Earticle

클러스터링은 상호 의존적 구성에 필요한 구조적인 기술이다. 클러스터링은 가변적 업무부하를 처리하거나, 서비스 연속성을 저해하는 고장 발생 시 운영이 계속되도록 여러대의 컴퓨터시스템 기능을 서로 연결하는 메커니즘이다. 고 가 용성 클러스터링 기능은 가능한 오랜시간 서버 시스템이 작동하는데 중점을 둔다. 이 클러스터 는 멀티플 시스템 에서 실행되는 노드와 서비스를 중복하여 가지고 있어서 서로가 서로를 추적할 수 있다. Active-Standby 상태의 두 시스템 이 있을 경우 활성 서버에 장애가 발생했을 때 모든 서비스가 대기 서버에서 구동돼 서비스가 이루어진다. 이 기능을 절체 또는 스위치오버(switchover)라 한다. 고가용성 클러스터링 기능은 가능한 오랜시간 서버 시스템이 작동하는데 중 점을 둔다. 이 클러스터는 멀티플 시스템 에서 실행되는 노드와 서비스를 중복하여 가지고 있어서 서로가 서로를 추적 할 수 있다. 한 노드가 장애 발생 시 둘째 노드가 몇초 이내에 고장 난 노드 임무를 수행한다. 고가용성 클러스터링 구 조는 효율성 여부가 측정되어야 한다. 시스템 성능은 인프라시스템의 performance, latency, 응답시간(responseTime), CPU 부하율(CPU utilization), CPU상의 시스템 프로세스(system process)수로 대표된다.

Clustering is required to configure clustering interdependent structural technology. Clustering handles variable workloads or impede continuity of service to continue operating in the event of a failure. Long as high-availability clustering feature focuses on server operating systems.Active-standby state of two systems when the active server fails, all services are running on the standby server, it takes the service. This function switching or switchover is called failover. Long as high-availability clustering feature focuses on server operating systems. The cluster node that is running on multiple systems and services have to duplicate each other so you can keep track of. In the event of a node failure within a few seconds the second node, the node shall perform the duties broken. Structure for high-availability clustering efficiency should be measured. System performance of infrastructure systems performance, latency, response time, CPU load factor(CPU utilization), CPU processes on the system (system process) channels are represented.

본 논문은 클라우드 컴퓨팅 환경에서 다양한 분산 멀티미디어 서비스를 효율적으로 제공하기 위한 새로운 시맨틱 버 스 모델을 제안한다. 제안하는 시맨틱 버스 모델은 1) 계층적으로 함께 협력하는 애플리케이션과 전송 바인딩으로 구성되어 있으며, 2) 버스의 실시간 동적 적응을 지원하기 위해, 형상 제어 공간과 기능 제어 공간으로 구성된 메타 공간을 갖으며, 3) 프로파일 기반 서비스 및 자원 관리를 지원한다. 4) 또한, 제안하는 시맨틱 버스를 생성하기 위 한 인터페이스를 Backus-Naur Form 정의를 기반으로 제안하였다. 실험을 통해 제안된 시맨틱 버스 모델이 기존 의 균일 non-시맨틱 모델에 비해 향상된 처리량을 제공함을 검증하였으며, 제안된 버스 모델은 차세대 컴퓨팅 플랫 폼에서 서비스 제공 및 데이터 전송을 위한 핵심 인프라로 활용될 수 있다.

This paper proposes a new semantic bus model to efficiently provide multimedia services in a cloud computing environment. The proposed semantic bus model 1) consists of applications and transport bindings that cooperate together hierarchically; 2) has a metaspace consisting of a configuration control space and a function control space to support real-time dynamic adaptation of the bus; 3) supports profile-based service and resource management; 4) We also proposed Backus-Naur Form (BNF) defintion based interface specification that generates the proposed semantic bus models. Through experiments, it was verified that the proposed semantic bus model provides improved throughput compared to the existing non-semantic model. The proposed bus model can be used as a core infrastructure for service provision and data transmission in next-generation computing platforms.

