857 research outputs found
Self-Healing Protocols for Connectivity Maintenance in Unstructured Overlays
In this paper, we discuss on the use of self-organizing protocols to improve
the reliability of dynamic Peer-to-Peer (P2P) overlay networks. Two similar
approaches are studied, which are based on local knowledge of the nodes' 2nd
neighborhood. The first scheme is a simple protocol requiring interactions
among nodes and their direct neighbors. The second scheme adds a check on the
Edge Clustering Coefficient (ECC), a local measure that allows determining
edges connecting different clusters in the network. The performed simulation
assessment evaluates these protocols over uniform networks, clustered networks
and scale-free networks. Different failure modes are considered. Results
demonstrate the effectiveness of the proposal.Comment: The paper has been accepted to the journal Peer-to-Peer Networking
and Applications. The final publication is available at Springer via
http://dx.doi.org/10.1007/s12083-015-0384-
Resource discovery for distributed computing systems: A comprehensive survey
Large-scale distributed computing environments provide a vast amount of heterogeneous computing resources from different sources for resource sharing and distributed computing. Discovering appropriate resources in such environments is a challenge which involves several different subjects. In this paper, we provide an investigation on the current state of resource discovery protocols, mechanisms, and platforms for large-scale distributed environments, focusing on the design aspects. We classify all related aspects, general steps, and requirements to construct a novel resource discovery solution in three categories consisting of structures, methods, and issues. Accordingly, we review the literature, analyzing various aspects for each category
A scalable approach for content based image retrieval in cloud datacenter
The emergence of cloud datacenters enhances the capability of online data storage. Since massive data is stored in datacenters, it is necessary to effectively locate and access interest data in such a distributed system. However, traditional search techniques only allow users to search images over exact-match keywords through a centralized index. These techniques cannot satisfy the requirements of content based image retrieval (CBIR). In this paper, we propose a scalable image retrieval framework which can efficiently support content similarity search and semantic search in the distributed environment. Its key idea is to integrate image feature vectors into distributed hash tables (DHTs) by exploiting the property of locality sensitive hashing (LSH). Thus, images with similar content are most likely gathered into the same node without the knowledge of any global information. For searching semantically close images, the relevance feedback is adopted in our system to overcome the gap between low-level features and high-level features. We show that our approach yields high recall rate with good load balance and only requires a few number of hops
Enhancing learning with technology
Specht, M., & Klemke, R. (2013, 26-27 September). Enhancing Learning with Technology. In D. Milosevic (Ed.), Proceedings of the fourth international conference on eLearning (eLearning 2013) (pp. 37-45). Belgrade Metropolitan University, Belgrade, Serbia. http://econference.metropolitan.ac.rs/We are living in a technology-enhanced world. Also learning is affected by recent, upcoming, and foreseen
technological changes. This paper gives a bird’s eye view to technological trends and reflects how learning can benefit
from them
Autonomous Gossiping: A self-organizing epidemic algorithm for selective information dissemination in mobile ad-hoc networks
We introduce autonomous gossiping (A/G), a new genre epidemic algorithm for selective dissemination of information in contrast to previous usage of epidemic algorithms which flood the whole network. A/G is a paradigm which suits well in a mobile ad-hoc networking (MANET) environment because it does not require any infrastructure or middleware like multicast tree and (un)subscription maintenance for publish/subscribe, but uses ecological and economic principles in a self-organizing manner in order to achieve its selectivity. The trade-off of using an infrastructure-less self-organizing mechanism like A/G is that it does not guarantee completeness deterministically as is one of the original objectives of alternate selective dissemination schemes like publish/subscribe. We argue that such incompleteness is not a problem in many non-critical real-life civilian application scenarios and realistic node mobility patterns, where the overhead of infrastructure maintenance may outweigh the benefits of completeness, more over, at present there exists no mechanism to realize publish/subscribe or other paradigms for selective dissemination in MANET environments. A/G's reliance and hence vulnerability on cooperation of mobile nodes is also much less as compared to other possible schemes using routing information, since it does not expect node philanthropy for forwarding/carrying information, but only cooperation to the extent that nodes already carrying the information pass it on to other suitable ones. Thus autonomous gossiping is expected to be a light-weight infrastructure-less information dissemination service for MANETs, and hence support any-to-many communication (flexible casting) without the need to establish and maintain separate routing information (e.g., multicast trees)
From online social network analysis to a user-centric private sharing system
Online social networks (OSNs) have become a massive repository of data constructed
from individuals’ inputs: posts, photos, feedbacks, locations, etc. By
analyzing such data, meaningful knowledge is generated that can affect individuals’
beliefs, desires, happiness and choices—a data circulation started from individuals
and ended in individuals! The OSN owners, as the one authority having full control
over the stored data, make the data available for research, advertisement and other
purposes. However, the individuals are missed in this circle while they generate
the data and shape the OSN structure.
In this thesis, we started by introducing approximation algorithms for finding
the most influential individuals in a social graph and modeling the spread of
information. To do so, we considered the communities of individuals that are
shaped in a social graph. The social graph is extracted from the data stored and
controlled centrally, which can cause privacy breaches and lead to individuals’
concerns. Therefore, we introduced UPSS: the user-centric private sharing system, in
which the individuals are considered as the real data owners and provides secure
and private data sharing on untrusted servers.
The UPSS’s public API allows the application developers to implement applications
as diverse as OSNs, document redaction systems with integrity properties,
censorship-resistant systems, health care auditing systems, distributed version control
systems with flexible access controls and a filesystem in userspace. Accessing
users’ data is possible only with explicit user consent. We implemented the two
later cases to show the applicability of UPSS.
Supporting different storage models by UPSS enables us to have a local, remote
and global filesystem in userspace with one unique core filesystem implementation
and having it mounted with different block stores.
By designing and implementing UPSS, we show that security and privacy
can be addressed at the same time in the systems that need selective, secure and collaborative information sharing without requiring complete trust
- …