Scheduling I/O requests in a multimedia-on-demand application

[[abstract]]©2001 Cambridge International Science Publishing-The tremendous progress of hardware technology has made the realization of multimedia application. In this paper, we first design and implement a distributed multimedia-on-demand application. As the huge volumes of media data and lots of viewers have to be supported, a distributed architecture is thus adopted for our system. Besides, to accommodate the limited bandwidth of disk subsystem and jitter-free characteristics of multimedia playback, we also propose a disk scheduling scheme for real-time I/O retrieval in a multimedia server. Previously, by DM-SCAN, groups of tasks that can be rescheduled under real-time constraints are selected. They are called MSGs (Maximum-Scannable-Groups). In this paper, an enlarged-MSG (E-MSG) is proposed to further expand the MSG concept. By removing excess constraints on MSG, E-MSG merges several MSGs as a new reschedulable group. Experiment results show that E-MSG scheme is superior to MSG in the obtained disk throughput[[department]]資訊工程學

An Online Reprogrammable Operating System for Wireless Sensor Networks *

Wireless sensor networks enjoy some unique characteristics. For example, sensor network are often autonomous, long-lived and rely on battery as the power source. In this paper, we thus improve the SOS kernel to address these unique characteristics. Firstly, we design and implement the hot-swapping capability in SOS that allows a module to be upgraded on the fly. In our system, the hot-swapping procedure is an online process and the execution state of the old module can be properly transferred to the new module. We also allow interface changes during hot-swapping. Moreover, we enable hot-swapping of not only application modules but also kernel modules. Finally, our hot-swapping procedure is lightweight in that, during hot-swapping, the job of module-linking is offloaded to the server to reduce the reprogramming cost in sensor nodes. In addition to supporting hot-swapping, we also enhance the system call performance in SOS by caching the access results of the system call jump table. Furthermore, we replace the first-fit flash memory allocation scheme in SOS by a technique that relies on erase counters to evenly distribute memory traffic around the flash memory. Moreover, the maintenance of erase counters of each block is also offloaded to the server. We have implemented our system on the Mica2 mote. Evaluations reveal that our kernel can effectively improve the performance of SOS by the above three mechanisms. For example, we support hot-swapping of both kernel and application modules while incurring a negligible overhead. Hot-swapping an application module with a size of 2.5 Kbytes can be done within 160 processor cycles. In addition, we also reduce the system call invocation overhead in SOS by about 15%. Finally, our new flash allocation scheme allows flash blocks to be erased more evenly to prolong the lifetime of flash memory

Enlarged-maximum-scannable-groups for real-time disk scheduling in a multimedia system

[[abstract]]In a multimedia system, disk I/O subsystem is the most important component due to its relatively limited throughput and large delay. Previously, by applying SCAN to reschedule tasks having the same deadline, SCAN-EDF tries to improve disk throughput while real-time constraints can be satisfied. In DM-SCAN, groups of tasks that can be successfully rescheduled by SCAN under specified real-time requirements are identified. They are called MSGs (maximum-scannable-groups). In this paper, an enlarged-MSG (E-MSG) is proposed to further expand the MSG concept and thus to obtain more improvement in disk throughput. By removing some excess constraints on MSG, E-MSG merges several MSGs as a new scannable-group. Experiment results show that the E-MSG scheme is better than both SCAN-EDF and MSG in the disk throughput obtained.[[fileno]]2030218030001[[department]]資訊工程學

KStable: A Computational Method for Predicting Protein Thermal Stability Changes by K-Star with Regular-mRMR Feature Selection

Thermostability is a protein property that impacts many types of studies, including protein activity enhancement, protein structure determination, and drug development. However, most computational tools designed to predict protein thermostability require tertiary structure data as input. The few tools that are dependent only on the primary structure of a protein to predict its thermostability have one or more of the following problems: a slow execution speed, an inability to make large-scale mutation predictions, and the absence of temperature and pH as input parameters. Therefore, we developed a computational tool, named KStable, that is sequence-based, computationally rapid, and includes temperature and pH values to predict changes in the thermostability of a protein upon the introduction of a mutation at a single site. KStable was trained using basis features and minimal redundancy⁻maximal relevance (mRMR) features, and 58 classifiers were subsequently tested. To find the representative features, a regular-mRMR method was developed. When KStable was evaluated with an independent test set, it achieved an accuracy of 0.708

PClass: Protein Quaternary Structure Classification by Using Bootstrapping Strategy as Model Selection

Protein quaternary structure complex is also known as a multimer, which plays an important role in a cell. The dimer structure of transcription factors is involved in gene regulation, but the trimer structure of virus-infection-associated glycoproteins is related to the human immunodeficiency virus. The classification of the protein quaternary structure complex for the post-genome era of proteomics research will be of great help. Classification systems among protein quaternary structures have not been widely developed. Therefore, we designed the architecture of a two-layer machine learning technique in this study, and developed the classification system PClass. The protein quaternary structure of the complex is divided into five categories, namely, monomer, dimer, trimer, tetramer, and other subunit classes. In the framework of the bootstrap method with a support vector machine, we propose a new model selection method. Each type of complex is classified based on sequences, entropy, and accessible surface area, thereby generating a plurality of feature modules. Subsequently, the optimal model of effectiveness is selected as each kind of complex feature module. In this stage, the optimal performance can reach as high as 70% of Matthews correlation coefficient (MCC). The second layer of construction combines the first-layer module to integrate mechanisms and the use of six machine learning methods to improve the prediction performance. This system can be improved over 10% in MCC. Finally, we analyzed the performance of our classification system using transcription factors in dimer structure and virus-infection-associated glycoprotein in trimer structure. PClass is available via a web interface at http://predictor.nchu.edu.tw/PClass/

iStable 2.0: Predicting protein thermal stability changes by integrating various characteristic modules

Protein mutations can lead to structural changes that affect protein function and result in disease occurrence. In protein engineering, drug design or and optimization industries, mutations are often used to improve protein stability or to change protein properties while maintaining stability. To provide possible candidates for novel protein design, several computational tools for predicting protein stability changes have been developed. Although many prediction tools are available, each tool employs different algorithms and features. This can produce conflicting prediction results that make it difficult for users to decide upon the correct protein design. Therefore, this study proposes an integrated prediction tool, iStable 2.0, which integrates 11 sequence-based and structure-based prediction tools by machine learning and adds protein sequence information as features. Three coding modules are designed for the system, an Online Server Module, a Stand-alone Module and a Sequence Coding Module, to improve the prediction performance of the previous version of the system. The final integrated structure-based classification model has a higher Matthews correlation coefficient than that of the single prediction tool (0.708 vs 0.547, respectively), and the Pearson correlation coefficient of the regression model likewise improves from 0.669 to 0.714. The sequence-based model not only successfully integrates off-the-shelf predictors but also improves the Matthews correlation coefficient of the best single prediction tool by at least 0.161, which is better than the individual structure-based prediction tools. In addition, both the Sequence Coding Module and the Stand-alone Module maintain performance with only a 5% decrease of the Matthews correlation coefficient when the integrated online tools are unavailable. iStable 2.0 is available at http://ncblab.nchu.edu.tw/iStable2