• Journal of KIISE
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• v.43 no.6
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• pp.613-620
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• 2016

This paper presents a MOHA(Many-Task Computing on Hadoop) framework which aims to effectively apply the Many-Task Computing(MTC) technologies originally developed for high-performance processing of many tasks, to the existing Big Data processing platform Hadoop. We present basic concepts, motivation, preliminary results of PoC based on distributed message queue, and future research directions of MOHA. MTC applications may have relatively low I/O requirements per task. However, a very large number of tasks should be efficiently processed with potentially heavy inter-communications based on files. Therefore, MTC applications can show another pattern of data-intensive workloads compared to existing Hadoop applications, typically based on relatively large data block sizes. Through an effective convergence of MTC and Big Data technologies, we can introduce a new MOHA framework which can support the large-scale scientific applications along with the Hadoop ecosystem, which is evolving into a multi-application platform.

• Journal of Broadcast Engineering
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• v.24 no.6
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• pp.1122-1133
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• 2019

We have designed and implemented a new data processing framework called MOHA(Mtc On HAdoop) which can effectively support Many-Task Computing(MTC) applications in a YARN-based Hadoop platform. MTC applications can be composed of a very large number of computational tasks ranging from hundreds of thousands to millions of tasks, and each MTC application may have different resource usage patterns. Therefore, we have implemented MOHA-TaskExecutor(a pilot-job that executes real MTC application tasks)'s Adaptive Parallel Computability which can adaptively execute multiple tasks simultaneously, in order to improve the parallel computability of a YARN container and the overall system throughput. We have implemented multi-threaded version of TaskExecutor which can "independently and dynamically" adjust the number of concurrently running tasks, and in order to find the optimal number of concurrent tasks, we have employed Hill-Climbing algorithm.

• Bulletin of the Korean Chemical Society
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• v.34 no.5
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• pp.1333-1338
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• 2013

Chitosan-based nanoparticles (CSNP) were prepared through ionic cross-linking and gelation of chitosan (CS) by tripolyphosphate (TPP). CS properties such as molecular weight, and preparation conditions were screened and the resulting nanoparticles were examined by Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM) and transmission electron microscopy (TEM). The obtained particles were consistently spherical with an overall diameter of approximately $107{\pm}20$ nm. They were successfully used as a carrier for Zidovudine, an anti-human immunodeficiency virus (HIV) which, to our knowledge, is novel. The encapsulation ability, loading capacity, and controlled release behavior for these CSNP was evaluated. Results indicated that their intrinsic properties were strongly affected by properties inherent to CS such as molecular weight, and by the preparation condition, such as cross-linking density, which depends on the concentration of the cross-linker. In vitro release tests for the entrapped zidovudine showed that the CNNP provided a continuous release that can last upwards 20 h.