An Efficient Implementation of Mobile Raspberry Pi Hadoop Clusters for Robust and Augmented Computing Performance

( Kathiravan Srinivasan ),( Chuan-yu Chang ),( Chao-hsi Huang ),( Min-hao Chang ),( Anant Sharma ),( Avinash Ankur ) 한국정보처리학회 2018 Journal of information processing systems Vol.14 No.4

Rapid advances in science and technology with exponential development of smart mobile devices, workstations, supercomputers, smart gadgets and network servers has been witnessed over the past few years. The sudden increase in the Internet population and manifold growth in internet speeds has occasioned the generation of an enormous amount of data, now termed ‘big data’. Given this scenario, storage of data on local servers or a personal computer is an issue, which can be resolved by utilizing cloud computing. At present, there are several cloud computing service providers available to resolve the big data issues. This paper establishes a framework that builds Hadoop clusters on the new single-board computer (SBC) Mobile Raspberry Pi. Moreover, these clusters offer facilities for storage as well as computing. Besides the fact that the regular data centers require large amounts of energy for operation, they also need cooling equipment and occupy prime real estate. However, this energy consumption scenario and the physical space constraints can be solved by employing a Mobile Raspberry Pi with Hadoop clusters that provides a cost-effective, low-power, high-speed solution along with micro-data center support for big data. Hadoop provides the required modules for the distributed processing of big data by deploying map-reduce programming approaches. In this work, the performance of SBC clusters and a single computer were compared. It can be observed from the experimental data that the SBC clusters exemplify superior performance to a single computer, by around 20%. Furthermore, the cluster processing speed for large volumes of data can be enhanced by escalating the number of SBC nodes. Data storage is accomplished by using a Hadoop Distributed File System (HDFS), which offers more flexibility and greater scalability than a single computer system.

An Efficient Implementation of Mobile Raspberry Pi Hadoop Clusters for Robust and Augmented Computing Performance

Srinivasan, Kathiravan,Chang, Chuan-Yu,Huang, Chao-Hsi,Chang, Min-Hao,Sharma, Anant,Ankur, Avinash Korea Information Processing Society 2018 Journal of information processing systems Vol.14 No.4

Rapid advances in science and technology with exponential development of smart mobile devices, workstations, supercomputers, smart gadgets and network servers has been witnessed over the past few years. The sudden increase in the Internet population and manifold growth in internet speeds has occasioned the generation of an enormous amount of data, now termed 'big data'. Given this scenario, storage of data on local servers or a personal computer is an issue, which can be resolved by utilizing cloud computing. At present, there are several cloud computing service providers available to resolve the big data issues. This paper establishes a framework that builds Hadoop clusters on the new single-board computer (SBC) Mobile Raspberry Pi. Moreover, these clusters offer facilities for storage as well as computing. Besides the fact that the regular data centers require large amounts of energy for operation, they also need cooling equipment and occupy prime real estate. However, this energy consumption scenario and the physical space constraints can be solved by employing a Mobile Raspberry Pi with Hadoop clusters that provides a cost-effective, low-power, high-speed solution along with micro-data center support for big data. Hadoop provides the required modules for the distributed processing of big data by deploying map-reduce programming approaches. In this work, the performance of SBC clusters and a single computer were compared. It can be observed from the experimental data that the SBC clusters exemplify superior performance to a single computer, by around 20%. Furthermore, the cluster processing speed for large volumes of data can be enhanced by escalating the number of SBC nodes. Data storage is accomplished by using a Hadoop Distributed File System (HDFS), which offers more flexibility and greater scalability than a single computer system.

Suture Repair in Endoscopic Surgery for Craniovertebral Junction

Mei-Yin Yeh,Wen-Cheng Huang,Jau-Ching Wu,Chao-Hung Kuo,Hsuan-Kan Chang,Tsung-Hsi Tu,Peng-Yuan Chang,Yu-Shu Yen,Henrich Cheng 대한척추신경외과학회 2019 Neurospine Vol.16 No.2

Objective: Endoscopic approaches to the craniovertebral junction (CVJ) have been established as viable and effective surgical treatments in the past decade. One of the major complications is leakage of the cerebrospinal fluid (CSF). This study aimed to investigate the efficacy and feasibility of suture closure at the nasopharyngeal mucosa upon durotomy. Methods: A series of consecutive patients who underwent different endoscopic approaches to the CVJ were retrospectively reviewed. The pathologies, surgical corridors, neurological and functional outcomes, radiological evaluations, and complications were analyzed. Different strategies of repair for the intraoperative CSF leakage were described and compared. Results: A total of 22 patients covering 13 years were analyzed. There were 12, 2, and 8 patients who underwent transnasal, transoral, and combined approaches, respectively. There were 8 patients (36.4%) who experienced intraoperative CSF leakage, and were grouped into 2: 4 in the nonsuture (NS) group and 4 in the suture-repaired (SR) group. The NS group had 3 (75%) persistent CSF leakages postoperation that caused 1 mortality, whereas patients of the SR group had only 1 minor CSF rhinorrhea that healed spontaneously within days. Conclusion: In this series of 22 patients who required anterior endoscopic resection of pathologies at the CVJ, there was 1 (4.5%) serious complication related to CSF leakage. For patients who had no durotomy, the mucosal incision at the nasopharynx usually healed rapidly and there were few procedure-related complications. For patients with intraoperative CSF leakage, suture closure was technically challenging but could significantly lower the risks of postoperative complications.

Measurement of Deformity at the Craniovertebral Junction: Correlation of Triangular Area and Myelopathy

Chih-Chang Chang,Jau-Ching Wu,Chin-Chu Ko,Hsuan-Kan Chang,Yi-Hsuan Kuo,Chao-Hung Kuo,Tsung-Hsi Tu,Wen-Cheng Huang 대한척추신경외과학회 2022 Neurospine Vol.19 No.4

Objective: Diseases of the craniovertebral junction (CVJ) are commonly associated with deformity, malalignment, and subsequent myelopathy. The misaligned CVJ might cause compression of neuronal tissues and subsequently clinical symptoms. The triangular area (TA), measured by magnetic resonance imaging/images (MRI/s), is a novel measurement for quantification of the severity of compression to the brain stem. This study aimed to assess the normal and pathological values of TA by a comparison of patients with CVJ disease to age- and sex-matched controls. Moreover, postoperative TAs were correlated with outcomes. Methods: Consecutive patients who underwent surgery for CVJ disease were included for comparison to an age- and sex-matched cohort of normal CVJ persons as controls. The demographics, perioperative information, and pre- and postoperative 2-year cervical MRIs were collected for analysis. Cervical TAs were measured and compared. Results: A total of 201 patients, all of whom had pre- or postoperative MRI, were analyzed. The TA of the CVJ deformity group was larger than the healthy control group (1.62 ± 0.57 cm2 vs. 1.01 ± 0.18 cm2 , p < 0.001). Moreover, patients who had combined anterior odontoidectomy and posterior laminectomy with fixation had the greatest reduction in the TA (1.18 ± 0.58 cm2 ). Conclusion: In CVJ deformity, the measurement of the cervical TA could indicate the severity of brain stem compression. After surgery, the TA had a varying degree of improvement, which could represent the efficacy of surgery.