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.

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>

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.

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.

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.

Load Balancing Through Arranging Task With Completion Time

Palash Samanta,Ranjan Kumar Mondal 보안공학연구지원센터 2016 International Journal of Grid and Distributed Comp Vol.9 No.5

Nowadays, different types of bandwidth eater are growing rapidly. Cloud computing as an Internet computing has propagate day by day to provide different type of accommodations and resources to web utilizer. Cloud computing employs Internet resources to execute sizably voluminous-scale tasks. Ergo, to cull felicitous node to execute a task is able to enhance the performance of astronomically immense-scale cloud computing environment. There are several different nodes in a cloud computing system. Namely, each node has different capability to execute task; hence, only consider the CPU remaining of the node is not enough when a node is opted to execute a task. Consequently, how to select an efficient node to execute a task is very consequential in a cloud computing.In this paper, we propose a scheduling algorithm, Load Balancing through Arranging Task with Completion Time, LBATCT which combines minimum completion time and load balancing strategies. For the case study, LBATCT can provide efficient utilization of computing resources and maintain the load balancing in cloud computing environment.

클라우드 컴퓨팅에서의 대용량 데이터 처리와 관리 기법에 관한 조사

이경하(Kyong-Ha Lee),최현식(Hyunsik Choi),정연돈(Yon Dohn Chung) 한국정보과학회 2011 정보과학회논문지 : 데이타베이스 Vol.38 No.2

클라우드 컴퓨팅은 규모의 경제를 실현할 수 있는 큰 데이터 센터로부터 컴퓨팅을 필요에 따라 임대하여 이용할 수 있게 한다. 보다 저렴하고 효과적인 컴퓨팅 방법을 제공함에 따라 클라우드 컴퓨팅은 IT 기업과 학계 양쪽의 많은 관심을 받고 있다. 클라우드 컴퓨팅에서의 데이터 처리는 특정 데이터센터들을 구성하는 수천, 수만 대의 노드 컴퓨터를 이용한 병렬 처리로 수행된다. 이러한 비공유 구조 상의 병렬 처리는 전통적인 분산, 병렬 컴퓨팅 분야에서 그간 많이 연구되어 왔으나, 전통적인 병렬 처리와는 구별되는 특징이 있다. 또한 클라우드 컴퓨팅에서의 데이터 관리는 DBMS와 같은 전통적 데이터 관리 기법과는 많은 차이를 가진다. 이러한 차이는 고성능, 고가용성을 위한 데이터 복제의 적극적인 이용, DBMS에서보다 더 완화된 일관성 모델, 좀더 단순한 접근 패턴과 유연한 데이터 모델 등에 기인한다. 이 논문에서는 현재 클라우드 컴퓨팅 분야에 적용된 여러 데이터 처리/관리에 관한 기법들을 조사한다. 또한, 컴퓨팅 작업의 성격에 따른 적합한 데이터 처리/관리 기술의 선택을 위한 가이드라인을 제시한다. 마지막으로 클라우드 컴퓨팅에서의 연구 이슈들과 도전 분야들을 소개한다. Cloud computing, "an old idea whose time has finally come" [1], has been gaining much interest from both IT industry and academia since it promises a cheaper and better way of computing by leasing their computing from the data centers of IT companies which are big enough to realize the economy of scale. In cloud computing, data processing is parallelized across tens of thousands of node computers which compose certain data centers. Although traditional distributed and parallel computing models such as parallel DBMS have already been discussed for many years, there are distinct differences between cloud computing and the conventional parallel processing. In addition, managing data in the cloud immensely differs from the conventional DBMS techniques in terms of the aggressive use of data replication for high performance and availability, less strong consistency model, simpler access patterns and the support of less-rigid data model. This paper gives readers a brief survey of techniques for processing and for managing data in cloud computing. Including the current techniques adopted in cloud computing, authors give guidelines about how to select relevant techniques with given work type and workload. Finally, we suggest research issues and opportunities in cloud data processing and management.

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.

Volume Rendering using Grid Computing for Large-Scale Volume Data

Nishihashi, Kunihiko,Higaki, Toru,Okabe, Kenji,Raytchev, Bisser,Tamaki, Toru,Kaneda, Kazufumi Society for Computational Design and Engineering 2009 International Journal of CAD/CAM Vol.9 No.1

In this paper, we propose a volume rendering method using grid computing for large-scale volume data. Grid computing is attractive because medical institutions and research facilities often have a large number of idle computers. A large-scale volume data is divided into sub-volumes and the sub-volumes are rendered using grid computing. When using grid computing, different computers rarely have the same processor speeds. Thus the return order of results rarely matches the sending order. However order is vital when combining results to create a final image. Job-Scheduling is important in grid computing for volume rendering, so we use an obstacle-flag which changes priorities dynamically to manage sub-volume results. Obstacle-Flags manage visibility of each sub-volume when line of sight from the view point is obscured by other subvolumes. The proposed Dynamic Job-Scheduling based on visibility substantially increases efficiency. Our Dynamic Job-Scheduling method was implemented on our university's campus grid and we conducted comparative experiments, which showed that the proposed method provides significant improvements in efficiency for large-scale volume rendering.