Modeling and Analyzing Distributed Computation in Monotone Spaces with Structural Map

Susmit Bagchi 보안공학연구지원센터 2016 International Journal of Software Engineering and Vol.10 No.4

Distributed computation follows the models of discrete structures in combinatorial forms. In higher-dimensions, the simplex structures of topological spaces as well as homology are employed to model and analyze distributed asynchronous computations. However, the monotone spaces are the general forms of topological spaces and can be effectively employed to analyze distributed computation. This paper proposes an analytical model of distributed computation in monotone spaces. It is illustrated that, the modeling of distributed computation in monotone spaces helps in determining consistent cuts under closure and convergence of computation. Furthermore, a connective mapping between the simplexes and monotone is constructed.

A Study on a Simple Algorithm for Parallel Computation of a Grid-Based One-Dimensional Distributed Rainfall-Runoff Model

최윤석,신문주,김경탁 대한토목학회 2020 KSCE Journal of Civil Engineering Vol.24 No.2

This paper presents an algorithm that can efficiently simulate a grid-based one-dimensional distributed rainfall-runoff model by performing parallel computations using flow accumulation values for individual grid cells, which are calculated through an eight flow direction method. This parallel computation algorithm uses information about flow accumulation to automatically find parallel computation target grid cells within the overall area and perform parallel computation on the grid by unit. The Microsoft .NET Parallel class was used to apply and evaluate the parallel computation algorithm independently on two machines. The results showed that the time reduction effect of parallel computation differed for each target domain, because flow accumulation values varied depending on the domain. Parallel computation reduced computation time by around 40% to 78% in virtual domains and around 63% in the real domain compared to sequential computation. The results of this study can be utilized to reduce the computation time of distributed models.

Software architecture and algorithm for reliable RPC for geo-distributed mobile computing systems

Khan, Asmat Ullah,Bagchi, Susmit North-Holland 2018 Future generations computer systems Vol.86 No.-

<P><B>Abstract</B></P> <P>Remote Procedure Call (RPC) is a computing as well as communication model for distributed processes to execute client routines on remote servers in the distributed systems. Due to the evolution of geo-distributed mobile cloud computing systems, mobile devices are exposed to frequent disconnection due to limited battery lifetime, processing capacity and network bandwidth while roaming globally. The existing standard RPC and mobile RPC frameworks are not completely suitable for applications in geo-distributed mobile cloud computing. This paper proposes a novel software architecture and associated algorithms for realizing reliable RPC under global mobility of clients. The stateful server chaining and multiple authentication primitives are employed in the proposed design to achieve security as well as location transparency. The software architecture is implemented on heterogeneous testbed and evaluated with promising results. The heterogeneity of mobile cloud platform is considered in the design by employing specific XDR format enhancing portability. A detailed comparative analysis of the proposed design is included in the paper.</P> <P><B>Highlights</B></P> <P> <UL> <LI> GMCC-RPC: Reliable mobile RPC for geo-distributed systems. </LI> <LI> Software architecture for mobile and reliable RPC for geo-distributed systems. </LI> <LI> Mobile and Reliable RPC using server chains in geo-distributed systems. </LI> </UL> </P>

딥러닝을 사용하는 IoT빅데이터 인프라에 필요한 DNA 기술을 위한 분산 엣지 컴퓨팅기술 리뷰

( Temesgen Seyoum Alemayehu ),조위덕 ( We-duke Cho ) 한국정보처리학회 2020 정보처리학회논문지. 컴퓨터 및 통신시스템 Vol.9 No.12

