Efficient DVFS for low power HEVC software decoder

Low power design is a primary concern for modern battery-driven devices and video applications such as video decoding are often the most resource intensive applications of consumer electronics devices. Modern embedded processors are now proven to support video applications with software. They are also equipped with advanced features including Dynamic Voltage Frequency Scaling and Dynamic Power Management in order to reduce their power consumption. High Efficiency Video Coding (HEVC) is the latest MPEG video standard offering state-of-the-art compression rates and advanced parallel processing solutions. This paper presents a low power real-time software architecture for a HEVC decoder. Software decoding fosters short time-to-market as it relies on software designs for a general purpose processor. The proposed architecture exploits the characteristics of the multicore ARM big.LITTLE System-on-a-Chip to provide a low power design. Extensive power measurements as well as real-time metrics are provided to compare the proposed architecture with state-of-the-art.     

基于软件的无线网绿色代理系统

提出了一种基于软件架构的无线网绿色节能代理系统.针对无线网络的特点,通过在代理服务端创建网内其他终端的虚拟网卡和虚拟镜像,配合针对终端移动的代理服务器切换机制与重发机制,使得无线终端在节能时快速进入休眠状态,在响应请求时快速、有效地被唤醒.该软件系统不对原有网络拓扑或硬件系统做任何改变,所以具有普适性和可移植性强的特点.该系统被实际部署到一个包括11台有线与无线上网的计算机测试平台上通过长时间的监测,整个网络功耗节省了超过60％,充分说明了该系统在实际应用中的经济价值.     

基于AADL语言的移动软件能耗评估方法

移动软件往往部署在电量受限的处理器上,能耗已成为评价这类软件的一个重要质量属性.与代码级和指令级相比,在设计级进行能耗评估具有耗时短、成本低的优点,近年来已成为软件工程学术界和工业界的研究热点.目前虽已涌现出一些设计级能耗评估方法,但这些方法大多未对软件构件的内部行为元素进行能耗评估,导致了精度问题.针对上述问题,基于体系结构分析设计语言AADL和StrongARM处理器构建了一种移动软件能耗评估模型,进一步定义了面向AADL语言的移动软件能耗评估过程,在此基础上研发了一款能耗评估工具,进而提出一种基于AADL语言的移动软件能耗评估方法.实验结果表明该方法较已有AADL能耗评估方法在精度上有所提高.     

Green challenges to system software in data centers

With the increasing demand and the wide application of high performance commodity multi-core processors, both the quantity and scale of data centers grow dramatically and they bring heavy energy consumption. Researchers and engineers have applied much effort to reducing hardware energy consumption, but software is the true consumer of power and another key in making better use of energy. System software is critical to better energy utilization, because it is not only the manager of hardware but also the bridge and platform between applications and hardware. In this paper, we summarize some trends that can affect the efficiency of data centers. Meanwhile, we investigate the causes of software inefficiency. Based on these studies, major technical challenges and corresponding possible solutions to attain green system software in programmability, scalability, efficiency and software architecture are discussed. Finally, some of our research progress on trusted energy efficient system software is briefly introduced.     

SEProf: A high-level software energy profiling tool for an embedded processor enabling power management functions

Energy efficiency has become one of the most important design issues for embedded systems. To examine the power consumption of an embedded system, an energy profiling tool is highly demanded. Although a number of energy profiling tools have been proposed, they are not directly applicable to the embedded processors with power management functions that are widely utilized in battery-operated embedded systems to reduce power consumption. Hence, this study presents a high-level energy profiling tool, called SEProf, that estimates the energy consumption of an embedded system running multithread software and a multitasking operating system (OS) that supports power management functions. This study implements the proposed SEProf in Linux 2.6.19 and evaluates its performance on an ARM11 MPCore processor. Experimental results demonstrate that the proposed tool can provide accurate energy profiling results with a low profiling overhead.     

