Provisioning of power event APIs as a mobile OS facility

Monitoring various hardware and software events for energy consumption is essential for energy management in mobile devices. However, current mobile operating systems (OS) lack monitoring functionality and do not provide sufficient information of this kind. In this paper, we propose PEMOS (Power Events Monitor for Mobile Operating Systems), a framework for power event APIs for mobile devices, that provides a wide spectrum of energy-related information, enabling in-depth analysis of energy problems. PEMOS provides a set of well-defined APIs as a mobile OS facility, defining various energy-related system events as power events. These are classified into system events and application events, encompassing extensive and fine-grained power-related events. Benefits of PEMOS include extensive coverage of power events, high portability across various platforms, and efficient API implementation. The framework structure is portable across multiple devices, and the standard ioctl-based API implementation enables the same operations on different devices without system modification. We implemented PEMOS on the Android platform to evaluate its efficacy and usefulness. The experimental results and case studies confirm that PEMOS is effective and useful for a range of energy management systems, with minimal overhead.     

基于数据分析的楼宇智能节电管理系统

城市中高层楼宇增加是城市内用电量上升的重要因素之一。使用计算机系统对建筑执行能耗量化管理从而控制降低建筑运营过程中所消耗的能量,降低建筑的运营成本,提高能源使用效率,已经成为用户最为关注的问题。已有的楼宇节电管理系统收集建筑内部的信息并通过启发式规则对楼宇的照明、动力、通风、空调、安防等系统进行协调控制及整合,以期达到节能的目的。这类型系统的缺点在于（1）大部分楼宇缺少专职的节电管理机构,没有相关的电能使用及管理的方法。在各个楼宇与电力公司之间还没有形成一个有效的信息共享链,内外信息共享能力差,存在信息孤岛现象（2）系统中的节电模型没有学习过程,不能根据建筑情况的变化,自动调整节电模式。面对这样的情况,该文提出一个基于数据分析的建筑楼宇智能节电系统,该系统（1）使用正态分布拟合用户用能习惯,分析得到用户能耗较高的关键设备,发现设备的耗能过高原因;（2）建立双向交换子系统实现楼宇与电力公司之间的信息交换,并根据电力公司生产计划及时调整楼宇用能策略。在天津动漫大厦实际运行结果显示,该文设计的智能节电系统可以有效的节省楼宇中的电力损耗。     

Exploring the impact of trust information visualization on mobile application usage

Trust allows people to live in a risky and uncertain situation by providing the means to decrease complexity. It is the key to decision making and engaging in usage. Visualizing trust information could thus leverage usage behavior and decisions. This article explores the impact of trust information visualization on mobile application usage with a three-stage experiment conducted in both Finland and China (1) by studying users’ opinions on the importance of mobile applications, (2) by evaluating the impact of a trust indicator on mobile application usage, and (3) by evaluating the impact of a trust/reputation indicator on mobile application usage. Although the results achieved in this study for Finland and China showed small differences on usage willingness and remarkable difference on trust information check willingness, both countries indicated that visualizing the reputation value of an application and/or the individual trust value of a user can assist in mobile application usage with different importance rates. In addition, the article discusses possible reasons for the difference in impact in Finland and China, other impact factors related to mobile application usage, and implications of our experiments with regard to a trust management system for mobile applications.     

Context-aware recommendations through context and activity recognition in a mobile environment

The mobile Internet introduces new opportunities to gain insight in the user’s environment, behavior, and activity. This contextual information can be used as an additional information source to improve traditional recommendation algorithms. This paper describes a framework to detect the current context and activity of the user by analyzing data retrieved from different sensors available on mobile devices. The framework can easily be extended to detect custom activities and is built in a generic way to ensure easy integration with other applications. On top of this framework, a recommender system is built to provide users a personalized content offer, consisting of relevant information such as points-of-interest, train schedules, and touristic info, based on the user’s current context. An evaluation of the recommender system and the underlying context recognition framework shows that power consumption and data traffic is still within an acceptable range. Users who tested the recommender system via the mobile application confirmed the usability and liked to use it. The recommendations are assessed as effective and help them to discover new places and interesting information.     

