Dynamically Adjusting the Mining Capacity in Cryptocurrency with Binary Blockchain

Many cryptocurrencies rely on Blockchain for its operation. Blockchain serves as a public ledger where all the completed transactions can be looked up. To place transactions in the Blockchain, a mining operation must be performed. However, due to a limited mining capacity, the transaction confirmation time is increasing. To mitigate this problem many ideas have been proposed, but they all come with own challenges. We propose a novel parallel mining method that can adjust the mining capacity dynamically depending on the congestion level. It does not require an increase in the block size or a reduction of the block confirmation time. The proposed scheme can increase the number of parallel blockchains when the mining congestion is experienced, which is especially effective under DDoS attack situation. We describe how and when the Blockchain is split or merged, how to solve the imbalanced mining problem, and how to adjust the difficulty levels and rewards. We then show the simulation results comparing the performance of binary blockchain and the traditional single blockchain

Hadoop Performance Analysis Model with Deep Data Locality

Background: Hadoop has become the base framework on the big data system via the simple concept that moving computation is cheaper than moving data. Hadoop increases a data locality in the Hadoop Distributed File System (HDFS) to improve the performance of the system. The network traffic among nodes in the big data system is reduced by increasing a data-local on the machine. Traditional research increased the data-local on one of the MapReduce stages to increase the Hadoop performance. However, there is currently no mathematical performance model for the data locality on the Hadoop. Methods: This study made the Hadoop performance analysis model with data locality for analyzing the entire process of MapReduce. In this paper, the data locality concept on the map stage and shuffle stage was explained. Also, this research showed how to apply the Hadoop performance analysis model to increase the performance of the Hadoop system by making the deep data locality. Results: This research proved the deep data locality for increasing performance of Hadoop via three tests, such as, a simulation base test, a cloud test and a physical test. According to the test, the authors improved the Hadoop system by over 34% by using the deep data locality. Conclusions: The deep data locality improved the Hadoop performance by reducing the data movement in HDFS

A monolithic and flexible fluoropolymer film microreactor for organic synthesis applications

A photocurable and viscous fluoropolymer with chemical stability is a highly desirable material for fabrication of microchemical devices. Lack of a reliable fabrication method, however, limits actual applications for organic reactions. Herein, we report fabrication of a monolithic and flexible fluoropolymer film microreactor and its use as a new microfluidic platform. The fabrication involves facile soft lithography techniques that enable partial curing of thin laminates, which can be readily bonded by conformal contact without any external forces. We demonstrate fabrication of various functional channels (similar to 300 mu m thick) such as those embedded with either a herringbone micromixer pattern or a droplet generator. Organic reactions under strongly acidic and basic conditions can be carried out in this film microreactor even at elevated temperature with excellent reproducibility. In particular, the transparent film microreactor with good deformability could be wrapped around a light-emitting lamp for close contact with the light source for efficient photochemical reactions with visible light, which demonstrates easy integration with optical components for functional miniaturized systems.open1112Ysciescopu