Earticle

In the era of Cloud Computing, the Infrastructure-as-a-Service is one of the key issues to provide feasible applications to enhance customer’s services. Cloud Computing Service model aims to provide Everything as a Service (XaaS) over Internet. It is still necessary to integrate infrastructure to have rich resource management for the Cloud Computing requirements. Grid Computing has been a popular solution in providing infrastructure and computational resources as service. Grid computing Infrastructure, despite various progresses, still has an issue of providing dedicated customized and controllable remote execution environment. Virtualization has been provided as one solution for the issue. Virtualization in grid environment can be extended to the cloud computing context to provide the Grid Infrastructure as a Service to the cloud users. In this paper, we propose a new middleware architecture and methodology for providing virtual environment in Grids as a Service for the cloud users. This can be advantageous in Grid computing scenario also, where virtual environment provides coarse grained job execution thereby enhancing Quality of Service (QOS) compared to fine grained execution of jobs and also management of resources. Towards virtualization at the user end, the middleware acts as a bridge between the Grid resource layer and the users, thereby making the middleware light-weight. The Grid Control node chooses the resource node for service based on the resource information and thus the Grid Infrastructure as a service to the users.

e-Science는 새로운 연구개발 패러다임으로 핵심 과학영역에서 글로벌 협력과 이를 가능하 게 하는 차세대 인프라에 관한 것이다. 인프라는 특히 e-Science에서 중요한 비중을 차지하며, e-Science 의 일부분이다. 이러한 인프라는 일반적으로 Grid로 불리며, 데이터 처리 컴퓨팅을 공유한다. Grid 컴퓨 팅기술은 글로벌 규모의 자원(컴퓨팅 파워, 데이터 스토리지, 그리고 관련 정보)의 공유를 가능하게 하 고, 또한 e-Science 경제에 의해 지배되기도 한다. 그 맥락에서 e-Science가 무엇이고, 개발정책은 어떻게 진행하여야 하는가를 파악해 보았다. 구조적으로 혼란한 e-Science의 개념적 수렴문제와 더불어 개발정책 순서의 딜레마를 해결해 보고자 하였다. 사례연 구는 과학 및 산업정책에서 탁월한 이론과 실무를 제시해왔던 독일의 e-Science 개발정책을 대상으로 하였다. 그 이유는 BMBF(독일연방교육연구부)의 주도 하에 체계적인 개발정책을 실행하였다는 점에서 특징적이기 때문이다. 한편 독일 e-Science Initiative는 독일과학자와 BMBF에 의해 D-Grid로 불린다. 이에 대해 본 연구는 e-Science 개발정책을 alpha(인문, 예술, 언어 등), beta(수학, 화학, 물리, 천문, 의 약 등), gamma(사회/행동 과학, 경제와 법률 등)의 학문분야에 필요한 e-Infra와 그리드 컴퓨팅, 그리고 e-Science경제 등에 관하여 정부가 충분히 준비해 줄 수 없다는 전제 하에서 출발하고 있다. 그렇기 때 문에 e-Science개발정책의 모형은 점(e-Society), 선(X,Y,Z), 면(e-Science economy, e-Infra, Grid Computing), 공간(Basic, Usage, Application)의 나선형적 진화구조인 동시에 전략적 접근형으로 파악되었다. 따라서 e-Science 개발정책은 공간적 의미를 포괄하는 대상 분야를 먼저 정한 뒤, 그리드 컴퓨팅 프로젝 트, e-Infra, 그리고 자원을 포괄하는 선의 개발로 이어지는 순서가 나타났다. 다시 말하면 고객 유인형으 로서 그리드 컴퓨팅을 통한 e-Science economy를 기대하는 차원에서 기술 드라이버형인 그리드 컴퓨팅 을 위한 e-Infra개발이 요구된다고 하겠다. 이와 같은 고찰을 바탕으로 독일의 e-Science정책을 파악하고 그 내용을 분석해 보았다. 독일의 e- Science 정책은 2003년 6월 독일의 과학자 및 과학협회가 주도하여 D-Grid Initiative(독일 e-Science의 대표 격)전략백서를 내 놓으면서 시작되었다. 보고서는 과학연구를 위한 Grid 기술의 현황과 결과를 검 토하고, 장기적인 안목에서 전략적 Grid연구와 개발Initiative를 권고하고 있다. 2004년 3월 BMBF는 Germany e-Science Initiative에 대한 정책을 공고하였으며 연이어 그리드 컴퓨팅, 이-러닝, 그리고 지식 경영에 대한 제안신청서를 요청하였던 바, D-Grid 프로젝트는 2005년 1월에 시작되었다. 그 특성을 반영하여 분석기법을 정하였던 바, D-Grid를 그 특성에 따라 그룹을 구성한 뒤에 결과를 매트 릭스로 제시하고 이론적 모형에서 제시하였던 개발정책의 순서와의 일치성을 파악하였다. D-Grid 1은 과학자를 대상으로 컴퓨터 과학과 과학 컴퓨팅 커뮤니티의 얼리어돕터(early adopters)에 의 해 설계 계발된 IT서비스이다. 이러한 서비스 인프라는 주로 고에너지물리, 천체물리학, 대체 에너지, 의 약과 생명공학, 지구과학(일례 기후), 엔지니어링 사이언스, 과학도서관의 일명 커뮤니티그리드에 의해 검토 사용되고 있는 중이다. D-Grid 2는 과학자, 산업계, 비즈니스를 대상으로 하는 IT서비스이다. 주로 건설산업, 금융, 항공우주와 자동차, 기업정보와 자원계획시스템, 지리적 데이터, 일반적인 IT애플리케이 션을 서비스한다. 특징을 보자면, 독일 e-Science개발정책은 그리드 컴퓨팅과 지식경영을 전략적으로 추진하되, 과학과 산 업분야 간의 내외 경계를 뛰어넘어 추진되고 있다. 추진 초기에는 그리드 컴퓨팅정책에 집중하였고, 점 차적으로 지식경영으로 확대한다는 점이다. 다만, 이-러닝은 아직까지 부차적인 입장이다. 대체적으로 독일 e-Science이니셔티브는 전략적인 개발대상이기 때문에 정책우선순위가 뚜렷한 편이며, BMBF도 전략적 과업으로 동감하고 있다. 더불어서 과학과 산업 간의 협력기반 생성을 통해 국제경쟁력까지 시도 하는 특징 또한 나타난다. 시사점은 e-Science를 적용하기 이전에 공간적인 특성에 대한 이해가 필요하고, 더불어서 현재와 미래 간의 갈등구조를 지니고 있는 e-Science의 잠재력 파악을 우선한 뒤, 개발정책을 실시하는 것이 요구된 다는 점이다.

