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.

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.

Due to the complexity of modern manufacturing and mechanical equipments, it is difficult for equipment users or maintainers to accomplish fault diagnosis independently. In this paper, the development history of fault diagnosis technology is surveyed and investigated, especially remote fault diagnosis system based on Internet in detail. Then, a remote fault diagnosis system based on grid computing technology is proposed to enable collaborative resource sharing and problem solving among multiple equipment suppliers and equipment users by integrating all kinds of diagnostic resources. The architecture of this fault diagnosis system is presented, as well as the Client-Master-Worker computation model and the diagnostic workflow. Finally, a prototype system is implemented using the data-driven middleware-BitDew, in which multiple fault diagnosis grid services are integrated in a unified Web portal. The case study of data-driven fault data analysis is conducted in our prototype which has proved the effectiveness of the system.

Resources in grid systems are heterogeneous, geographically distributed, belong to different administrative domains and apply different management policies. The roles of resource selection mechanisms are to identify, select and allocate the most suitable resources for a given set of tasks. This paper presents a taxonomy that facilitates identifying and classifying the mechanisms used in the implementation of grid resource selection process, as well as describing the most significant features of grid resource selection mechanisms. The benefit of this taxonomy is to highlight the main aspects of the selection mechanisms, which can help researchers and developers of grid resource management systems.

The optimization of scheduling problems is based on different criteria to optimize. One of the most important criteria is the minimization of completion time of the last task on the last machine called makespan. In this paper, we present a parallel algorithm for solving the permutation flow shop problem. Our algorithm is a basic parallel distributed algorithm deployed in a grid of computer (Grid’5000). The objective of this work is minimizing the total makespan of the tasks. Our algorithm uses the exact Branch and Bound method to find optimal solutions of the problem through the distribution of the tasks among the available processors. Computational results of our parallel algorithm using well known Taillard’s benchmarks, showed encouraging results. In particular, we succeeded to solve two new instances to optimality which had never been resolved before neither in sequential nor in parallel [29].

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

Scheduling jobs on computational grids is identified as NP-complete problem due to the heterogeneity of resources; the resources belong to different administrative domains and apply different management policies. This paper conducted an extensive and wide literature review to study the state of the art of grid scheduling algorithms. This review starts with an overview of the grid technologies and a description of the grid resource management systems. The evolution of the grid scheduling mechanisms is illustrated in this paper started from basic scheduling mechanisms such as Min-Min and Max-Min approaches ending with the swarm intelligence optimization methods. The swarm intelligence and evolutionary mechanisms are also presented and critically analyzed.

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.

Computational Grid is a high performance computing environment that participating machines resources are used through software layer as transparent and reliable. Task assignment problem in Grid Computing is a NP-Complete problem that has been studied by several researchers. The most common objective functions of task scheduling problems are Makespan and Flowtime. This paper gives a classification of meta-heuristic scheduling algorithms in distributed computing that are applicable to grid environment and addresses scheduling problem of independent tasks on Computational Grids. A Hybrid Discrete Particle Swarm Optimization and Min-min algorithm (HDPSO) is presented to reduce overall Completion Time of task.

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 propose an energy-efficient scheduling scheme for real-time periodic tasks on a heterogeneous Grid computing system. The Grid system consists of heterogeneous processors providing the DVFS mechanism with a finite set of discrete clock frequencies. In order to save energy consumption, the proposed scheduling scheme assigns each real-time task to a processor with the least energy increment. Also the scheme activates a part of all available processors with unused processors powered off. Evaluation shows that the proposed scheme saves up to 70% energy consumption of the previous method.

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.

본 논문은 경제급전 최적화 문제에 균형-교환 방법을 제안하였다. 제안된 알고리즘은 모든 발전기를 가능한 한 밸브지점으로 운영한다고 가정한다. 초기치로 최대 발전량 로 설정하고, 각 발전기의 밸브지점 까지 발전량을 감소시켰을 때의 평균 발전단가 가 최대가 되는 발전기 의 발전량을 밸브지점 발전단가 로 감소시켰으며, 이면 의 발전기 발전량을 로 감소시켜 의 균형을 맞추었다. 다음으로, 의 범위에 대해 ”-10“ 간격으로 감소시키는 성인걸음법으로, 범위에 대해서는 ”-1“의 아기걸음법으로, 에 대한 이면 , 로 발전량을 교환하는 방법으로 최적화를 수행하였다. 다음으로 에 대해 미세한 교환을 수행하였다. 동적 경제급전 문제의 시험사례에 제안된 알고리즘을 적용한 결과 기존의 휴리스틱 알고리즘 최적화 발전비용을 크게 감소시켜 경제적인 이익을 극대화 시켰다.

This paper proposes a balance-swap method for the dynamic economic load dispatch problem. Based on the premise that all generators shall be operated at valve-points, the proposed algorithm initially sets the maximum generation power at . As for generator with , which is the maximum operating cost produced when the generation power of each generator is reduced to the valve-point , the algorithm reduces ’s generation power down to , the valve-point operating cost. When , it reduces the generation power of a generator with of to so as to restore the equilibrium . The algorithm subsequently optimizes by employing an adult-step method in which power in the range of is reduced by 10; a baby step method in which power in the range of is reduced by 1; and a swap method for of , in which power is swapped to , . It finally executes minute swap process for . When applied to various experimental cases of the dynamic economic load dispatch problems, the proposed algorithm has proved to maximize economic benefits by significantly reducing the optimal operating cost of the extant Heuristic algorithm.