오늘날 데이터 네트워크 AI (DNA) 기반 지능형 서비스 및 애플리케이션은 비즈니스의 삶의 질과 생산성을 향상시키는 새로운 차원의 서비스를 제공하는 것이 현실이 되었다. 인공지능(AI)은 IoT 데이터(IoT 장치에서 수집한 데이터)의 가치를 높이며, 사물 인터넷(IoT)은 AI의 학습 및 지능기능을 촉진한다. 딥러닝을 사용하여 대량의 IoT 데이터에서 실시간으로 인사이트를 추출하려면 데이터가 생성되는 IoT 단말 장치에서의 처리능력이 필요하다. 그러나 딥러닝에는 IoT 최종 장치에서 사용할 수 없는 상당 수의 컴퓨팅 리소스가 필요하다. 이러한 문제는 처리를 위해 IoT 최종 장치에서 클라우드 데이터 센터로 대량의 데이터를 전송함으로써 해결되었다. 그러나 IoT 빅 데이터를 클라우드로 전송하면 엄청나게 높은 전송 지연과 주요 관심사인 개인 정보 보호 문제가 발생한다. 분산 컴퓨팅 노드가 IoT 최종 장치 가까이에 배치되는 엣지 컴퓨팅은 높은 계산 및 짧은 지연 시간 요구 사항을 충족하고 사용자의 개인 정보를 보호하는 실행 가능한 솔루션이다. 본 논문에서는 엣지 컴퓨팅 내에서 딥러닝을 활용하여 IoT 최종 장치에서 생성된 IoT 빅 데이터의 잠재력을 발휘하는 현재 상태에 대한 포괄적인 검토를 제공한다. 우리는 이것이 DNA 기반 지능형 서비스 및 애플리케이션 개발에 기여할 것이라고 본다. 엣지 컴퓨팅 플랫폼의 여러 노드에서 딥러닝 모델의 다양한 분산 교육 및 추론 아키텍처를 설명하고 엣지 컴퓨팅 환경과 네트워크 엣지에서 딥러닝이 유용할 수 있는 다양한 애플리케이션 도메인에서 딥러닝의 다양한 개인정보 보호 접근 방식을 제공한다. 마지막으로 엣지 컴퓨팅 내에서 딥러닝을 활용하는 열린 문제와 과제에 대해 설명한다. Nowadays, Data-Network-AI (DNA)-based intelligent services and applications have become a reality to provide a new dimension of services that improve the quality of life and productivity of businesses. Artificial intelligence (AI) can enhance the value of IoT data (data collected by IoT devices). The internet of things (IoT) promotes the learning and intelligence capability of AI. To extract insights from massive volume IoT data in real-time using deep learning, processing capability needs to happen in the IoT end devices where data is generated. However, deep learning requires a significant number of computational resources that may not be available at the IoT end devices. Such problems have been addressed by transporting bulks of data from the IoT end devices to the cloud datacenters for processing. But transferring IoT big data to the cloud incurs prohibitively high transmission delay and privacy issues which are a major concern. Edge computing, where distributed computing nodes are placed close to the IoT end devices, is a viable solution to meet the high computation and low-latency requirements and to preserve the privacy of users. This paper provides a comprehensive review of the current state of leveraging deep learning within edge computing to unleash the potential of IoT big data generated from IoT end devices. We believe that the revision will have a contribution to the development of DNA-based intelligent services and applications. It describes the different distributed training and inference architectures of deep learning models across multiple nodes of the edge computing platform. It also provides the different privacy-preserving approaches of deep learning on the edge computing environment and the various application domains where deep learning on the network edge can be useful. Finally, it discusses open issues and challenges leveraging deep learning within edge computing.

FEA-Based Optimal Design of Permanent Magnet DC Motor Using Internet Distributed Computing

Lee, Cheol-Gyun,Choi, Hong-Soon 한국전기전자학회 2009 전기전자학회논문지 Vol.13 No.3

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.

FEA–Based Optimal Design of Permanent Magnet DC Motor Using Internet Distributed Computing

이철균,최홍순 한국전기전자학회 2009 전기전자학회논문지 Vol.13 No.3

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.

A Protocol Model of S3 Computing Designed for Learning Community Platform of College Teachers

Liang Jia,Liuhong Yan 보안공학연구지원센터 2016 International Journal of Future Generation Communi Vol.9 No.7

S3 Computing requires distributed computing performed by social network platform has high scalability and security. Protocol models meeting the requirements of S3 Computing not only ensure the correctness and robustness of distributed computing, but also reduce risks introduced by involvement of nodes with low reputation in computing. These models safeguard the data collections and computations performed on platform of teacher’s learning community for social researches. This paper constructs a protocol model entitled which adapts platform of teacher’s learning community and meets the requirements of S3 Computing. This protocol model is the key step of implementing distributed computations on learning community platform.