위치기반 서비스란 휴대폰과 같은 모바일 단말기 속에 위성 항법장치(GPS)와 연결되는 칩을 부착하여 위치추적 서비스, 공공안전 서비스, 위치기반 정보 서비스 등 위치와 관련된 각종 정보를 제공하는 서비스를 일컫는다. 그리고 클라우드 컴퓨팅이란 개인용 컴퓨터 또는 기업의 서버에 개별적으로 저장해 두었던 자료와 소프트웨어들을 클라우드 클러스터로 구축하여 필요할 때 PC나 휴대폰 같은 각종 단말기를 이용하여 원격 작업을 수행할 수 있는 환경을 의미한다. 본 논문에서는 구글의 연구로부터 시작된 클라우드 컴퓨팅 기술에 주목하고 클라우드 컴퓨팅 환경을 위한 대용량 이동 객체의 정보를 저장하고 질의하는 시스템에 대하여 연구하였다. 그리고 위치 기반 서비스를 위한 대용량 이동객체에 대하여 효율적인 질의를 수행할 수 있는 저장 구조를 제안하였다. 본 논문에서 제안한 방법을 적용한 시스템의 효율성을 증명하기 위해 저장 및 질의 성능을 다양한 측면에서 실험하고, 실험 결과를 다른 분산처리 시스템과의 비교하여 성능을 입증하였다.

The location-based service is referred to as the service for providing various types of information regarding such as the location-tracing service, the public-safety service and the location-based information service by attaching chips connected with the GPS to the mobile phone. In addition, cloud computing means the environment which makes it possible to carry out remote operations by establishing cloud clusters with the data and software which have been individually stored in personal computer or corporate servers and using devices such as personal computers and cellular phones whenever it is necessary. Throughout this paper, the cloud computing technology which originates from the research by Google the system has been focused on and the system for storing a large volume of information for moving object and making query for cloud computing environment has been studied. Besides, the storing structure for executing efficient query regarding the high-capacity mobile devices for the location-based service has been suggested. In order to prove the efficiency of the system with the application of the method suggested in this paper, the storage and qualitative functions have been experimented in various aspects and compared with other distributed processing system's performance.

본 논문에서는 모바일 컴퓨팅 환경에서의 디바이스 연계 통합을 위한 스토리지 가상화 기술로서, PosCFS+ 분산파일 서비스를 소개한다. PosCFS+ 분산 파일 서비스는 사용자 영역 네트워크(PAN) 상의 장치별 스토리지 공간을 하나의 가상 스토리지 공간으로 자동 구성하며, 구성된 가상 스토리지 공간의 파일 입출력을 위하여 파일 메타데이터를 기준으로 가상 디렉토리를 동적으로 구성함으로써, 사용자의 데이터 접근성 개선 및 데이터 통합 관리를 지원한다.

In this paper, we present the design and implementation of a distributed file service, PosCFS+, for device ensembles. All the storages in the network could be integrated automatically into a virtual storage by the file service and then the service creates virtual directories to provide a semantic file addressing based on the file metadata on the virtual storage. Through the virtualization technique, the file service is able to enhance the data accessibility.

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.

Frequent Itemset Mining (FIM) is one of most fundamental techniques in data mining with extensive applications to a variety of data mining problems such as association rule mining, correlations, clustering and classification. Since the first proposal of frequent itemset mining, numerous serial algorithms have been proposed in order to improve mining performance, yet most of them cannot scale to massive datasets which are very common nowadays. In this paper, we propose a new parallel FIM algorithm named PFIN based on Nodeset which is a more efficient data structure for mining frequent itemsets. PFIN can intelligently decompose a large-scale FIM problem into a set of tasks, where each task can be executed in parallel without unnecessary communication overheads. Moreover, a hash-based load balancing strategy has been adopted to optimize resource use and maximize throughput. For evaluating the performance of PFIN, we have conduct extensive experiments on Spark which is an emerging distributed in-memory processing framework to compare it against PFP which is one of state-of-the-art parallel FIM algorithms on a range of real datasets. The experimental results demonstrate that our proposed PFIN are highly competitive with PFP in scalability performance, outperforming PFP in speed performance.