DataABC: A fast ABC based energy-efficient live VM consolidation policy with data-intensive energy evaluation model

Live Virtual Machine (VM) consolidation is an effective method of improving energy-efficiency level in green data centers. Currently, to evaluate energy consumption in green data centers, energy-efficiency evaluation model with CPU utilization rate has been proposed. However, it is not suitable for data-intensive computing due to great energy consumption by GPU-intensive processing. In this paper, we have proposed a new energy evaluation model with CPU and GPU utilization rates. There are two kinds of policies in live VM consolidation: one for VM selection and the other for VM allocation. Some researchers have proposed their solutions based on VM selection policy or VM allocation policy respectively. However, it will be a better energy-efficiency VM consolidation policy if these two polices are integrated together. Based on these two policies, a fast Artificial Bee Colony (ABC) based energy-efficiency live VM consolidation policy with data-intensive energy model, named as DataABC, is proposed. DataABC adopts the idea of Artificial Bee Colony algorithm to get a fast and global optimized decision of VM consolidation. Compared with two state-of-art policies of PS-ABC and PS-ES, the total energy consumption of DataABC evidently drop by 9.72% and 5.84% respectively. As a result, based on the ESV metric, the DataABC approach has proved that (a) the energy-efficiency evaluation model with data-intensive computing is valid and that (b) DataABC can save energy with a good Quality of Service (QoS) in green data centers.     

一种适用于多任务多资源移动边缘计算环境下的改进粒子群算力卸载算法

为了研究移动设备在多资源复杂环境下的能量消耗问题,提出一种针对移动边缘设备计算卸载的改进粒子群算法。首先基于多环境的移动设备能耗提出一种移动设备能量消耗的计算模型;其次针对计算资源分配问题设计一种可以用于衡量分配方案优劣的适应度算法;最后提出一种改进的粒子群算法,用于求解进一步降低移动边缘设备能耗分配方案的最优解。通过使用模拟仿真软件对多种卸载策略下移动设备能耗、系统响应时间等关键指标对比表明,本文算法在满足用户响应时间的前提下,在求解降低移动设备能耗调度分配方案最优解的过程中具有更优的表现。     

基于嵌入式软件算法级能耗问题研究

能耗已经成为嵌入式系统设计中一个重要的约束条件.嵌入式系统是典型的软件驱动执行系统,硬件的电路活动直接导致系统参数功耗,而软件中的指令执行和数据存取等操作底层的微处理、总线、Cache、存储器和I/O接口等硬件的活动都会间接的导致系统参数功耗.在现代“低碳经济”的背景下嵌入系统的功耗已经引起人们关注的一个重点.而软件设计早期对高层所作的功率耗评估和优化对整个系统的的能耗影响最为显著.本文通过构建算法级能耗估算模型,并通过实例采用神经网络算法、遗传算法等进行能耗求解,同时在求解过程中进行能耗分析.     

The role of model checking in software engineering

Model checking is a formal verification technique. It takes an exhaustively strategy to check hardware circuits and network protocols against desired properties. Having been developed for more than three decades, model checking is now playing an important role in software engineering for verifying rather complicated software artifacts.This paper surveys the role of model checking in software engineering. In particular, we searched for the related literatures published at reputed conferences, symposiums, workshops, and journals, and took a survey of (1) various model checking techniques that can be adapted to software development and their implementations, and (2) the use of model checking at different stages of a software development life cycle. We observed that model checking is useful for software debugging, constraint solving, and malware detection, and it can help verify different types of software systems, such as object- and aspect-oriented systems, service-oriented applications, web-based applications, and GUI applications including safety- and mission-critical systems.The survey is expected to help human engineers understand the role of model checking in software engineering, and as well decide which model checking technique(s) and/or tool(s) are applicable for developing, analyzing and verifying a practical software system. For researchers, the survey also points out how model checking has been adapted to their research topics on software engineering and its challenges.     

Energy-efficient and Secure Wireless Communication for Telemedicine in IoT

The Internet of Things (IoT) represents a radical shifting paradigm for technological innovations as it can play critical roles in cyberspace applications in various sectors, such as security, monitoring, medical, and environmental sectors, and also in control and industrial applications. The IoT in E-medicine unleashed the design space for new technologies to give instant treatment to patients while also monitoring and tracking health conditions. This research presents a system-level architecture approach for IoT energy efficiency and security. The proposed architecture includes functional components that provide privacy management and system security. Components in the security function group provide secure communications through Multi-Authority Ciphertext-Policy Attributes-Based Encryption (MA-CPABE). Because MA-CPABE is assigned to unlimited devices, presuming that the devices are reliable, the user encodes data with Advanced Encryption Standard (AES) and protects the ABE approach using the solutions of symmetric key. The Johnson’s algorithm with a new computation measure is used to increase network lifetime since an individual sensor node with limited energy represents the inevitable constraints for the broad usage of wireless sensor networks. The optimal route from a source to destination turns out as the cornerstone for longevity of network and its sustainability. To reduce the energy consumption of networks, the evaluation measures consider the node’s residual energy, the number of neighbors, their distance, and the link dependability. The experiment results demonstrate that the proposed model increases network life by about 12.25% (27.73%) compared to Floyd–Warshall’s, Bellman–Ford’s, and Dijkstra’s algorithms, lowering consumption of energy by eliminating the necessity for re-routing the message as a result of connection failure.     

