Second, when the server updates a hot data item, all clients have to query the server and get the data from the server separately, which wastes a large amount of bandwidth. In this paper, we propose an IR-based cache invalidation algorithm, which can significantly reduce the query latency and efficiently utilize the broadcast bandwidth. Detailed analytical analysis and simulation experiments are carried out to evaluate the proposed methodology. Compared to previous IR-based schemes, our scheme can significantly improve the throughput and reduce the query latency, the number of uplink request, and the broadcast bandwidth requirements.     

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.     

基于优先级的数据广播内容选择算法

在以无线网络为代表的移动计算环境中,数据广播是一种有效的数据访问方式。为响应最多用户数据请求,提出了优先级计算模型,进而提出了一种基于优先级的广播内容选择算法。该算法综合考虑了事务存取多个数据项和满足定时限制的要求,根据用户请求队列状态动态选择广播内容,并应用剪枝机制减少了选择开销。实验结果表明它比现有算法有明显的优越性。     

移动环境中请求多数据项的广播调度算法

提出移动环境中请求多数据项的广播调度算法——基于权重的调度算法(BWS)和权重比截止时间算法(WID)。BWS算法根据数据项对客户的满足情况确定权重,并以数据项的总权重作为调度的依据,同时考虑数据项的使用频率和数据项对于客户的满足情况。WID算法以总权重与截止时间的比值作为调度依据,同时考虑广播效率和紧急性的要求。在数据广播调度方面这2种算法比传统的算法具有更好的性能。     

多盘广播的偏斜索引算法

在移动环境中,多盘广播是被广泛使用的数据广播调度算法之一。为广播数据建立索引可以使移动客户机选择性地监听信道,以减少电源消耗,但过多地插入索引会增加数据访问时间。针对该问题,为多盘广播建立偏斜索引,设计索引树构造算法和广播数据的索引树算法。与可变扇出算法相比,多盘广播的偏斜索引算法需要的平均访问时间和平均调谐时间较短。