基于强化学习的SBS云应用自适应性能优化方法

自适应的调整云应用所占用的资源是一种有效的保障云应用性能的方法,但传统的决策方法面向基于服务的系统(Service-Based System,SBS)时会存在一些问题,例如基于应用系统性能模型的决策方法不能很好适应云环境下SBS的动态变化,基于智能优化算法的决策方法效率较低.该文提出了一种基于强化学习的SBS云应用自适应性能优化方法.在该方法中,该文建立了自适应基本要素之间相互关系的特征描述框架,利用高层次的系统行为指标(如响应时间、用户并发量、资源量等)来描述系统性能的优化目标等.为了应对云环境以及SBS的动态变化,该文的方法采用了无模型(model-free)的在线学习算法,当用户并发量发生变化导致系统的预期行为发生偏差时,该方法通过不断重复"执行-积累-学习-决策"的过程,可以不断的积累经验数据并优化决策结果.为了保证自适应优化的高效性,该文提出了一种引导算子,可以有效的缩小候选自适应动作的范围,提高算法的学习效率.该文实现了以一个SBS为例的原型框架,使用该框架的实验结果证明了该文提出方法的有效性.

Situation and Development Tendency of Indoor Positioning

This paper introduces the significance of indoor positioning and analyzes the related problems. The latest research on indoor positioning is introduced. Further, the positioning accuracy and the cost of typical local and wide area indoor positioning systems are compared. The results of the comparison show that Time & Code Division-Orthogonal Frequency Division Multiplexing (TC-OFDM) is a system that can achieve real-time meter-accuracy of indoor positioning in a wide area. Finally, in this paper, we indicate that the seamless high-accuracy indoor positioning in a wide area is the development trend of indoor positioning. The seamless Location Based Services (LBS) architecture based on a heterogeneous network, key technologies in indoor positioning for decimeter-accuracy and seamless outdoor and indoor Geographic Information System (GIS) are elaborated as the most important research fields of future indoor positioning.

Privacy-preserving Distributed Location Proof Generating System

The rapid development of location-based service(LBS) drives one special kind of LBS, in which the service provider verifies user location before providing services. In distributed location proof generating schemes, preventing users from colluding with each other to create fake location proofs and protecting user's location privacy at the same time, are the main technical challenges to bring this kind of LBS into practical. Existing solutions tackle these challenges with low collusion-detecting efficiency and defected collusion-detecting method. We proposed two novel location proof generating schemes, which inversely utilized a secure secret-sharing scheme and a pseudonym scheme to settle these shortcomings. Our proposed solution resists and detects user collusion attacks in a more efficient and correct way. Meanwhile, we achieve a higher level of location privacy than that of previous work. The correctness and efficiency of our proposed solution is testified by intensive security analysis, performance analysis, as well as experiments and simulation results.

Pseudo-Location Updating System for Privacy-Preserving Location-Based Services

With the rapid development of location-aware devices such as smart phones,Location-Based Services(LBSs) are becoming increasingly popular. Users can enjoy convenience by sending queries to LBS servers and obtaining service information that is nearby.However, these queries may leak the users' locations and interests to the un-trusted LBS servers, leading to serious privacy concerns. In this paper, we propose a Privacy-Preserving Pseudo-Location Updating System(3PLUS) to achieve k-anonymity for mobile users using LBSs. In 3PLUS, without relying on a third party, each user keeps pseudo-locations obtained from both the history locations and the encountered users, and randomly exchanges one of them with others when encounters occur. As a result, each user's buffer is disordered. A user can obtain any k locations from the buffer to achieve k-anonymity locally. The security analysis shows the security properties and our evaluation results indicate that the user's privacy is significantly improved.

Development of Re-usable C＋＋ classes for Location Aware Management and Control Systems for Shipping Applications

Location aware management and location based automation are fast upcoming technologies which are facilitated by availability of reliable, accurate, and cheap global positioning systems and location based services. This paper discusses the development of C＋＋ classes, which may be used for development of software for location aware management and control, specifically in merchant shipping, so as to provide improved safety, increased automation, prevention of pollution and reducing the work burden of the crew. The GPS is used in conjunction with the regionally accessible nested global shorelines （RANGS） dataset for this purpose. The design of two main C＋＋ classes named CShorelines and CGPSInput have been discussed. A control system mandatory on all oil-tankers above 150 GRT （and few other vessels） called ODMCS has been made location aware using the novel software developed and the results obtained have been critically examined and presented.

云计算环境中SBS应用动态资源分配方法

建立了一个SBS(基于服务的系统,service based system)应用端到端性能评价模型,并在该模型的基础上提出了SBS应用动态资源分配方法,该方法通过将静态的初始资源分配和动态的资源分配方案调整相结合,能够适应用户访问行为的动态变化,保证SBS应用端到端性能.在SBS应用动态资源分配问题求解中,提出了服务吞吐量约束确定算法并在此基础上给出了SBS应用动态资源分配算法,该算法根据SBS应用吞吐量约束确定服务资源需求量并在此基础上通过起始服务到服务间的转移时间计算服务的资源分配时刻,从而能够在保证SBS应用端到端性能约束的同时提高资源利用率.实验验证了所提出的基于SBS应用端到端性能评价模型的动态资源分配方法的有效性.