The e-Science is emerged on a new R&D paradigm and is concerned about global collaboration in key areas of science including the next generation of infrastructure that will enable it. In particular, the infrastructure is considered as an important part of e-science. It is generally referred to as the Grid that shares e-infrastructure to display data. Grid Computing Technology enables to share all resources(computing power, data storage and relevant information) on a global scale and is also governed by e-Science economy. In this paper, we investigate what is e-Science, and how to develop the policy associated with it consequently. We also focus on the situations that is brought about public consensus and a dilemma on developing policies for e-Science. Additionally, we attempt to identify the characteristics of German policies on e-Science that shows the excellent evidence of theories and practices in science and industry policies in German. That is the reason why we choose and analyze the cases of e-Science Initiatives in German. It provides us a lot lessons because German policies are based on the coordination and cooperation of widely distributed science and industrial parts, and are guided by BMBF systematically. By a research model in this study, it provides the clue of starting point developing a policy of e-Science. Since Government is not able to deal with all the system, such as e-Infra, Grid Computing, and e-Science economy which are necessary for alpha(humanities, arts, language etc), beta(mathematics, chemistry, physics, Astrology, Medicine etc), gamma(Society/behavior science, economy and law etc), the developing policies of e-Science needs to apply to a strategic approach after identifying the situations such as a dot(e-Society), a linear(X, Y, Z), an aspect(e-Science economy, e-Infra, Grid Computing), a space(Basic, Usage, Application). As the above view, e-Science policy needs to focus on the target area. After that, it is recommended to move to Grid Computing project, developing e-Infra step by step. Again, e-Science Policy moves to develop CDI-P type(Customer Driven Innovation Pull): e-Science economy through Grid Computing, T-P type(Tech Push): e-Infra for Grid Computing. Meanwhiles, German scientists and scientific organizations in 2003 jointly published a strategic paper which examined the status and consequences of grid technology on scientific research in Germany and recommended a long-term strategic grid research and development initiative. This resulted in the German D-Grid Initiative founded by the German Federal Ministry of Education and Research(BMBF) in March 2004, together with a call for proposals in the areas of Grid Computing, e-Learning, and Knowledge Management. The first D-Grid projects started on September 1, 2005. D-Grid 1: IT services for scientists, designed and developed by the ‘early adopters’ of the computer science and scientific computing communities. D-Grid 2: IT services for scientists, industry, and business. Accordingly, though Germany e-Science Initiatives strategically drive up Grid Computing and Knowledge Management, it has a point beyond the boundary between science and industry. It is characterized that the initial stage begins with Grid Computing policy, transfers to Knowledge Management thereafter. E-Learning is placed on the optional position. Normally, e-Science is considered as a strategic goal so the priority of it is relatively clear. BMBF also clear states “The German e-Science Initiative is a strategic task”. A another is to try up international competition through a peculiar collaboration-based formation between science and industry. In this paper, we try to analyse the German e-Science Initiatives in the context of the e-Science eco-system. German e-science projects are called “D-Grid" by German scientists and the BMBF. As a method, the on-going Grid computing projects in German are grouped in terms of the D-Grid characteristics and reported in a matrix form as a result. The implication of this study shows that it is very important to understand an inherent characteristics of e-science before adoption of it. It also tries to identify the potential impacts on e-science and provides clues how to deal with exposed uncertainty when a government wants to settle the public policy on e-science.

