The domain of plant genotyping and phenotyping presents a number of challenges in the area of large data computation Various tools and systems have been developed to automate the scientific workflows and support the computational needs of this domain In this paper, we review a number of the popular systems (ie, Galaxy, iPlant, GenAp and LemnaTec) in the domain of plant genotyping and phenotyping using the scenario-based architectural analysis method (SAAM) In particular, we focus on how different stakeholders are using these systems in a variety of scenarios and to what extent the systems support their needs Our SAAM analysis shows that the existing systems have shortcomings For example, they are limited in their support for high throughput processing of large amounts of heterogeneous types of data Based on our findings we propose a reference architecture along with a preliminary evaluation in the subject domain The reference architecture and its evaluation is aimed at helping developers/architects create suitable architectural designs and select appropriate technologies when developing plant phenotyping and genotyping systems

Towards a Reference Architecture for Cloud-Based Plant Genotyping and Phenotyping Analysis Frameworks

With the rapid advancement of computing technologies, various scientific research communities have been extensively using cloud-based software tools or applications. Cloud-based applications allow users to access software applications from web browsers while relieving them from the installation of any software applications in their desktop environment. For example, Galaxy, GenAP, and iPlant Colaborative are popular cloud-based systems for scientific workflow analysis in the domain of plant Genotyping and Phenotyping. These systems are being used for conducting research, devising new techniques, and sharing the computer assisted analysis results among collaborators. Researchers need to integrate their new workflows/pipelines, tools or techniques with the base system over time. Moreover, large scale data need to be processed within the time-line for more effective analysis. Recently, Big Data technologies are emerging for facilitating large scale data processing with commodity hardware. Among the above-mentioned systems, GenAp is utilizing the Big Data technologies for specific cases only. The structure of such a cloud-based system is highly variable and complex in nature. Software architects and developers need to consider totally different properties and challenges during the development and maintenance phases compared to the traditional business/service oriented systems. Recent studies report that software engineers and data engineers confront challenges to develop analytic tools for supporting large scale and heterogeneous data analysis. Unfortunately, less focus has been given by the software researchers to devise a well-defined methodology and frameworks for flexible design of a cloud system for the Genotyping and Phenotyping domain. To that end, more effective design methodologies and frameworks are an urgent need for cloud based Genotyping and Phenotyping analysis system development that also supports large scale data processing. In our thesis, we conduct a few studies in order to devise a stable reference architecture and modularity model for the software developers and data engineers in the domain of Genotyping and Phenotyping. In the first study, we analyze the architectural changes of existing candidate systems to find out the stability issues. Then, we extract architectural patterns of the candidate systems and propose a conceptual reference architectural model. Finally, we present a case study on the modularity of computation-intensive tasks as an extension of the data-centric development. We show that the data-centric modularity model is at the core of the flexible development of a Genotyping and Phenotyping analysis system. Our proposed model and case study with thousands of images provide a useful knowledge-base for software researchers, developers, and data engineers for cloud based Genotyping and Phenotyping analysis system development.

Towards a Reference Architecture with Modular Design for Large-scale Genotyping and Phenotyping Data Analysis: A Case Study with Image Data

CCNA: Cisco Certified Network Associate Study Guide, 7th Edition (ISBN: 9780470901076)--The Cisco Certified Network Associate (CCNA) certification is the first-tier certification and by far Cisco's most popular. This 7th edition of the best-selling CCNA Study Guide provides in-depth coverage of every exam objectives, plus updates that reflect technology developments over the past years. It addition, it offers expanded coverage on key topics reflected in the new version of the exam. Finally, the enhanced CD contains over an hour of useful video and audio files, as well as the Sybex Test Engine and electronicflashcards.CCNA Virtual Lab, Titanium Edition 3.0 (ISBN: 9781118431993)--Hands-on practice with routers and switches is critical for success on the CCNA exam, yet the costs associated with the required hardware are prohibitive. Using the latest drag-and-drop technology, the CCNA Virtual Lab, Titanium Edition 3.0 allows users to create a simulated lab using an unlimited number of routers and switches. Users can then either follow the lab exercises provided or experiment with the hundreds of configuration commands built into the simulator. An extensive Help menu is available to guide users through complex tasks.CCNA: Cisco Certified NetworkAssociateReview Guide (ISBN: 9781118063460)--Organized by exam objectives, this is a focused, concise book that works hand-in-hand with any learning tool, including the Sybex CCNA: Cisco Certified Network Associate Study Guide. The book will consist of eight high-level chapters, each mapping to the four main Domains of the exam skill-set. The book will drill down into the specifics of the exam.

CCNA Cisco Certified Network Associate Certification Kit, 8th Edition (640-802)

For the past few years, technology has been steadily rising and leading to rapid change in the development process. Information & Communication Technology (ICT) approaches have been moving toward new development trends with a huge elevation of comprehension. Service providers are always seeking customer satisfaction and due to multiple essentials, clients prefer to migrate from one application to another. Newer software applications are being launched, but they are constricted within a defined scope and cannot perform functions of interoperable components. Therefore,the aim of this study is to facilitate clients with multiple services over a single platform and gain knowledge about cloud computing, service computing techniques to enable flexibility and interoperability between applications. This work proposes an STA-Vision19 cloud platform and Service Integration Bridge (SIB) as solutions. To validate methods,abasic prototype for the proposedframework STA-Vision19 andtwo applications E-Learning and Blogging System were implemented. In results, software evaluation parameters, and testing results are discussed, which indicates that the proposed system can collaborate two or more applications and it is beneficial to use as compared to an isolated system.

/pdf/sta-vision19-enabling-interoperability-between-distinct-3omklxjf.pdf

Sta-vision19 enabling interoperability between distinct systems

Developing a government enterprise architecture framework to support the requirements of big and open linked data with the use of cloud computing

Cloud computing and big data are two core services in many organizations. Combining a big data platform, such as Hadoop, into the cloud architecture using virtualization technique will result in losing the performance benefit of MapReduce. Unique for the existing virtualized big data cloud, this work introduces an innovative cloud architecture called the heterogeneous cloud. In the heterogeneous cloud, the big data service is directly deployed onto the bare-metal machines without the help of virtualization. In order to implement such a heterogeneous architecture, we propose a primer architecture design with detailed solutions of potential technical issues and conflicts. With the maximal isolation of computing, network, and storage, the proposed heterogeneous cloud can reach the multifunction of big data and cloud computing with improved performance and energy efficiency.

A Design of Heterogeneous Cloud Infrastructure for Big Data and Cloud Computing Services

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A Design of Heterogeneous Cloud Infrastructure for Big Data and Cloud Computing Services