User satisfaction aware routing and energy modeling of polymorphic network on chip architecture

In mobile devices, multiple applications contend for limited resources in the underlying embedded system framework. Application resource requirements in mobile systems vary by computation needs, energy consumption and user interaction frequency. Quality of service (QoS) is the predominant metric of choice to manage resources among contending applications. Resource allocation policies to support static QoS for applications do not reflect the changing demands of the user in contemporary network on chip (NoC) based embedded architectures. User satisfaction with the user interactions and user interface design ought to be the primary design driver. Some recent research has integrated a saturating, non-linear user satisfaction function in the application thread scheduler. The application and operating system level user satisfaction research assumes that the throughput of inter-thread edges is limited only by the computational constraints of the nodes. With NoC, however, NoC resource allocation policies play an important role in determining the inter-thread communication flow’s throughput and the resulting application level user satisfaction. In this paper, we filter down the user satisfaction from an application layer attribute to a router level attribute to improve the resource and energy utilization for routing in order to leverage the user satisfaction at the application and system level. We demonstrate that this technique improves the user satisfaction of audio (MP3) application by 10% while maintaining the user satisfaction of video (MPEG-2) application. Experiments also show that a fixed energy source can be extended for an average of 18% of the time using the NoC user satisfaction based energy optimization proposed in this research.     

基于硬件运行时间的Android应用非线性能耗模型

Android应用能耗分析一直是移动应用测试的重要组成部分,通过对移动应用和底层移动终端硬件特性的分析,提出一种基于硬件运行时间的非线性能耗模型,与使用复杂但精度高的硬件测量能耗相比,该模型将单个硬件在不同状态的能耗列为基本能耗单元,然后以此为基础结合时间变量刻画终端的功耗,由于运行时间容易精确获取和测量,从而能够快速估算应用程序运行时所产生的能耗。实验结果表明,通过此模型进行的能耗估算结果与通过硬件测量得到的能耗相比平均误差不超过6%,能够为用户快速检测应用程序所消耗的电量提供有效评估。     

Factors affecting reposting behaviour using a mobile phone-based user-generated-content online community application among Chinese young adults

Mobile phone-based user-generated-content (UGC) online community applications have gained increasing popularity among young generations. However, factors that may affect usage behaviour regarding the applications are not fully investigated. In this study, we employed the Technology Acceptance Model as the basis to explore factors that are able to predict user reposting behaviour with the applications. University students (N?=?322) completed a self-reported questionnaire for measuring the studied constructs after they experienced a high-fidelity prototype of a mobile UGC online community application. Results from path analysis demonstrated that perceived usefulness and attitude towards usage were significant determinants of user reposting intention, with 23% of its variance explained. Perceived usefulness, perceived ease of use and information credibility directly predicted attitude towards usage and accounted for 45% of its variance. Perceived ease of use exerted influence on both perceived usefulness and information credibility. The findings can enhance our understanding of factors that contribute to user reposting behaviour and provide insight into design and implementation strategies to increase the likelihood of user intention to repost information using mobile UGC online community applications.     

基于延迟接受的多用户任务卸载策略

随着人工智能的应用对计算资源的要求越来越高,移动设备由于计算能力和存储能量有限而无法处理这类有实时性需求的计算密集型应用.移动边缘计算(Mobile Edge Computing,MEC)可以在无线网络边缘提供计算卸载服务,达到缩短时延和节约能源的目的.针对多用户依赖任务卸载问题,在综合考虑时延与能耗的基础上建立用户依...     

面向移动Web应用的浏览器缓存性能度量与优化

随着移动互联网的飞速发展,用户越来越多地通过移动设备访问Web应用.浏览器为Web应用提供基本的计算、渲染等运行时支撑,其缓存机制可以支持Web应用直接从本地而不是通过网络来获取可复用资源,不仅能够减少整体的执行时间从而提升应用加载速度,还能够减少网络流量使用和电池电量消耗,从而保证移动Web用户体验.近年来,围绕面向移动Web应用的浏览器缓存优化得到了国内外学术界和工业界的广泛关注.然而,现有研究工作大多都是从网络层面关注浏览器缓存的整体性能,未充分考虑移动互联网用户访问行为的差异性和动态性,以及Web应用自身持续演化对浏览器实际缓存性能的影响.针对这一问题,首先设计了一种新型主动式缓存度量实验,通过仿真用户的访问行为来分析移动Web应用实际资源使用情况,揭示了浏览器缓存的理论性能上限和实际性能之间的巨大差距,并发现了造成这一差距的3个主要原因：重复请求别名资源、启发式过期时间和保守的过期时间配置.基于此发现,从应用层和平台层分别提出了两种浏览器缓存性能优化方案,并实现了原型系统.实验结果表明,采用两种方法分别平均可减少8%~51%和4%~58%的网络流量,且系统开销较小.     