Today's IT technology is widely utilized in various fields through continuous development, it is also constantly improving the performance of computing resources along with it. Recently it was important to quickly process a large amount of data due to the popularization of the Internet and the development of high-speed network, whereby the high performance computing resources are required. Client is a client which assigns the task to return the result of operation after performing the operation back to the central server uses the server technique. This technique can be any functioned because it is concentrated on a central server, due to the overload of the increasingly deepened a bottleneck and as a result of the central server to the central server, with the increasing number of clients cause problems in scalability of the entire system. Moreover, if a failure occurs on a central server, there is a drawback that can not maintain a longer functional in the entire system. In this paper, we verify the information resources for the increment in grid computing-based operations, and has resources similar to the information of the work problems by checking the similar work resources when allocating exclude task assignments, assign a task to other resources may be proposed a monitoring system to check the progress of the tasks through a real-time monitoring the operation of the system resources.

정보기술과 전력 공급 시스템을 결합하여 전력 공급자와 소비자 간의 실시간 정보 교환을 통해 에너지 효율을 극대화하는 스마트 그리드 시스템이 등장했습니다. 최근 스마트 그리드 시스템에서 다양한 인증 기법이 제안되고 있다. 스마트 그리드를 위한 중앙 클라우드 서버와 IoT 기기 간의 전력 관련 정보를 수집 및 저장하고 데이터를 처리하는 블록체인 기술과 엣지 서 버의 아키텍처. 스마트 그리드 환경에서 보안을 강화하기 위해 인증 방식이 제안되고 있지만 여전히 많은 취약점이 보고되고 있다. 본 논문은 블록체인을 이용한 엣지 컴퓨팅 기반의 스마트 그리드에서 사용자의 프라이버시와 익명성을 보장하기 위한 새로운 상호 인증 기법을 제시한다. 제안된 방식에서는 키 자료 업데이트 및 폐기와 같은 키 관리의 효율성을 위해 스마트 계 약을 사용합니다. 마지막으로 제안하는 기법이 사용자의 스마트 그리드-IoT 기기와 에지 서버 간의 세션 키를 안전하게 설정 함과 동시에 익명성을 보장함을 증명한다.

