A Grid-Enabled Infrastructure for Resource Sharing, E-Learning, Searching and Distributed Repository Among Universities

In the recent years, service-based approaches for sharing of data among repositories and online learning are rising to prominence because of their potential to meet the requirements in the area of high performance computing. Developing education based grid services and assuring high availability reliability and scalability are demanding in web service architectures. On the other hand, grid computing provides flexibility towards aggregating distributed CPU, memory, storage, data and supports large number of distributed resource sharing to provide the full potential for education like applications to share the knowledge that can be attainable on any single system. However, the literature shows that the potential of grid resources for educational purposes is not being utilized yet. In this paper, an education based grid framework architecture that provides promising platform to support sharing of geographically dispersed learning content among universities is developed. It allows students, faculty and researchers to share and gain knowledge in their area of interest by using e-learning, searching and distributed repository services among universities from anywhere, anytime. Globus toolkit 5.2.5 (GTK) software is used as grid middleware that provides resource access, discovery and management, data movement, security, and so forth. Furthermore, this work uses the OGSA-DAI that provides database access and operations. The resulting infrastructure enables users to discover education services and interact with them using the grid portal

Threshold Based Auto Scaling of Virtual Machines in Cloud Environment

Part 3: Virtualization and Cloud Computing TechnologiesInternational audienceCost effectiveness is one of the reasons behind the popularity of Cloud. By effective resource utilization cost can be further reduced and resource wastage can be minimized. The application requirement may vary over time depending on many factors (for instance load on the application); user may run different types of application (a simple MS word to complex HPC application) in a VM. In such cases if the VM instance capacity is fixed there is a high possibility of mismatch between the VM capacity and application resource requirement. If the VM capacity is more than the application resource requirement then resource will be wasted; if the VM capacity is less than the application resource requirement then the application performance will degrade. To address these issues we are proposing threshold based auto scaling of virtual machines in which VMs will be dynamically scaled based on the application resource utilization (CPU and Memory). Using our approach effective resource utilization can be achieved

Application of computational intelligence techniques for internet of things: an extensive survey

The application of computational intelligence (CI) techniques to internet of things (IoT) is gaining popularity due to its capability of providing human-like knowledge, such as cognition, recognition, understanding, learning, and others. This paper attempts to provide an exhaustive survey of the available literature on IoT using CI techniques. In addition, detailed categorisation has been provided on the basis of different CI tools and their hybridisations used to tackle different problems of IoT. The potential benefits and utility of CI techniques in IoT are highlighted. The possible mapping of CI techniques to the real-world IoT problems is presented. The advantages and disadvantages of CI algorithms over traditional IoT solutions are discussed. A general evaluation of CI algorithms is presented, which will serve as a guide for using CI algorithms for IoT. Finally, some considerations regarding the recent trends and potential