A contextualized and personalized model to predict user interest using location-based social networks

The accurate determination of user interest in terms of geographic information is essential to numerous mobile applications, such as recommender systems and mobile advertising. User interest is greatly influenced by the usage context and varies across individuals; therefore, a user interest model should incorporate these individual needs and propensities. In this paper, we present an approach to model user interest in a contextualized and personalized manner based on location-based social networks. Multinomial logistic regression is employed to quantify the relationship between user interest and usage context at both the aggregate and individual levels. The proposed approach is tested in a real-world application using Foursquare check-ins issued between February and June 2014 in the three major cities of Chicago, Los Angeles and New York. Results demonstrate the capability of the contextualization process for capturing contextual influences on user interest, and that such influences can be observed at a fine-grained scale at the individual level through the personalization process. The proposed approach therefore enables contextualized and personalized estimation of user interest, thereby contributing useful information to follow-up mobile applications.     

SUTIL – Network selection based on utility function and integer linear programming

This work presents SUTIL, a mechanism for network selection in the context of next generation networks (NGN). SUTIL selection mechanism prioritizes networks with higher relevance to the application and lower energy consumption and it enables full and seamless connectivity to mobile user devices and applications. Consequently, SUTIL contributes to realize the vision of ubiquitous computing, in which services, devices, and sensor-enriched environments interact anytime, anywhere to accomplish human designed tasks. The provided solution is based on utility function and integer linear programming and it aims at: (i) maximizing the user satisfaction while meeting application QoS and (ii) minimizing the energy consumption of devices when connecting to a target network. The solution is global since it considers for a given base station all devices that are simultaneously candidate for handoff. Simulation results showed the benefits of SUTIL usage in NGN environments.     

Green software requirements and measurement: random decision forests-based software energy consumption profiling

This paper proposes an explicit definition of green software requirements and a tool to support their evaluation. The proposed evaluation tool describes the green efficiency by considering the energy consumption as the main aspect to be studied during the development stage. This approach consists of building a multiple regression model, by using a supervised learning algorithm, in order to reproduce the energy consumption pattern of devices at different workload circumstances. The energy consumption model is then deployed to estimate the impact of software applications based on their resource usage. Our work has been validated on desktop and mobile devices. The experiments show the effectiveness of the proposed energy profiling tool that provided relevant information on the energy consumption of software applications.     

基于应用分类和系统调用的Android恶意程序检测

针对Android平台恶意程序泛滥的问题,提出一种基于应用分类和系统调用的恶意程序检测方法。以Google Play为依据进行应用程序分类,利用运行时产生的系统调用频数计算每个类别的系统调用使用阈值。当应用程序安装运行时,手机端收集应用程序权限信息和产生的系统调用信息发给远程服务器,远程服务器根据权限信息采用序列最小优化算法给应用程序进行分类,分类后利用系统调用频数计算出系统调用使用值,与该类别的阈值进行比较判断是否恶意程序,将分类结果及判定结果反馈给用户,由用户判断是否需要更改分类重新检测。实验结果表明了该方法的可行性和有效性,不仅减少了手机的资源消耗,又能对产生恶意行为的应用程序及时做出反应。     

Effectiveness Analysis of DVFS and DPM in Mobile Devices

The demand for high-performance embedded processors in multimedia mobile electronics is growing and their power consumption thus increasingly threatens battery lifetime.It is usually believed that the dynamic voltage and frequency scaling (DVFS) feature saves significant energy by changing the performance levels of processors to match the performance demands of applications on the fly.However,because the energy efficiency of embedded processors is rapidly improving,the effectiveness of DVFS is expected to change.In this paper,we analyze the benefit of DVFS in state-of-the-art mobile embedded platforms in comparison to those in servers or PCs.To obtain a clearer view of the relationship between power and performance,we develop a measurement methodology that can synchronize time series for power consumption with those for processor utilization.The results show that DVFS hardly improves the energy efficiency of mobile multimedia electronics,and can even significantly worsen energy efficiency and performance in some cases.According to this observation,we suggest that power management for mobile electronics should concentrate on adaptive and intelligent power management for peripheral devices.As a preliminary design,we implement an adaptive network interface card (NIC) speed control that reduces power consumption by 10% when NIC is not heavily used.Our results provide valuable insights into the design of power management schemes for future mobile embedded systems.     