The smart grid system has emerged to maximize energy efficiency through real-time information exchange between power providers and consumers by combining information technology and power supply systems. Recently, various authentication schemes in a smart grid system have been proposed. Blockchain technology and an edge server's architecture to collect and store electric power-related information and process data between a central cloud server and IoT devices for the smart grid. Although authentication schemes are being proposed to enhance security in the smart grid environment, many vulnerabilities are still reported. This paper presents a new mutual authentication scheme to guarantee users' privacy and anonymity in a smart grid based on edge computing using blockchain. In the proposed scheme, we use the smart contract for the key management's efficiency, such as updating and discarding key materials. Finally, we prove that the proposed scheme not only securely establishes a session key between the smart grid-IoT device of the user and the edge server but also guarantees anonymity.

With the increasing scale of grid systems, reliability evaluation for both grid systems and applications become more and more challenging, especially when taking the heterogeneity and dynamical workload into consideration. In this work, a workload-aware reliability evaluation model is proposed, in which queuing system is applied to describe the dynamic workload and working of grid resources. To supporting deadline-sensitive applications, a new class of resource fault, namely Deadline-Miss fault, is introduced to evaluate the reliability of these applications. The validity of the proposed model and its approach to calculate deadline-sensitive job’s reliability are presented theoretically. Extensive experiments are conducted to verify its performance, and the results show that the proposed model can significantly improve the accuracy of reliability evaluation in presence of dynamic workload. Also, scheduling algorithm based on this model can reduce mean response time and the deadline-miss rate for deadline-sensitive grid applications.

Scheduling jobs on computational grids is identified as NP-hard problem due to the heterogeneity of resources; the resources belong to different administrative domains and apply different management policies. Genetic algorithm which is a metaheuristic search on the basis of the idea of the natural evolution of living organisms generate solutions in order to reach the best solution, using techniques inspired by nature, such as the selection, crossover and mutation. One of the most important processes in the genetic algorithm is the crossover process that combines two chromosomes (parents) to produce a new chromosome (offspring). The parents with the highest fitness functions are selected to participate in the process. The idea behind crossover is that the new chromosome will be better than both parents because it takes the best qualities of both of them. This paper proposed a new job scheduling mechanism based on increasing the crossover rate in genetic algorithm in order to reach the best solution faster to improve the functionality of the genetic algorithm. To evaluate the proposed mechanism this study conducted a simulation using GridSim simulator and different workloads. The results of the simulation process revealed that the increase in the exploitation process decrease the finish time.

Grid computing is a promising technology for future computing platforms and is expected to provide easier access to remote computational resources that are usually locally limited. Scheduling is one of the active research topics in grid environments. The goal of grid task scheduling is to achieve high system throughput and to allocate various computing resources to applications. The Complexity of scheduling problem increases with the size of the grid and becomes highly difficult to solve effectively. Many different methods have been proposed to solve this problem. Some of these methods are based on heuristic techniques that provide an optimal or near optimal solution for large grids. In this paper, a new heuristic algorithm for scheduling meta-tasks in grid computing system is presented which tries to consider the execution time and machine state simultaneously by a mapping function. According to the experimental results, the proposed algorithm confidently demonstrates its competitiveness with previously proposed algorithms.

Grid computing is a form of distributed computing involves coordinating and sharing computing, application, data storage or network resources across dynamic and geographically dispersed organization. One of the scheduling techniques in Grid Computing is Tabu Search algorithm. A good scheduling algorithm is normally shows lower value of total tardiness and schedule time. The implementation Tabu Search algorithm was tested and evaluated on universal datasets using GridSim tool. The results indicate performance of tardiness is directly related to number of machines up to certain number of resources. Small and medium company can use grid in operation process because it saves cost and times.

