• Journal of KIISE:Computer Systems and Theory
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• v.30 no.9
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• pp.512-522
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• 2003

For a time-multiplexed FPGA, a circuit is partitioned into several subcircuits, so that they temporally share the same physical FPGA device by hardware reconfiguration. In these architectures, all the hardware reconfiguration information called contexts are generated and downloaded into the chip, and then the pre-scheduled context switches occur properly and timely. Typically, the size of the chip required to implement the circuit depends on both the maximum number of the LUT blocks required to implement the function of each subcircuit and the maximum number of micro-registers to store results over context switches in the same time. Therefore, many partitioning or synthesis methods try to minimize these two factors. In this paper, we present a new estimation technique to find the lower bound on the number of micro-registers which can be obtained by any synthesis methods, respectively, without performing any actual synthesis and/or design space exploration. The lower bound estimation is very important in sense that it greatly helps to evaluate the results of the previous work and even the future work. If the estimated lower bound exactly matches the actual number in the actual design result, we can say that the result is guaranteed to be optimal. In contrast, if they do not match, the following two cases are expected: we might estimate a better (more exact) lower bound or we find a new synthesis result better than those of the previous work. Our experimental results show that there are some differences between the numbers of micro-registers and our estimated lower bounds. One reason for these differences seems that our estimation tries to estimate the result with the minimum micro-registers among all the possible candidates, regardless of usage of other resources such as LUTs, while the previous work takes into account both LUTs and micro-registers. In addition, it implies that our method may have some limitation on exact estimation due to the complexity of the problem itself in sense that it is much more complicated than LUT estimation and thus needs more improvement, and/or there may exist some other synthesis results better than those of the previous work.

• Journal of Internet Computing and Services
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• v.14 no.6
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• pp.71-84
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• 2013

Log data, which record the multitude of information created when operating computer systems, are utilized in many processes, from carrying out computer system inspection and process optimization to providing customized user optimization. In this paper, we propose a MongoDB-based unstructured log processing system in a cloud environment for processing the massive amount of log data of banks. Most of the log data generated during banking operations come from handling a client's business. Therefore, in order to gather, store, categorize, and analyze the log data generated while processing the client's business, a separate log data processing system needs to be established. However, the realization of flexible storage expansion functions for processing a massive amount of unstructured log data and executing a considerable number of functions to categorize and analyze the stored unstructured log data is difficult in existing computer environments. Thus, in this study, we use cloud computing technology to realize a cloud-based log data processing system for processing unstructured log data that are difficult to process using the existing computing infrastructure's analysis tools and management system. The proposed system uses the IaaS (Infrastructure as a Service) cloud environment to provide a flexible expansion of computing resources and includes the ability to flexibly expand resources such as storage space and memory under conditions such as extended storage or rapid increase in log data. Moreover, to overcome the processing limits of the existing analysis tool when a real-time analysis of the aggregated unstructured log data is required, the proposed system includes a Hadoop-based analysis module for quick and reliable parallel-distributed processing of the massive amount of log data. Furthermore, because the HDFS (Hadoop Distributed File System) stores data by generating copies of the block units of the aggregated log data, the proposed system offers automatic restore functions for the system to continually operate after it recovers from a malfunction. Finally, by establishing a distributed database using the NoSQL-based Mongo DB, the proposed system provides methods of effectively processing unstructured log data. Relational databases such as the MySQL databases have complex schemas that are inappropriate for processing unstructured log data. Further, strict schemas like those of relational databases cannot expand nodes in the case wherein the stored data are distributed to various nodes when the amount of data rapidly increases. NoSQL does not provide the complex computations that relational databases may provide but can easily expand the database through node dispersion when the amount of data increases rapidly; it is a non-relational database with an appropriate structure for processing unstructured data. The data models of the NoSQL are usually classified as Key-Value, column-oriented, and document-oriented types. Of these, the representative document-oriented data model, MongoDB, which has a free schema structure, is used in the proposed system. MongoDB is introduced to the proposed system because it makes it easy to process unstructured log data through a flexible schema structure, facilitates flexible node expansion when the amount of data is rapidly increasing, and provides an Auto-Sharding function that automatically expands storage. The proposed system is composed of a log collector module, a log graph generator module, a MongoDB module, a Hadoop-based analysis module, and a MySQL module. When the log data generated over the entire client business process of each bank are sent to the cloud server, the log collector module collects and classifies data according to the type of log data and distributes it to the MongoDB module and the MySQL module. The log graph generator module generates the results of the log analysis of the MongoDB module, Hadoop-based analysis module, and the MySQL module per analysis time and type of the aggregated log data, and provides them to the user through a web interface. Log data that require a real-time log data analysis are stored in the MySQL module and provided real-time by the log graph generator module. The aggregated log data per unit time are stored in the MongoDB module and plotted in a graph according to the user's various analysis conditions. The aggregated log data in the MongoDB module are parallel-distributed and processed by the Hadoop-based analysis module. A comparative evaluation is carried out against a log data processing system that uses only MySQL for inserting log data and estimating query performance; this evaluation proves the proposed system's superiority. Moreover, an optimal chunk size is confirmed through the log data insert performance evaluation of MongoDB for various chunk sizes.