Accurate power modeling of modern mobile application processors

The power modeling of mobile application processors (APs) is a challenging task due to their complexity. The existing power models and their associated devices have mostly been made obsolete by recent hardware developments. In this paper, we propose an enhanced power model used in modern mobile devices. The model accurately estimates the power consumption of AP component and utilizes the runtime usage information of each hardware component. We evaluated the model accuracy using various benchmarks, as well as popular smartphone applications with multiple devices that employ different APs. The evaluation shows that our model achieves the mean absolute percentage error (MAPE) of 5.1%.     

Energy consumption analysis of data stream processing: a benchmarking approach

Energy efficiency of data analysis systems has become a very important issue in recent times because of the increasing costs of data center operations. Although distributed streaming workloads have increasingly been present in modern data centers, energy‐efficient scheduling of such applications remains as a significant challenge. In this paper, we conduct an energy consumption analysis of data stream processing systems in order to identify their energy consumption patterns. We follow stream system benchmarking approach to solve this issue. Specifically, we implement Linear Road benchmark on six stream processing environments (S4, Storm, ActiveMQ, Esper, Kafka, and Spark Streaming) and characterize these systems' performance on a real‐world data center. We study the energy consumption characteristics of each system with varying number of roads as well as with different types of component layouts. We also use a microbenchmark to capture raw energy consumption characteristics. We observed that S4, Esper, and Spark Streaming environments had highest average energy consumption efficiencies compared with the other systems. Using a neural networkbased technique with the power/performance information gathered from our experiments, we developed a model for the power consumption behavior of a streaming environment. We observed that energy‐efficient execution of streaming application cannot be specifically attributed to the system CPU usage. We observed that communication between compute nodes with moderate tuple sizes and scheduling plans with balanced system overhead produces better power consumption behaviors in the context of data stream processing systems. Copyright © 2016 John Wiley & Sons, Ltd.     

Design and implementation of a smart system for personalization and accurate selection of mobile services

Wireless mobile services are computing applications that run on handheld wireless devices. Such applications must work within the daunting constraints of the devices, which include memory, processing power, input capabilities, and size of display. It is therefore important that mobile services take into account the user’s context, optimize resource usage, and minimize input effort imposed on the user. In this paper, we present the design and implementation of a smart agent-enabled system for personalizing wireless mobile services and advertisements for Java 2 Micro Edition (J2ME) or Java ME, and Wireless Application Protocol (WAP) enabled devices. We use software agents for context filtering because such autonomous software entities have characteristics that can benefit mobile devices and the wireless environment, and the Composite Capability/Preference Profiles (CC/PP) standard for defining profiles for user preferences and device capabilities. The system incorporates the use of artificial neural networks to adaptively and iteratively learn to select the best available service based on contextual information. The system is evaluated using practical operating scenarios, as well as empirical data and results show an 87% success rate in the selection of the best available service.     

Elastic resource provisioning in hybrid mobile cloud for computationally intensive mobile applications

The paper addresses the integration of hybrid cloud with mobile applications. The challenge about hybrid mobile cloud resource provisioning is the trade-offs between energy consumption, performance provided to users and how resources, such as processing power and network, are being utilized. The proposed elastic hybrid mobile cloud resource provisioning model is jointly optimized to improve mobile user experience within the constraints of available resources and user QoS requirement. The paper presents the system utility of hybrid cloud system involving local cloud and public cloud infrastructure. From the perspectives of both mobile applications and cloud providers, the proposed system utility is optimized to improve the performance of mobile applications and the utilization of cloud resources. The proposed elastic hybrid mobile cloud resource provisioning algorithm includes two sub-algorithms. To evaluate and validate performance of the proposed algorithm, a series of experiments are conducted. The comparison results and analyses are discussed. The experimental results show the improvement to previous works.     

MobiByte: An Application Development Model for Mobile Cloud Computing

Mobile cloud computing presents an effective solution to overcome smartphone constraints, such as limited computational power, storage, and energy. As the traditional mobile application development models do not support computation offloading, mobile cloud computing requires novel application development models that can facilitate the development of cloud enabled mobile applications. This paper presents a mobile cloud application development model, named MobiByte, to enhance mobile device applications’ performance, energy efficiency, and execution support. MobiByte is a context-aware application model that uses multiple data offloading techniques to support a wide range of applications. The proposed model is validated using prototype applications and detailed results are presented. Moreover, MobiByte is compared with the most recent application models with a conclusion that it outperforms the existing application models in many aspects like energy efficiency, performance, generality, context awareness, and privacy.