Hyun-Hwa Choi

A low SAR design of folder type handset with dual antennas

In this study, a folder type mobile phone that has dual antennas was designed to reduce SAR (specific absorption rate) on the human head. One of the antennas is a PIFA (planar inverted-F antenna) that is inserted in the folder case, the other is typical monopole antenna. In order to achieve optimal performance from these antennas, the phone was designed such that the PIFA is operated only when the phone is used near the head with the folder opened. When phone is not used with the folder closed, the monopole antenna is operated in order to prevent deterrence of radiation that happens when the main beam of a planar antenna is directed to the human body. For the design of the folder type mobile phone with dual antennas and SAR calculation, we used a commercial tool based upon the FDTD technique, SAMCAD. The resonance frequency was tuned to PCS band, 1.86 GHz. In this simulation, bandwidths, resonant frequencies and radiation patterns were calculated. 1g-average peak SAR values were also calculated and compared with those of conventional folder-type mobile phones. The radiation pattern and gain measurements are carried out inside an anechoic chamber with metallic paint on the inside of the plastic case.

Performance Evaluation of a Remote Block Device with High-Speed Cluster Interconnects

Todays data intensive applications require to process large amount of data on cluster systems that consist of commodity computing nodes with high performance network interconnects. Individual computing nodes in the cluster system have indentical resource specification although applications vary greatly in their resource needs. From imbalance of the amount of memory used different computing nodes, we can consider the use of remote idle memory to alleviate the memory pressure on individual computing nodes. We propose a remote memory block device (RMBD) using remote memory access capabilities provided by both Infiniband and RoCE as one of remote swapping techniques. We evaluates the I/O performance of RMBD with it of the block device using TCP/IP protocols. From performance experiments, we find that RMBD using RDMA is able to improve througput by up to 20 times in read operation and 6 times in write operation compared to it using TCP/IP. We demonstrate that RMBD as swap device can provide performance benefits that is up to 2 times speed up compared to HDD for quick-sorting.

Cloud SLA relationships in multi-cloud environment: models and practices

In the past few years, cloud computing has been realized and has achieved advancement. Sincemany cloud systems and providers have been deployed in this world, various models and platforms have been developed to support multiple cloud (i.e., multi-cloud) environments (e.g., inter-cloud, federated cloud, distributed cloud, hybrid cloud, multi-cloud management platform, and cloud service brokerageplatform). Whereas those models provide a multi-cloud environment, they are different according to their objectives, service models, SLA (service level agreement) models, and so on. Even though SLA is considered as a very important issue in cloud computing, SLA relationships in multi-cloud models are not formally described yet. Therefore, in this paper, we introduce various multi-cloud models and analyze the multi-cloud models in term of SLA relationship based on the proposed SLA relationship description model. In addition, this paper introduces open source multi-cloud projects to understand practices in cloud SLA relationships.

A workflow for parallel and distributed computing of large-scale genomic data

Workflow management systems are emerging as dominant solution in bioinformatics because they enable researchers to analyze the huge amount of data generated by modern laboratory equipment. The growth of genomic data generated by next generation sequencing (NGS) results in an increasing need to analyze data on distributed computer clusters. In this paper, we construct a semi-automated workflow system for the analysis of large-scale sequence data sets, describe a pipeline designed with parallel computation to perform the optimal computational steps required to analyze whole genome sequence data, and report the overall execution time of the pipeline using cores on multiple machines.

Workflow-based Bio Data Analysis System for HPC

Since human genome project finished, the cost for human genome analysis has decreased very rapidly. This results in the sharp increase of human genome data to be analyzed. As the need for fast analysis of very large bio data such as human genome increases, non IT researchers such as biologists should be able to execute fast and effectively many kinds of bio applications, which have a variety of characteristics, under HPC environment. To accomplish this purpose, a biologist need to define a sequence of bio applications as workflow easily because generally bio applications should be combined and executed in some order. This bio workflow should be executed in the form of distributed and parallel computing by allocating computing resources efficiently under HPC cluster system. Through this kind of job, we can expect better performance and fast response time of very large bio data analysis. This paper proposes a workflow-based data analysis system specialized for bio applications. Using this system, non-IT scientists and researchers can analyze very large bio data easily under HPC environment.

Performance Enhancement of a DVA-tree by the Independent Vector Approximation

Most of the distributed high-dimensional indexing structures provide a reasonable search performance especially when the dataset is uniformly distributed. However, in case when the dataset is clustered or skewed, the search performances gradually degrade as compared with the uniformly distributed dataset. We propose a method of improving the k-nearest neighbor search performance for the distributed vector approximation-tree based on the strongly clustered or skewed dataset. The basic idea is to compute volumes of the leaf nodes on the top-tree of a distributed vector approximation-tree and to assign different number of bits to them in order to assure an identification performance of vector approximation. In other words, it can be done by assigning more bits to the high-density clusters. We conducted experiments to compare the search performance with the distributed hybrid spill-tree and distributed vector approximation-tree by using the synthetic and real data sets. The experimental results show that our proposed scheme provides consistent results with significant performance improvements of the distributed vector approximation-tree for strongly clustered or skewed datasets.

Distributed high dimensional indexing for k-NN search

Although conventional index structures provide various nearest-neighbor search algorithms for high-dimensional data, there are additional requirements to increase search performances, as well as to support index scalability for large-scale datasets. To support these requirements, we propose a distributed high-dimensional index structure based on cluster systems, called a Distributed Vector Approximation-tree (DVA-tree), which is a two-level structure consisting of a hybrid spill-tree and Vector Approximation files (VA-files). We also describe the algorithms used for constructing the DVA-tree over multiple machines and performing distributed k-nearest neighbors (NN) searches. To evaluate performances of the DVA-tree, we conduct an experimental study using both real and synthetic datasets. The results show that our proposed method has significant performance advantages over existing index structures on different kinds of dataset.