A Taxonomy of Workflow Management Systems for Grid Computing

With the advent of Grid and application technologies, scientists and engineers are building more and more complex applications to manage and process large data sets, and execute scientific experiments on distributed resources. Such application scenarios require means for composing and executing complex workflows. Therefore, many efforts have been made towards the development of workflow management systems for Grid computing. In this paper, we propose a taxonomy that characterizes and classifies various approaches for building and executing workflows on Grids. We also survey several representative Grid workflow systems developed by various projects world-wide to demonstrate the comprehensiveness of the taxonomy. The taxonomy not only highlights the design and engineering similarities and differences of state-of-the-art in Grid workflow systems, but also identifies the areas that need further research.Comment: 29 pages, 15 figure

Modern approaches to modeling user requirements on resource and task allocation in hierarchical computational grids

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An Enhanced Model for Job Sequencing and Dispatch in Identical Parallel Machines

This paper has developed an efficient scheduling model that is robust and minimizes the total completion time for job completion in identical parallel machines. The new model employs Genetic-Fuzzy technique for job sequencing and dispatch in identical parallel machines. It uses genetic algorithm technique to develop a job scheduler that does the job sequencing and optimization while fuzzy logic technique was used to develop a job dispatcher that dispatches job to the identical parallel machines. The methodology used for the design is the Object Oriented Analysis and Design Methodology (OOADM) and the system was implemented using C# and .NET framework. The model was tested with fifteen identical parallel machines used for printing. The parameters used in analyzing this model include the job scheduling length, average execution time, load balancing and machines utilization. The result generated from the developed model was compare with the result of other job scheduling models like First Come First Sever (FCFS) scheduling approach and Genetic Model (GA) scheduling approach. The result of the new model shows a better load balancing and high machine utilization among the individual machines when compared with the First Come First Serve (FCFS) scheduling model and Genetic Algorithm (GA) scheduling model. Keywords:  Parallel Machines, Genetic Model, Job Scheduler, Fuzzy Logic Technique, Load Balancing, Machines   Utilization DOI: 10.7176/CEIS/11-2-05 Publication date: March 31st 202

Dht-based security infrastructure for trusted internet and grid computing

Abstract: We designed a distributed security infrastructure with self-defence capabilities to secure networked resources in Grids and internet applications. This paper reports new developments in fuzzy trust management, game-theoretic Grid models, security-binding methodology, as well as new Grid performance metrics, defence architecture and mechanisms against intrusions, worms, and low-rate pulsing Distributed Denial of Service (DDoS) attacks. The design is based on a novel Distributed Has

Towards the Exploration of Task and Workflow Scheduling Methods and Mechanisms in Cloud Computing Environment

Cloud computing sets a domain and application-specific distributed environment to distribute the services and resources among users. There are numerous heterogeneous VMs available in the environment to handle user requests. The user requests are defined with a specific deadline. The scheduling methods are defined to set up the order of request execution in the cloud environment. The scheduling methods in a cloud environment are divided into two main categories called Task and Workflow Scheduling. This paper, is a study of work performed on task and workflow scheduling. Various feature processing, constraints-restricted, and priority-driven methods are discussed in this research. The paper also discussed various optimization methods to improve scheduling performance and reliability in the cloud environment. Various constraints and performance parameters are discussed in this research

Sea Container Terminals

Due to a rapid growth in world trade and a huge increase in containerized goods, sea container terminals play a vital role in globe-spanning supply chains. Container terminals should be able to handle large ships, with large call sizes within the shortest time possible, and at competitive rates. In response, terminal operators, shipping liners, and port authorities are investing in new technologies to improve container handling infrastructure and operational efficiency. Container terminals face challenging research problems which have received much attention from the academic community. The focus of this paper is to highlight the recent developments in the container terminals, which can be categorized into three areas: (1) innovative container terminal technologies, (2) new OR directions and models for existing research areas, and (3) emerging areas in container terminal research. By choosing this focus, we complement existing reviews on container terminal operations

Analysis of power consumption in heterogeneous virtual machine environments

Reduction of energy consumption in Cloud computing datacenters today is a hot a research topic, as these consume large amounts of energy. Furthermore, most of the energy is used inefficiently because of the improper usage of computational resources such as CPU, storage and network. A good balance between the computing resources and performed workload is mandatory. In the context of data-intensive applications, a significant portion of energy is consumed just to keep alive virtual machines or to move data around without performing useful computation. Moreover, heterogeneity of resources increases the difficulty degree, when trying to achieve energy efficiency. Power consumption optimization requires identification of those inefficiencies in the underlying system and applications. Based on the relation between server load and energy consumption, we study the efficiency of data-intensive applications, and the penalties, in terms of power consumption, that are introduced by different degrees of heterogeneity of the virtual machines characteristics in a cluster