http://www.ronpub.com/publications/search.php?journal=ALL&author=K.+Chandrasekaran&exactauthor=on&title=&abstract=&volume=&issue=&year1=&year2=&searchtype=advanced This feed contains the result of an user-defined search in RonPub publications en-us https://www.ronpub.com/ojbd/OJBD_2015v1i1n02_Teli.html http://nbn-resolving.de/urn:nbn:de:101:1-201705194335 With the emergence of cloud computing, Big Data has caught the attention of many researchers in the area of cloud computing. As the Volume, Velocity and Variety (3 Vs) of big data are growing exponentially, dealing with them is a big challenge, especially in the cloud environment. Looking at the current trend of the IT sector, cloud computing is mainly used by the service providers to host their applications. A lot of research has been done to improve the network utilization of WAN (Wide Area Network) and it has achieved considerable success over the traditional LAN (Local Area Network) techniques. While dealing with this issue, the major questions of data movement such as from where to where this big data will be moved and also how the data will be moved, have been overlooked. As various applications generating the big data are hosted in geographically distributed data centers, they individually collect large volume of data in the form of application data as well as the logs. This paper mainly focuses on the challenge of moving big data from one data center to other. We provide an efficient algorithm for the optimization of cost in the movement of the big data from one data center to another for offline environment. This approach uses the graph model for data centers in the cloud and results show that the adopted mechanism provides a better solution to minimize the cost for data movement. https://www.ronpub.com/ojbd/OJBD_2015v1i1n03_YR.html http://nbn-resolving.de/urn:nbn:de:101:1-201705194340 In today's world of exponentially growing technology, spam is a very common issue faced by users on the internet. Spam not only hinders the performance of a network, but it also wastes space and time, and causes general irritation and presents a multitude of dangers - of viruses, malware, spyware and consequent system failure, identity theft, and other cyber criminal activity. In this context, cognition provides us with a method to help improve the performance of the distributed system. It enables the system to learn what it is supposed to do for different input types as different classifications are made over time and this learning helps it increase its accuracy as time passes. Each system on its own can only do so much learning, because of the limited sample set of inputs that it gets to process. However, in a network, we can make sure that every system knows the different kinds of inputs available and learns what it is supposed to do with a better success rate. Thus, distribution and combination of this cognition across different components of the network leads to an overall improvement in the performance of the system. In this paper, we describe a method to make machines cognitively label spam using Machine Learning and the Naive Bayesian approach. We also present two possible methods of implementation - using a MapReduce Framework (hadoop), and also using messages coupled with a multicast-send based network - with their own subtypes, and the pros and cons of each. We finally present a comparative analysis of the two main methods and provide a basic idea about the usefulness of the two in various different scenarios. https://www.ronpub.com/ojcc/OJCC_2015v2i2n01e_Groppe.html http://nbn-resolving.de/urn:nbn:de:101:1-201704229173 Cloud computing provides on-demand access to a shared pool of configurable and dynamically reallocated computing resources typically located in third-party data centers. Ubiquitous computing aims at providing computing resources anytime and everywhere by using any device, in any location, and in any format. This special issue, Advances in Cloud and Ubiquitous Computing (ACUC), aims at addressing the challenges and reporting the latest research findings in the fields of Cloud computing and Ubiquitous Computing respectively, and how new technologies of Cloud Computing and Ubiquitous Computing complete each other. https://www.ronpub.com/ojcc/OJCC_2015v2i2n03_Thomas.html http://nbn-resolving.de/urn:nbn:de:101:1-201705194523 Cloud Federation is an emerging technology where Cloud Service Providers (CSPs) offering specialized services to customers collaborate in order to reap the real benefits of Cloud Computing. When a CSP in the Cloud Federation runs out of resources, it can get the required resources from other partners in the federation. Normally, there will be QoS agreements between the partners in the federation for the resource sharing. In this paper, we propose a trust based mechanism for the management of dynamic QoS violations, when one CSP requests resources from another CSP in the federation. In this work, we have implemented the partner selection process, when one CSP does not have enough resources, using the Analytic Hierarchy Process (AHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) methods, and also considering the trust values of various CSPs in the federation. We have also implemented the Single Sign-On (SSO) authentication in the cloud federation using the Fully Hashed Menezes-Qu-Vanstone (FHMQV) protocol and AES-256 algorithm. The proposed trust-based approach is used to dynamically manage the QoS violations among the partners in the federation. We have implemented the proposed approach using the CloudSim toolkit, and the analysis of the results are also given.