To meet the demands of the large-scale network simulation platform technology, we have improved the classical Min-min algorithm widely used in Grid application. We proposed a novel multi-task network simulation scheduling algorithm based on multi-valued mapping and the new algorithm is called MUNS-Min-min, we also discussed how to determine the weight of simulation time and the resource consumption. Experimental results show that our algorithm is suitable for complex computing environment, and the performance of the simulation platform has improved nearly 20%, compared with traditional scheduling algorithms.

The huge amount of biological information implies a great challenge for data analysis, particularly for combinatorial methods such as Multifactor Dimensionality Reduction. This method can be computationally intensive, especially when more than ten polymorphisms need to be evaluated. The Grid is a promising architecture for genomics problems providing high computing capabilities. In this paper, we describe a framework for supporting the MDR method on Grid environments. This framework helps biologists to automate the execution of multiple tests of gene-gene interactions detection. To evaluate the eciency of the proposed framework, we conduct experiments on the Grid5000. A Grid infrastructure distributed in nine sites around France, for research in large-scale parallel and distributed systems. compute-intensive

In recent years, distributed computing technology has been one of the cutting edge technologies for its low power and cost, which makes numerous IT organizations extend their hands in order to improve their financial ability. Because of these new features, grid computing, the original task scheduling mechanism, can’t work effectively in distributed computing environments, hence, we need a new task scheduling method to solve the problems. With considering the complex characters of the task in different distributed computing applications, firstly, we construct a more comprehensive task scheduling model, which has three sub objective functions. Secondly, we present an improved genetic algorithm to solve the multi- objective NP-hard problem. Finally, we implement some simulation experiments, and the evaluation results show us that the proposed model and improved GA are efficient and effective. The first part is the research status and related problems. The second part is the establishment of system architecture and task scheduling model. The last part is the experimental analysis and conclusion.

With the rapid development of embedded system and Internet of Things technology, embedded system and smart device based on embedded system is collecting huge amounts of data, and corresponding data processing and application method have been greatly changed, different from the traditional big data and cloud computing focus , local processing and application become important trend. This paper tries to take Cortex-A7 as the main system with the stronger ability , Hadoop distributed computing system is deployed in embedded system and could meet the demand of the future data process with directly managing the resource-constrained sensors, embedded systems and smart devices . Successfully deploying over 20GB data in the test, the system is verified that it can complete most of the functions of data processing cluster , and can also manage the collected sensors and embedded system terminal ,with better research and market promotional value.

Runtime verification aims to check whether an application executes its behaviour as specified. Thereby the active execution trace of an application is checked in terms of the actual execution context; diagnosis and, possibly, recovery actions are taken when the specification is violated. In today’s practices, software is increasingly distributed over multiple processes, potentially running at different locations. This is supported by middleware that, to some extent, allows implementing applications in a distribution-transparent way. To enable effective runtime verification in distributed software we focus in this paper on three requirements: (1) distribution-transparent specification of software behaviour, as software is also implemented in such a way, (2) end-to-end verification of behaviour, and (3) automatic generation of verification modules for arbitrary process and distribution structures. We present a novel runtime verification approach satisfying these requirements and present its implementation in the EventChaser system. Furthermore we present an approach that at least minimizes the distribution-awareness of specifications to an acceptable level for software not using supported middleware for inter-process communication.

The information that caused by the complex network is massive, but because of a large amount of information, so the use of traditional data analysis has been unable to meet the search and mining complex network data, so the distributed computing mode, i.e., cloud computing has become the main method of calculation method of complex network, according to the calculation of cloud computing, it needs to consider computing topological partition method and the computational performance. This paper presents a data mining model matrix, according to this model can integrate different information, optimization of data mining, so as to improve the efficiency of complex network distributed computing.