In this paper, we research on the modern intelligent healthcare platform from the perspectives of grid based cloud computing and information management system assisted Internet of things technology. In the increasing pressure of modern society, people often attend the poor general health. The more deadly is that people lack the necessary technical means that our physical condition of the related data. Hospital information construction is to adapt to the information age and the modern medical mode, necessary measures of health economy and the hospital information system is the inevitable outcome of the application of information technology in hospital. Excellent hospital information system for standardized hospital management, and security of clinical activities smoothly to carry out the relevant scientific research activity has the important meaning. Intelligent medical treatment is the use of advanced network, communication, computer and digital technology, to implement the intelligent of the medical information collection, conversion, storage, transmission and processing, and various medical business process of digital operation, thus realizes the patients and medical staff, medical institutions, medical equipment, the interaction between, gradually achieve the medical information. Intelligent medical treatment can not only improve the hospital and medical staff's working efficiency, reduce mistakes in the work, also can through the remote medical treatment, remote consultation and so on ways to solve the problem of unequal distribution of regional health resources. Our research combines the grid and cloud computing technique to build up the novel intelligent healthcare platform with IOT technique which will enhance the current medical treatment mode and level up the overall performance of the modern information management system for the general usages.

In large-scale grid platforms, providing fault-tolerance for users is always a challenging task because of the uncertainty of network resources. In this paper, we present an intelligent agent based meta-scheduler, which is aiming at improving the fault-tolerance of grid systems when running user’s application. The proposed meta-scheduler is designed as an extendable framework, which allows plugging in multiple scheduling policies for deal with different scenarios. The agent-based scheduling framework enables grid systems to deploy their local schedulers in a flexible manner. Extensive experiments are conducted to investigate the performance of the proposed meta-scheduler, and the results show that it is effective to provide enhanced dependability for grid users, especially when the system is across multi-organization.

In large-scale grid systems, plenty of applications are constrained by soft or hard deadline requirement. However, it is difficult to guarantee the deadline requirements of these applications because of the dynamical nature of distributed systems. In this paper, a novel approach is proposed to evaluate the deadline-guarantee of co-allocation schemes that obtained from conventional co-allocation policies. Based on this approach, a hybrid-policy co-allocation model is also proposed to address the issue of deadline-constrained resource co-allocation in grid environments. The proposed model integrates multiple co-allocation policies to generate different co-allocation schemes, and selects the one with optimal deadline-guarantee for grid applications. By this way, the hybrid-policy model combines the merits of different policies, and overcomes the shortcomings of those policies. Extensive simulations are conducted to verify the effectiveness and the performance of the proposed model in terms of deadline-miss rate. Experimental results show that it can provide co-allocation scheme with enhanced deadline-guarantee as well as lower deadline-miss rate.

Grid Computing provides seamless and scalable access to wide-area distributed resources. Since, computational grid shares, selects and aggregates wide variety of geographically distributed computing resources and presents them as a single resource for solving large scale computing applications, there is a need for a scheduling algorithm which takes into account the various requirements of grid environment. Hence, this research proposes a new scheduling algorithm for computational grids that considers load balancing, fault tolerance and user satisfaction based on the grid architecture, resource heterogeneity, resource availability and job characteristics such as user deadline. This algorithm reduces the makespan of the schedule along with user satisfaction and balanced load. A simulation is conducted using Grid Simulator Toolkit (GridSim). The simulation results shows that the proposed algorithm has better makespan, hit rate and resource utilization.

Advance reservation is an effective technique to realize end-to-end quality of service (QoS) guarantees for grid applications. However, excessive advance reservation will result in low resource utilization and high rejection rate on receiving new requests, which in turn reduces user’s QoS satisfaction. In this paper, a slack reservation strategy is proposed, with aiming to mitigate the negative effects brought about by conventional reservation service. The strategy is based on the observation that applications tend to overestimate reservation deadline to ensure their completion. It allows accepting new reservation requests that overlapping with existing ones under certain conditions. Both the benefits and risks of the proposed policy are presented theoretically. Experimental results show that the strategy can bring about remarkably higher resource utilization and lower rejection rate when the system is in presence of heavy reservation workload.