Strategy for Task Offloading of Multi-user and Multi-server Based on Cost Optimization in Mobile Edge Computing Environment

( Yanfei He ),( Zhenhua Tang ) 한국정보처리학회 2021 Journal of information processing systems Vol.17 No.3

With the development of mobile edge computing, how to utilize the computing power of edge computing to effectively and efficiently offload data and to compute offloading is of great research value. This paper studies the computation offloading problem of multi-user and multi-server in mobile edge computing. Firstly, in order to minimize system energy consumption, the problem is modeled by considering the joint optimization of the offloading strategy and the wireless and computing resource allocation in a multi-user and multi-server scenario. Additionally, this paper explores the computation offloading scheme to optimize the overall cost. As the centralized optimization method is an NP problem, the game method is used to achieve effective computation offloading in a distributed manner. The decision problem of distributed computation offloading between the mobile equipment is modeled as a multi-user computation offloading game. There is a Nash equilibrium in this game, and it can be achieved by a limited number of iterations. Then, we propose a distributed computation offloading algorithm, which first calculates offloading weights, and then distributedly iterates by the time slot to update the computation offloading decision. Finally, the algorithm is verified by simulation experiments. Simulation results show that our proposed algorithm can achieve the balance by a limited number of iterations. At the same time, the algorithm outperforms several other advanced computation offloading algorithms in terms of the number of users and overall overheads for beneficial decision-making.

분산컴퓨팅 환경에서 공력 설계최적화의 효율성 연구

김양준(Y.-J. Kim),정현주(H.-J. Jung),김태승(T.-S. Kim),손창호(C.-H. Son),조창열(C.-Y. Joh) 한국전산유체공학회 2006 한국전산유체공학회지 Vol.11 No.2

A research to evaluate the efficiency of design optimization was carried out for aerodynamic design optimization problem in distributed computing environment. The aerodynamic analyses which take most oj computational work during design optimization were divided into several jobs and allocated to associated PC clients through network. This is not a parallel process based on domain decomposition in a single analysis rather than a simultaneous distributed-analyses using network-distributed computers. GBOM(gradient-based optimization method), SAO(Sequential Approximate Optimization) and RSM(Response Surface Method) were implemented to perform design optimization of transonic airfoils and evaluate their efficiencies. One dimensional minimization followed by direction search involved in the GBOM was found an obstacle against improving efficiency of the design process in the present distributed computing system. The SAO was found fairly suitable for the distributed computing environment even it has a handicap of local search. The RSM is apparently the most efficient algorithm in the present distributed computing environment, but additional trial and error works needed to enhance the reliability of the approximation model deteriorate its efficiency from the practical point of view.

자바를 위한 분산된 병렬 컴퓨팅 환경

이상윤,김승호 대한전자공학회 2004 電子工學會論文誌-CI (Computer and Information) Vol.41 No.6

자바의 쓰레드는 다중 처리 환경에서 하나의 프로그램 공간 내의 독립적인 프로세스로 취급되는 객체 요소이므로 병렬처리를 위한 독립적인 프로세스로 활용할 수 있다. 또한, 자바의 동기화 메커니즘과 쓰레드를 활용하면 병렬 처리를 수행하는 응용프로그램을 쉽게 작성할 수 있다. 이에 따라, 자바의 병렬 처리 지원 기능을 분산된 컴퓨팅 환경에 적용하기 위한 많은 연구 결과가 있다. 본 논문에서는 레거시 자바 프로그램에 포함된 쓰레드를 분산된 컴퓨팅 환경에서 병렬 수행 하도록 지원하는 시스템 환경을 제안한다. TORB(Transparent Object Request Broker)라고 명명된 본 시스템은 프로그래밍 투명성을 지원하므로 이미 작성된 레거시 자바 프로그램을 간단한 변환 과정을 거친 후 병렬 수행 하도록 지원한다. TORB는 본 연구팀에서 이미 발표한 분산 프로그래밍 도구의 기능을 확장한 것이며, 이는 지정된 기능을 지정된 컴퓨터에서 수행하도록 지원하는 전형적인 분산처리 기능만을 보유하고 있었다. Since java thread is an object which is treated as independent process within one execution space in the multiprocessing environment, we can use it for independent process of parallel processing. Using thread and synchronization mechanism of java enables us to write parallel application program easily. Therefore, a lot of results are exist which is apply the feature of java that support parallel processing to the distributed computing environment. In this paper, we introduce a system of environment that support parallel execution of thread which is included in legacy java program. The system named TORB(Transparent Object Request Broker) enables us parallel execution of legacy java program after simple converting process, since it support the feature of programming transparency. TORB is extended version of distributed programming tool that is published by our research team. And it had only typical distributed processing feature that is execute a specified function at the specified computer.