Energy consumption of data mining algorithms on mobile phones: Evaluation and prediction

The pervasive availability of increasingly powerful mobile computing devices like PDAs, smartphones and wearable sensors, is widening their use in complex applications such as collaborative analysis, information sharing, and data mining in a mobile context. Energy characterization plays a critical role in determining the requirements of data-intensive applications that can be efficiently executed over mobile devices. This paper presents an experimental study of the energy consumption behavior of representative data mining algorithms running on mobile devices. Our study reveals that, although data mining algorithms are compute- and memory-intensive, by appropriate tuning of a few parameters associated to data (e.g., data set size, number of attributes, size of produced results) those algorithms can be efficiently executed on mobile devices by saving energy and, thus, prolonging devices lifetime. Based on the outcome of this study we also proposed a machine learning approach to predict energy consumption of mobile data-intensive algorithms. Results show that a considerable accuracy is achieved when the predictor is trained with specific-algorithm features.     

Software generated device exception for more intensive device-related software testing: An industrial field study

It is more important to properly handle exceptions, than to prevent exceptions from occurring, because they arise from so many different causes. In embedded systems, a vast number of exceptions are caused by hardware devices. In such cases, numerous software components are involved in these hardware device-originated exceptions, ranging from the device itself to the device driver, the kernel, and applications. Therefore, it takes a lot of time to debug software that fails to handle exceptions. This paper proposes a lightweight device exception testing method, and a related automation tool, AMOS v3.0. The proposed method artificially triggers more realistic device exceptions in runtime, and monitors how software components handle exceptions in detail. AMOS v3.0 has been applied to the exception testing of car-infotainment systems in an automobile company. The results based on this industrial field study have revealed that 39.13% of the failures in exception handling were caused by applications, 36.23% of the failures were caused by device drivers, and 24.64% were derived from the kernel. We conclude that the proposed method is highly effective, in that it can allow developers to identify the root cause of failure for exception handling.     

HSIR: hybrid architecture for sensor identification and registration for IoT applications

Internet of things is the backbone of the smart applications, which attracts many types of research on the state-of-the-art network applications. Enormous research on sensor networks left more devices that are sensible in the day-to-day life. Hence, implementing new sensor networks for smart applications is not necessary. Many researchers have accepted and utilized existing networks for their request. In this case, techniques for identifying and registering existing sensible things are on demand. This paper proposed a hybrid framework for sensor identification and registration (HSIR) for new IoT applications. This research proposing HSIR as a framework aimed for user-friendliness in the IoT as well as addressed toward the scalability requirement of IoT applications. This model uses content- and context-based multicast communication instead of broadcast to reduce energy and time consumption in sensor identification. HSIR also proposed a public key to register the new network for application requirements. The behaviour of the proposed model has been assayed in realistic with simulations and proved by comparing other models.

     

Processing capacity-based decision mechanism edge computing model for IoT applications

The handling of complex tasks in IoT applications becomes difficult due to the limited availability of resources in most IoT devices. There arises a need to offload the IoT tasks with huge processing and storage to resource enriched edge and cloud. In edge computing, factors such as arrival rate, nature and size of task, network conditions, platform differences and energy consumption of IoT end devices impacts in deciding an optimal offloading mechanism. A model is developed to make a dynamic decision for offloading of tasks to edge and cloud or local execution by computing the expected time, energy consumption and processing capacity. This dynamic decision is proposed as processing capacity-based decision mechanism (PCDM) which takes the offloading decisions on new tasks by scheduling all the available devices based on processing capacity. The target devices are then selected for task execution with respect to energy consumption, task size and network time. PCDM is developed in the EDGECloudSim simulator for four different applications from various categories such as time sensitiveness, smaller in size and less energy consumption. The PCDM offloading methodology is experimented through simulations to compare with multi-criteria decision support mechanism for IoT offloading (MEDICI). Strategies based on task weightage termed as PCDM-AI, PCDM-SI, PCDM-AN, and PCDM-SN are developed and compared against the five baseline existing strategies namely IoT-P, Edge-P, Cloud-P, Random-P, and Probabilistic-P. These nine strategies are again developed using MEDICI with the same parameters of PCDM. Finally, all the approaches using PCDM and MEDICI are compared against each other for four different applications. From the simulation results, it is inferred that every application has unique approach performing better in terms of response time, total task execution, energy consumption of device, and total energy consumption of applications.     