Grid computing allows, controlled and coordinated resource sharing and problem solving in dynamic, multi- institutional virtual organizations. Grids are categorized in to two, as Computational grid and Data grid. In computing grid, allocating resources to the tasks based on its requirements and task scheduling is an important issue. Generally, the task scheduler assigns tasks to a suitable resource. The nodes with better performance would be assigned first for execution. When the load on a grid was high, and all resource with a better performance is assigned, other tasks have to be assigned to the resource with unsuitable performance. To improve the performance and throughput of the system, it is required to assign the tasks to a suitable and lower fault rate processor. Therefore, if a task is allocated to a resource without considering the performance factor, resource fault rate, the overall execution time will increases and throughput will decreases obviously. To solve this problem, a task scheduling algorithm that finds suitable and low fault rate resource for task execution is introduced in this paper. Experiments made on GridSim, shows that the proposed algorithm works well in dynamic environments.

Grid Computing has enabled us to utilize the unused computing power (CPU cycles) of computers connected to networks (e.g. Internet). Nowadays, there are lots of scientific projects going on in the domain of High Energy Physics (HEP) and Grid infrastructure constitutes the core computing facility of these projects. One such project is LHC (Large Hadron Collider) deployed at CERN. These experiments produce and manage a large amount of data per day and run thousands of computing jobs to process that data. The applications for these experiments require large data transfers over the network from data sources to computing resources. It is the duty of meta-scheduler to allocate jobs to most appropriate resources, and to use network in an efficient way. In this work, a Network and Data Location Aware job scheduling has been proposed for data intensive jobs. The proposed scheduling algorithm takes into account network characteristics, disk read speed of data sources, and data locations of input files, as well as other computational factors (CPU power, memory, CPU load, e.t.c) when making scheduling decisions. This scheduling algorithm aims to minimize not only file staging (data transfer) time but also turnaround time of the jobs. There are extensions to GridWay that consider the network state when making scheduling decisions, but they have not used network information in an efficient way, and have not considered data locations of input files either. Thus, they are not efficient for data intensive jobs which require huge data movement over the network. The authors have improved the GridWay MetaScheduler with Network and Data Location Aware scheduling algorithm. The improved GridWay MetaScheduler has been tested for data intensive jobs. Results presented here shows that the data transfer time and turnaround time of jobs are reduced when network characteristics, data locations of input files, and disk read speed of storage drive at data sources are considered in the jobs scheduling process.

A Computational grid is highly useful computing infrastructure that enables effective access of resources to perform high performance computing wherein job management, efficient resource utilization are key grid service issues. To make grid environment greatly useful, an optimized scheduling system is essential as computational grid is often heterogeneous with complex environment. This complexity raises difficulties in gathering the load information of computational resources as well as coping up with user needs specifically when users dealt with jobs. Other existing fact of grid computing is that it is best known for handling batch job rather than interactive job submission. Interactive Job submission is useful in situations when a job requires human intervention to control the job result. This research paper tries to satisfy the requirements of the user by developing user driven scheduler. We have developed a scheduler here after referred as NGSched compatible with Globus and hence provides user on-demand and interactive access to grid resources. It adopts novel approach for scheduling multiple jobs by giving privileges of resource selection to the user. The proposed and implemented algorithm also satisfies the deadlines given by the user for job execution on the selected resources and balances the load on grid nodes. The proposed heuristic also calculates the share of each resource according to capability to handle load. Since this scheduler is user centric, it facilitates the user to monitor the job, cancel the job, schedule on selected resources, suspend and resume the job. Results obtained proved that our innovative algorithm is better and efficient.

Cloud computing is a new paradigm in which dynamically scalable virtualized computing resources are provided as a service over the Internet. However, since resources are limited, it is very important that cloud providers efficiently provide their resources. This paper presents a trust model for efficient reconfiguration and allocation of computing resources satisfying various user requests. Our model collects and analyzes reliability based on historical information of servers in a Cloud data center. Then it prepares the best available resources for each service request in advance, providing the best resources to users. We also carried out experiments for reliability analysis with 4 data types, including an all data set, random data set, recent data set, and the data set within a standard deviation. As a result, using our trust model, cloud providers can utilize their resources efficiently and also provide highly trusted resources and services to many users.

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