Dot Net Platform for Distributed Evolutionary Algorithms with Application in Hydroinformatics

Real-world problems often contain nonlinearities, relationships, and uncertainties that are too complex to be modeled analytically. In these scenarios, simulation-based optimization is a powerful tool to determine optimal system parameters. Evolutionary Algorithms (EAs) are robust and powerful techniques for optimization of complex systems that perfectly fit into this concept. Since evolutionary algorithms require a large number of time expensive evaluations of candidate solutions, the whole process of optimization can take huge CPU time. In this chapter, .NET platform for distributed evaluation using WCF (Windows Communication Foundation) Web services is presented in order to reduce computational time. This concept provides parallelization of evolutionary algorithms independently of geographic location and platform where evaluation is performed. Hydroinformatics is a typical representative of fields where complex systems with many uncertainties are studied. Application of the developed platform in hydroinformatics is also presented in this chapter.

Ontology-Based Multimodal Language Learning

L2 language learning is an activity that is becoming increasingly ubiquitous and learner-centric in order to support lifelong learning. Applications for learning are constrained by multiple technical and educational requirements and should support multiple platforms and multiple approaches to learning. This chapter investigates the possibility of applying ontology-based, dynamically generated learning objects implemented on a cloud computing infrastructure in order to satisfy these requirements. Previous work on using mobile learning objects is used as a starting point in an attempt to design a system that will preserve all of the advantages of utilizing learning objects, while eliminating any flaws and maximizing compatibility with existing systems. A model of a highly modular, flexible, multiplatform language learning system is presented along with some implementation remarks and advices for future implementation.

Student Relationship Management Using Social Clouds

A new vision of higher education systems, in which the student is the central subject of the teaching process, opens up new learning opportunities that include customization of teaching methods to the students’ needs, and new modes of communication both between teachers and students and among students themselves. The main subject of this chapter is the implementation and improvement of the Student Relationship Management (SRM) concept as a cloud service in an e-education system by using social media. The experimental part of the chapter presents the design and implementation of an e-education model based on cloud computing. The proposed model is implemented at the Faculty of Organizational Sciences, University of Belgrade, by using the existing cloud computing infrastructure of the Laboratory for E-Business.

Grids, Clouds, and Massive Simulations

In this chapter, the authors discuss issues surrounding High Performance Computing (HPC)-driven science on the example of Peta science Monte Carlo experiments conducted at the Brookhaven National Laboratory (BNL), one of the US Department of Energy (DOE) High Energy and Nuclear Physics (HENP) research sites. BNL, hosting the only remaining US-based HENP experiments and apparatus, seem appropriate to study the nature of the High-Throughput Computing (HTC) hungry experiments and short historical development of the HPC technology used in such experiments. The development of parallel processors, multiprocessor systems, custom clusters, supercomputers, networked super systems, and hierarchical parallelisms are presented in an evolutionary manner. Coarse grained, rigid Grid system parallelism is contrasted by cloud computing, which is classified within this chapter as flexible and fine grained soft system parallelism. In the process of evaluating various high performance computing options, a clear distinction between high availability-bound enterprise and high scalability-bound scientific computing is made. This distinction is used to further differentiate cloud from the pre-cloud computing technologies and fit cloud computing better into the scientific HPC.

Application of Cloud-Based Simulation in Scientific Research

This chapter is a review of the literature related to the use of cloud-based computer simulations in scientific research. The authors examine the types and good examples of cloud-based computer simulations, offering suggestions for the architecture, frameworks, and runtime infrastructures that support running simulations in cloud environment. Cloud computing has become the standard for providing hardware and software infrastructure. Using the possibilities offered by cloud computing platforms, researchers can efficiently use the already existing IT resources in solving computationally intensive scientific problems. Further on, the authors emphasize the possibilities of using the existing and already known simulation models and tools in the cloud computing environment. The cloud environment provides possibilities to execute all kinds of simulation experiments as in traditional environments. This way, models are accessible to a wider range of researchers and the analysis of data resulting from simulation experiments is significantly improved.

High Performance and Grid Computing Developments and Applications in Condensed Matter Physics

This chapter introduces applications of High Performance Computing (HPC), Grid computing, and development of electronic infrastructures in Serbia, in the South Eastern Europe region, and in Europe as a whole. Grid computing represents one of the key enablers of scientific progress in many areas of research. Main HPC and Grid infrastructures, initiatives, projects and programs in Europe, Partnership for Advanced Computing in Europe (PRACE) and European Grid Initiative (EGI) associations, as well as Academic and Educational Grid Initiative of Serbia (AEGIS) are presented. Further, the chapter describes some of the applications related to the condensed matter physics, developed at the Scientific Computing Laboratory of the Institute of Physics, University of Belgrade.

Mobile Learning Services on Cloud

This chapter discusses providing mobile learning services on cloud. Mobile cloud computing brings numerous benefits and enables overcoming technical constraints of mobile learning. The main techniques and approaches in mobile cloud computing are analyzed. A model for mobile learning services delivering through cloud computing is proposed. Several examples of mobile learning services implementations on cloud are presented: Android native application that provides Moodle learning management system features and a SMS service and mobile application for managing the infrastructure of e-learning system.

From Software Specification to Cloud Model

Applications are often multi-tier and require application servers, workflow engines, and database management systems. Cloud computing is a computing paradigm wherein the resources such as processors, storage, and software applications are provided as services via the Internet. Moving an enterprise application to the cloud can be a challenge. This application needs to be split into the components that then automatically deploy on the cloud. In this chapter, the authors introduce a way to automatically derivate the main architecture components from the software requirements that can serve as a basis for an architecture diagram in the MOCCA method. The proposed approach is model and use case driven.

From Mainframe to Cloud

The adoption of cloud computing accelerated significantly over the past few years, and this trend will remain. As cloud-computing technologies and vendors mature, more educational institutions will adopt the Internet-based computing style. Organizations will use cloud computing to reduce the cost of e-mail, IT infrastructure, data centers and storage, and business applications. Cloud computing is a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction. This cloud model promotes availability and is composed of five essential characteristics, three service models, and four deployment models. The absence of a clear definition of cloud computing is slowing the adoption of cloud computing by needlessly increasing user apprehension and obscuring the cloud's benefits. Organizations need to understand cloud computing before they can realize its benefits and avoid its risks. This chapter clears up confusion about the cloud by defining cloud computing and its characteristics, architectural model, benefits, and shortcomings. This chapter provides the definition of the concept of cloud computing and cloud computing as a service. Subsequently, it explores the characteristics of different types of clouds, as well as the security aspect of this technology. Major trends of cloud computing, such as social computing, context-aware computing, and pattern based strategy, are described. In a conclusion, the authors provide an overview of future use of cloud computing.

Security Issues of Cloud Computing and an Encryption Approach

The main security and privacy issues of cloud computing as well as the related implications are addressed, and a general framework for achieving the goals is summarized. This chapter basically considers scientific and educational employment of a cloud as a particular instance of a public cloud and its security, and as a potentially specific issue, a request for a heavy minimization of the costs implied by security is pointed out. Consequently, the problem of minimization of the overheads implied by security/privacy mechanisms is addressed. The main security requirements are given as well as the main recommendations, providing a framework for the security management. As a particular issue, data protection is considered and significance of data access control and encryption are discussed. Accordingly, an illustrative approach for achieving lightweight and provable secure encryption is shown. The considered encryption is based on joint employment of cryptographic and coding methods.