Application software

About: Application software is a research topic. Over the lifetime, 12185 publications have been published within this topic receiving 219822 citations. The topic is also known as: software application & application software.

Power analysis of embedded operating systems

Abstract: The increasing complexity and software content of embedded systems has led to the frequent use of system software that helps applications access underlying hardware resources easily and efficiently. In this paper, we analyze the power consumption of real-time operating systems (RTOSs), which form an important component of the system software layer. Despite the widespread use of, and significant role played by, RTOSs in mobile and low-power embedded systems, little is known about their power consumption characteristics. This work presents the power profiles for a commercial RTOS, μC/OS, running several applications on an embedded system based on the Fujitsu SPARClite processor. Our work demonstrates that the RTOS can consume a significant fraction of the system power and, in addition, impact the power consumed by other software components. We illustrate the ways in which application software can be designed to use the RTOS in a power-efficient manner. We believe that this work is a first step towards establishing a systematic approach to RTOS power modeling and optimization.

Elements of Software Configuration Management

Abstract: Software configuration management (SCM) is one of the disciplines of the 1980's which grew in response to the many failures of the software industry throughout the 1970's. Over the last ten years, computers have been applied to the solution of so many complex problems that our ability to manage these applications has all too frequently failed. This has resulted in the development of a series of ''new'' disciplines intended to help control the software process. This paper will focus on the discipline of SCM by first placing it in its proper context with respect to the rest of the software development process, as well as to the goals of that process. It will examine the constituent components of SCM, dwelling at some length on one of those components, configuration control. It will conclude with a look at what the 1980's might have in store.

Constrained component deployment in wide-area networks using AI planning techniques

Abstract: Wide-area network applications are increasingly being built using component-based models, which enable integration of diverse functionality in modules distributed across the network. In such models, dynamic component selection and deployment enables an application to flexibly adapt to changing client and network characteristics, achieve load-balancing, and satisfy QoS requirements. Unfortunately, the problem of finding a valid component deployment is hard because one needs to decide on the set of components while satisfying various constraints resulting from application semantic requirements, network resource limitations, and interactions between the two. In this paper, we propose a general model for the component placement problem and present an algorithm for it, which is based on AI planning algorithms. We validate the effectiveness of our algorithm by demonstrating its scalability with respect to network size and number of components in the context of deployments generated for two example applications - a security-sensitive mail service, and a Webcast service - in a variety of network environments.

Is software green? Application development environments and energy efficiency in open source applications

Abstract: Context: The energy efficiency of IT systems, also referred to as Green IT, is attracting more and more attention. While several researchers have focused on the energy efficiency of hardware and embedded systems, the role of application software in IT energy consumption still needs investigation. Objective: This paper aims to define a methodology for measuring software energy efficiency and to understand the consequences of abstraction layers and application development environments for the energy efficiency of software applications. Method: We first develop a measure of energy efficiency that is appropriate for software applications. We then examine how the use of application development environments relates to this measure of energy efficiency for a sample of 63 open source software applications. Results: Our findings indicate that a greater use of application development environments - specifically, frameworks and external libraries - is more detrimental in terms of energy efficiency for larger applications than for smaller applications. We also find that different functional application types have distinctly different levels of energy efficiency, with text and image editing and gaming applications being the most energy inefficient due to their intense use of the processor. Conclusion: We conclude that different designs can have a significant impact on the energy efficiency of software applications. We have related the use of software application development environments to software energy efficiency suggesting that there may be a trade-off between development efficiency and energy efficiency. We propose new research to further investigate this topic.

A Human Action Recognition System for Embedded Computer Vision Application

Abstract: In this paper, we propose a human action recognition system suitable for embedded computer vision applications in security systems, human-computer interaction and intelligent environments. Our system is suitable for embedded computer vision application based on three reasons. Firstly, the system was based on a linear support vector machine (SVM) classifier where classification progress can be implemented easily and quickly in embedded hardware. Secondly, we use compacted motion features easily obtained from videos. We address the limitations of the well known motion history image (MHI) and propose a new hierarchical motion history histogram (HMHH) feature to represent the motion information. HMHH not only provides rich motion information, but also remains computationally inexpensive. Finally, we combine MHI and HMHH together and extract a low dimension feature vector to be used in the SVM classifiers. Experimental results show that our system achieves significant improvement on the recognition performance.