• Structural Engineering and Mechanics
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• v.37 no.1
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• pp.17-37
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• 2011

This paper presents a new Internet-based computational framework for the realistic simulation of multi-scale response of structural systems. Two levels of parallel processing are involved in this frame work: multiple local distributed computing environments connected by the Internet to form a cluster-to-cluster distributed computing environment. To utilize such a computing environment for a realistic simulation, the simulation task of a structural system has been separated into a simulation of a simplified global model in association with several detailed component models using various scales. These related multi-scale simulation tasks are distributed amongst clusters and connected to form a multi-level hierarchy. The Internet is used to coordinate geographically distributed simulation tasks. This paper also presents the development of a software framework that can support the multi-level hierarchical simulation approach, in a cluster-to-cluster distributed computing environment. The architectural design of the program also allows the integration of several multi-scale models to be clients and servers under a single platform. Such integration can combine geographically distributed computing resources to produce realistic simulations of structural systems.

• Journal of the Korea Society of Computer and Information
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• v.11 no.4 s.42
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• pp.69-75
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• 2006

Internet-based distributed computing environment have been developed for advanced science and engineering by sharing large-scale resources. Therefore efficient scheduling algorithms for allocating user job to resources in the Internet-based distributed computing environment are required. Many scheduling algorithms have been proposed. but these algorithms are not suitable for the Internet-based Distributed computing environment. That is the previous scheduling algorithm does not consider peer self-control. In this paper, we propose a Peer Availability Period Prediction Strategy for Internet-based distributed computing environment and show that our Strategy has better performance than other Strategy through extensive simulation.

• Journal of IKEEE
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• v.13 no.3
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• pp.24-31
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• 2009

The computation time of FEA(finite element analysis) for one model may range from a few seconds up to several hours according to the complexity of the simulated model. If these FEA is used to calculate the objective and the constraint functions during the optimal solution search, it causes very excessive execution time. To resolve this problem, the distributed computing technique using internet web service is proposed in this paper. And the dynamic load balancing mechanisms are established to advance the performance of distributed computing. To verify its validity, this method is applied to a traditional mathematical optimization problem. And the proposed FEA-based optimization using internet distributed computing is applied to the optimal design of the permanent magnet dc motor(PMDCM) for automotive application.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.1
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• pp.117-135
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• 2016

Performance enhancement is one of the most important issues in high performance distributed computing systems. In such computing systems, online users submit their jobs anytime and anywhere to a set of dynamic resources. Jobs arrival and processes execution times are stochastic. The performance of a distributed computing system can be improved by using an effective load balancing strategy to redistribute the user tasks among computing resources for efficient utilization. This paper presents a multi-class load balancing strategy that balances different classes of user tasks on multiple heterogeneous computing nodes to minimize the per-class mean response time. For a wide range of system parameters, the performance of the proposed multi-class load balancing strategy is compared with that of the random distribution load balancing, and uniform distribution load balancing strategies using simulation. The results show that, the proposed strategy outperforms the other two studied strategies in terms of average task response time, and average computing nodes utilization.

• Journal of Korea Multimedia Society
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• v.7 no.5
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• pp.755-765
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• 2004

Recently, the Internet-based distributed/parallel computing using many of idle hosts has been demonstrated its usefulness for processings of a large-scale task and involving several important issues. While executing a large-scale task, the realtime monitoring is required for adaptive strategy of the performance and state change of host. This paper provides the realtime monitoring and visualization on global computing infrastructure called PDP(Parallel Distributed Processing) which is a parallel computing framework implemented with Jana for parallel computing on the Internet.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.2
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• pp.450-470
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• 2023

Traditional cloud computing faces some challenges such as huge energy consumption, network delay and single point of failure. Edge computing is a typical distributed processing platform which includes multiple edge servers closer to the users, thus is more robust and can provide real-time computing services. Although outsourcing data to edge servers can bring great convenience, it also brings serious security threats. In order to provide image retrieval while ensuring users' data privacy, a privacy preserving image retrieval scheme in edge environment is proposed. Considering the distributed characteristics of edge computing environment and the requirement for lightweight computing, we present a privacy-preserving image retrieval scheme in edge computing environment, which two or more "honest but curious" servers retrieve the image quickly and accurately without divulging the image content. Compared with other traditional schemes, the scheme consumes less computing resources and has higher computing efficiency, which is more suitable for resource-constrained edge computing environment. Experimental results show the algorithm has high security, retrieval accuracy and efficiency.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.9
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• pp.3663-3679
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• 2020

Scheduling plays a dynamic role in cloud computing in generating as well as in efficient distribution of the resources of each task. The principle goal of scheduling is to limit resource starvation and to guarantee fairness among the parties using the resources. The demand for resources fluctuates dynamically hence the prearranging of resources is a challenging task. Many task-scheduling approaches have been used in the cloud-computing environment. Security in cloud computing environment is one of the core issue in distributed computing. We have designed a deep learning-based security model for scheduling tasks in cloud computing and it has been implemented using CloudSim 3.0 simulator written in Java and verification of the results from different perspectives, such as response time with and without security factors, makespan, cost, CPU utilization, I/O utilization, Memory utilization, and execution time is compared with Round Robin (RR) and Waited Round Robin (WRR) algorithms.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.8
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• pp.3182-3202
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• 2015

Flexibly expanding the storage capacity required to process a large amount of rapidly increasing unstructured log data is difficult in a conventional computing environment. In addition, implementing a log processing system providing features that categorize and analyze unstructured log data is extremely difficult. To overcome such limitations, we propose and design a MongoDB-based unstructured log processing system (MdbULPS) for collecting, categorizing, and analyzing log data generated from banks. The proposed system includes a Hadoop-based analysis module for reliable parallel-distributed processing of massive log data. Furthermore, because the Hadoop distributed file system (HDFS) stores data by generating replicas of collected log data in block units, the proposed system offers automatic system recovery against system failures and data loss. Finally, by establishing a distributed database using the NoSQL-based MongoDB, the proposed system provides methods of effectively processing unstructured log data. To evaluate the proposed system, we conducted three different performance tests on a local test bed including twelve nodes: comparing our system with a MySQL-based approach, comparing it with an Hbase-based approach, and changing the chunk size option. From the experiments, we found that our system showed better performance in processing unstructured log data.

• Journal of Internet Computing and Services
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• v.17 no.5
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• pp.67-74
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• 2016

Distributed agile software development faces difficulties for instance lack of visibility across development and delivery processes, complex and disjointed development processes, inability to capitalize on business opportunities, lack of communication agility between disconnected owners, development teams, and users or clients. However these difficulties are solved with the help of cloud computing services. This study proposes a framework to provide a skeletal or structural environment for distributed agile software development in cloud computing environment. The framework guide towards the best tooling to deliver a consistent, automated, governed, and unified agile software development process with reduced technical debt, and minimized project backlog. In addition to this, the study highlights the benefits of cloud computing in agile software development.

• Smart Media Journal
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• v.4 no.3
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• pp.62-67
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• 2015

The trend keywords in ICT sector will be Big Data, Internet of Things, and Cloud Computing. The rear end to support those techniques requires a large-capacity storage technology of low-cost. Therefore, we proposed the prototype of low-cost and mass distributed storage system based on PC using open-source Ceph FS for SMB.