基于稳定匹配的多用户任务卸载策略

随着许多计算密集型应用的出现,移动设备因其有限的计算能力无法满足用户时延、能耗等需求。移动边缘计算（MEC）通过无线信道将用户的任务计算卸载到MEC服务器,从而显著减少任务响应时延和能耗。针对多用户任务卸载问题,提出了基于稳定匹配的多用户任务卸载策略（MUTOSA）,在保证用户的时延要求下达到能耗最小化。首先,在综合考虑时延与能耗的基础上,对独立任务场景下的多用户任务卸载问题进行建模;然后,基于博弈论的稳定匹配中的延迟接收思想,提出了一种调整策略;最后,通过不断迭代,解决了多用户任务卸载问题。实验结果表明,该策略相较于基准策略和启发式策略能够满足更多用户的时延要求,平均提高约10%的用户满意度,并能减少约50%的用户设备总能耗。所提策略在保证用户时延要求的同时有效地减少了能耗,可以有效地提高用户对于时延敏感型应用的体验。     

FuPerMod: a software tool for the optimization of data-parallel applications on heterogeneous platforms

Optimization of data-parallel applications for modern HPC platforms requires partitioning the computations between the heterogeneous computing devices in proportion to their speed. Heterogeneous data partitioning algorithms are based on computation performance models of the executing platforms. Their implementation is not trivial as it requires: accurate and efficient benchmarking of computing devices, which may share resources and/or execute different codes; appropriate interpolation methods to predict performance; and advanced mathematical methods to solve the data partitioning problem. In this paper, we present FuPerMod, a software tool that addresses these implementation issues and automates the development of data partitioning code in data-parallel applications for heterogeneous HPC platforms.     

The REBOOT approach to software reuse

Although some companies have been successful in software reuse, many research projects on reuse have had little industrial penetration. Often the proposed technology has been too ambitious or exotic, or did not scale up. REBOOT emphasizes industrial applicability of the proposed technology in a holistic perspective: a validated method through a Methodology Handbook, a stabilized tool set around a reuse library, a training package, and initial software repositories of reusable components extracted from company-specific projects. This article presents the REBOOT approach to software reuse, covering both organizational and technical aspects and the experiences so far from the applications.     

A quality framework for developing and evaluating original software components

Component-based software development is being identified as the emerging method of developing complex applications consisting of heterogeneous systems. Although more research attention has been given to Commercial Off The Shelf (COTS) components, original software components are also widely used in the software industry. Original components are smaller in size, they have a narrower functional scope and they usually find more uses when it comes to specific and dedicated functions. Therefore, their need for interoperability is equal or greater, than that of COTS components. A quality framework for developing and evaluating original components is proposed in this paper, along with an application methodology that facilitates their evaluation. The framework is based on the ISO9126 quality model which is modified and refined so as to reflect better the notion of original components. The quality model introduced can be tailored according to the organization-reuser and the domain needs of the targeted component. The proposed framework is demonstrated and validated through real case examples, while its applicability is assessed and discussed.     

基于投影的软件易用性评价方法

随着IT行业的不断发展,软件的易用性越来越受到用户和专家的关注与重视。如何科学地量化软件产品的易用性,进而对产品质量进行综合地测评,决定了软件产品的竞争力和用户的满意度。针对软件的易用性,提出了一种基于投影和直觉模糊理论的评价方法。首先,依据用户调查得到的信息,给出评价软件易用性的直觉模糊评价矩阵,并在此基础上建立了评价矩阵的理想决策。其次,借助群决策的框架,给出评价矩阵到理想决策上的投影。然后,基于投影给出测评软件的优劣序。最后,用一个测评实例展示出本方法的有效性和实用性。结果表明,本文提出的模型是一种软件质量综合评价的有效方法。     

Educational software for electrical engineering: A methodology and some results

In this paper, problems related to the use of the microcomputer as a teaching tool in engineering education are discussed. The objectives and methodological principles of educational software are defined, stressing the need to improve the quality of engineering education. The microcomputer is considered as an engineering tool in the decision-making process, offering possibilities for further deeper analysis of physical phenomena in electrical devices. Examples of applications of the methodological principles developed for the elaboration of educational software packages for electrical engineering are given.