A middleware framework for application-aware and user-specific energy optimization in smart mobile devices

Mobile battery-operated devices are becoming an essential instrument for business, communication, and social interaction. In addition to the demand for an acceptable level of performance and a comprehensive set of features, users often desire extended battery lifetime. In fact, limited battery lifetime is one of the biggest obstacles facing the current utility and future growth of increasingly sophisticated “smart” mobile devices. This paper proposes a novel application-aware and user-interaction aware energy optimization middleware framework (AURA) for pervasive mobile devices. AURA optimizes CPU and screen backlight energy consumption while maintaining a minimum acceptable level of performance. The proposed framework employs a novel Bayesian application classifier and management strategies based on Markov Decision Processes and Q-Learning to achieve energy savings. Real-world user evaluation studies on Google Android based HTC Dream and Google Nexus One smartphones running the AURA framework demonstrate promising results, with up to 29% energy savings compared to the baseline device manager, and up to 5×savings over prior work on CPU and backlight energy co-optimization.     

Practical minimization of pairwise-covering test configurations using constraint programming

Context: Testing highly-configurable software systems is challenging due to a large number of test configurations that have to be carefully selected in order to reduce the testing effort as much as possible, while maintaining high software quality. Finding the smallest set of valid test configurations that ensure sufficient coverage of the system’s feature interactions is thus the objective of validation engineers, especially when the execution of test configurations is costly or time-consuming. However, this problem is NP-hard in general and approximation algorithms have often been used to address it in practice.Objective: In this paper, we explore an alternative exact approach based on constraint programming that will allow engineers to increase the effectiveness of configuration testing while keeping the number of configurations as low as possible.Method: Our approach consists in using a (time-aware) minimization algorithm based on constraint programming. Given the amount of time, our solution generates a minimized set of valid test configurations that ensure coverage of all pairs of feature values (a.k.a. pairwise coverage). The approach has been implemented in a tool called PACOGEN.Results: PACOGEN was evaluated on 224 feature models in comparison with the two existing tools that are based on a greedy algorithm. For 79% of 224 feature models, PACOGEN generated up to 60% fewer test configurations than the competitor tools. We further evaluated PACOGEN in the case study of an industrial video conferencing product line with a feature model of 169 features, and found 60% fewer configurations compared with the manual approach followed by test engineers. The set of test configurations generated by PACOGEN decreased the time required by test engineers in manual test configuration by 85%, increasing the feature-pairs coverage at the same time.Conclusion: Our experimental evaluation concluded that optimal time-aware minimization of pairwise-covering test configurations is efficiently addressed using constraint programming techniques.     

基于分解的二维指数交叉熵图像阈值分割

尽管用对数定义的Shannon熵是测度信息不确定性的有效方法,但存在无定义值和零值的问题,且现有的二维Shannon交叉熵法其运行速度仍有提升空间.为此,提出了一维和二维指数交叉熵阈值分割算法.首先给出了指数交叉熵的定义,并导出了一维指数交叉熵阈值选取方法;然后将其推广提出了基于分解的二维指数交叉熵阈值分割算法.通过分别求原像素灰度级图像和邻域平均灰度级图像的一维指数交叉熵最佳阈值,并将其组合求解二维指数交叉熵最佳阈值,从而将二维运算转换到两个一维空间上,大大缩小了搜索空间,使计算复杂度由0(L4)降为0(L).实验结果表明,与最近提出的二维Shannon交叉熵法及二维Tsaliis交叉熵法相比,所提出的方法能够得到更为优越的分割效果,且运行时间大幅减少.     

最小状态变元平均奖赏的强化学习方法

针对采用折扣奖赏作为评价目标的Q学习无法体现对后续动作的影响问题,提出将平均奖赏和Q学习相结合的AR-Q-Learning算法,并进行收敛性证明.针对学习参数个数随着状态变量维数呈几何级增长的"维数灾"问题,提出最小状态变元的思想.将最小变元思想和平均奖赏用于积木世界的强化学习中,试验结果表明,该方法更具有后效性,加快算法的收敛速度,同时在一定程度上解决积木世界中的"维数灾"问题.     

Alert based disaster notification and resource allocation

When a disaster occurs, timely actions in response to urgent requests conveyed by critical messages (known as alerts) constitute a vital key to effectiveness. These actions include notifying potentially affected parties so that they can take precautionary measures, gathering additional information, and requesting remedial actions and resource allocation. However, there are different types of disasters such as epidemic outbreaks, natural disasters, major accidents, and terrorist attacks. At the same time, there are also many different parties involved such as governments, healthcare institutions, businesses, and individuals. To address these problems, we introduce a Disaster Notification and Resource Allocation System (DNRAS) based on an Alert Management System (AMS) implemented through Web services. This unified platform supports timely interactions among various parties, focusing on notification and monitoring, resource enquiry and allocation, as well as the mobility of information. We detail the mechanisms of these functions in our system, illustrating the Web services interface parameters for communications and interoperability. We illustrate the applicability of our approach with an example of an epidemic outbreak and discuss the advantage of our approach with respect to various stakeholders of our system.     

一种轻量级的Android内核保护监控器

为阻止针对Android系统内核的攻击,保护Android系统内核不被破坏,设计一种基于ARM平台轻量级的hypervisor监控器架构。该架构利用ARM硬件虚拟化扩展技术,对不信任模块进行隔离,阻止模块中恶意代码对内核的破坏,保护关键对象不被篡改,并基于hypervisor、利用交叉视图检测rootkit。实验结果表明,该架构能及时阻止对被监控对象的修改,快速检测出rootkit威胁,减少系统被攻击后带来的损失。     

普适计算中基于移动代理的应用follow me迁移技术*

为了更好地利用普适计算环境中的计算资源、减少人为干预、提高工作效率,提出了应用的follow-me迁移需求,即计算可以随着用户的移动在异构设备和计算环境中移动,让移动中的用户能够体验到不间断的计算。提出了基于移动代理的普适计算中间件FollowMeAgent。结合情境感知技术,利用软件代理所具备的移动性实现应用的自主迁移,并根据应用特点对迁移策略进行了细分。在此平台上开发了若干应用,通过性能测试证明其可行性和有效性。     

基于复杂问题定义模型的复杂问题求解*

复杂问题求解往往涉及多个知识领域,其对应的工作流和知识流也非常复杂.为了促进后续问题求解过程的可靠性和针对性,利用现有的形式化和非形式化的方法,提出了一种复杂问题定义模型,并在此基础上分析了复杂问题求解过程中的知识流和工作流.结合大型软件项目开发,提出了复杂问题求解的参考步骤.     

基于敏感数据加密的分布式安全数据库服务研究*

针对目前基于数据加密技术的安全数据库服务不能有效平衡数据处理性能与数据隐私保护的不足,提出一种新的基于分布式安全数据库服务的隐私保护方法,通过引入准标志属性集的自动检测技术,采用对部分敏感属性加密和分解准标志属性集的方式实现数据的垂直分解,通过基于元数据的查询分解实现分布式查询处理。实验结果表明,该方法能较好地平衡查询性能与隐私保护之间的矛盾。     

基于摘要的内存泄露分析方法改进*

为了更加精确高效地分析源代码中的内存泄露,针对内存泄露属性分析建模,通过多种途径对现有的方法进行改进。使用函数摘要的方法提高分析效率,并使用对库函数建模、检测虚假路径的方法降低误报,同时尝试检测动态内存通过参数逃逸的情况。在crystal基础上实现了静态分析内存泄露的工具SMD,取得了良好的实验结果。