A Study of Extensible Systems for Resource-Constrained Embedded Devices

• Main Authors: Da-Wei Chang, 張大緯
• Other Authors: Ruei-Chuan Chang
• Format: Others
• Language: en_US
• Published: 2001
• Online Access: http://ndltd.ncl.edu.tw/handle/90840252636424937955

博士 === 國立交通大學 === 資訊科學系 === 89 === With the rapid development on embedded system techniques and Internet technologies, network-enabled embedded devices has grown in their popularity. Two critical design trends of such devices are as follows. First, embedded devices are shifting from static, fixed-function systems to more dynamic and extensible ones. Second, owing to the excellent features of Java, embedded system researchers start seeking ways to make these devices Java-enabled. However, making embedded devices extensible and applying Java technology to these devices are both challenging due to the shortage of resources on these devices. In this thesis, we present our efforts on achieving the above two goals. First of all, we describe the OSP framework (Operating System Portal framework), which makes embedded kernels become extensible while keeping the added overheads minimal. By storing kernel modules on a resource-rich server (i.e. the OS Portal) and loading them on demand, the need for equipping a local storage on the device is eliminated. In addition, we propose mechanisms for reducing the memory requirements and performing on-line module-replacement on the embedded devices. Secondly, we present EJVM (Economic Java Virtual Machine), an economic way to run Java programs on network-enabled, and resource-limited embedded devices. Espousing the architecture proposed by distributed JVM, we store all Java codes on the server to reduce the storage needs of the client devices. In addition, we use two novel techniques to reduce the client-side memory footprints: server-side class representation conversion and on-demand bytecode loading. And we maintain client-side caches and provide performance evaluation on different caching policies. According to the performance measurement, our techniques reduce noticeable resource requirements of the embedded devices. This allows our framework to be applied on a wide range of embedded devices.