Routing and caching on DHTS

L'obiettivo della tesi e' quello di analizzare i principali meccanismi di caching e routing implementati oggigiorno nelle DHT piu' utilizzate. In particolare, la nostra analisi mostra come tali meccanismi siano sostanzialmente inefficaci nel garantire un adeguato load balancing tra i peers; le principali cause di questo fenomeno sono individuate nella struttura, eccessivamente rigida, adottata dalle DHT e nella mancanza di correlazione tra meccanismi di routing e di caching. Viene quindi proposto un diverso overlay, organizzato in base a una struttura ipercubica, che permetta di adottare un algoritmo di routing piu' flessibile e di sviluppare due meccanismi di caching e routing strettamente interconnessi. In particolare, l'overlay ottenuto riesce a garantire che ogni nodo subisca un carico al piu' costante, con una taglia di cache costante e una complessita' di routing polilogaritmica nel caso peggior

Novel graph analytics for enhancing data insight

Graph analytics is a fast growing and significant field in the visualization and data mining community, which is applied on numerous high-impact applications such as, network security, finance, and health care, providing users with adequate knowledge across various patterns within a given system. Although a series of methods have been developed in the past years for the analysis of unstructured collections of multi-dimensional points, graph analytics has only recently been explored. Despite the significant progress that has been achieved recently, there are still many open issues in the area, concerning not only the performance of the graph mining algorithms, but also producing effective graph visualizations in order to enhance human perception. The current thesis deals with the investigation of novel methods for graph analytics, in order to enhance data insight. Towards this direction, the current thesis proposes two methods so as to perform graph mining and visualization. Based on previous works related to graph mining, the current thesis suggests a set of novel graph features that are particularly efficient in identifying the behavioral patterns of the nodes on the graph. The specific features proposed, are able to capture the interaction of the neighborhoods with other nodes on the graph. Moreover, unlike previous approaches, the graph features introduced herein, include information from multiple node neighborhood sizes, thus capture long-range correlations between the nodes, and are able to depict the behavioral aspects of each node with high accuracy. Experimental evaluation on multiple datasets, shows that the use of the proposed graph features for the graph mining procedure, provides better results than the use of other state-of-the-art graph features. Thereafter, the focus is laid on the improvement of graph visualization methods towards enhanced human insight. In order to achieve this, the current thesis uses non-linear deformations so as to reduce visual clutter. Non-linear deformations have been previously used to magnify significant/cluttered regions in data or images for reducing clutter and enhancing the perception of patterns. Extending previous approaches, this work introduces a hierarchical approach for non-linear deformation that aims to reduce visual clutter by magnifying significant regions, and leading to enhanced visualizations of one/two/three-dimensional datasets. In this context, an energy function is utilized, which aims to determine the optimal deformation for every local region in the data, taking the information from multiple single-layer significance maps into consideration. The problem is subsequently transformed into an optimization problem for the minimization of the energy function under specific spatial constraints. Extended experimental evaluation provides evidence that the proposed hierarchical approach for the generation of the significance map surpasses current methods, and manages to effectively identify significant regions and deliver better results. The thesis is concluded with a discussion outlining the major achievements of the current work, as well as some possible drawbacks and other open issues of the proposed approaches that could be addressed in future works.Open Acces

Multiple Bus Networks for Binary -Tree Algorithms.

Multiple bus networks (MBN) connect processors via buses. This dissertation addresses issues related to running binary-tree algorithms on MBNs. These algorithms are of a fundamental nature, and reduce inputs at leaves of a binary tree to a result at the root. We study the relationships between running time, degree (maximum number of connections per processor) and loading (maximum number of connections per bus). We also investigate fault-tolerance, meshes enhanced with MBNs, and VLSI layouts for binary-tree MBNs. We prove that the loading of optimal-time, degree-2, binary-tree MBNs is non-constant. In establishing this result, we derive three loading lower bounds Wn , W&parl0;n23&parr0; and W&parl0;nlogn&parr0; , each tighter than the previous one. We also show that if the degree is increased to 3, then the loading can be a constant. A constant loading degree-2 MBN exists, if the algorithm is allowed to run slower than the optimal. We introduce a new enhanced mesh architecture (employing binary-tree MBNs) that captures features of all existing enhanced meshes. This architecture is more flexible, allowing all existing enhanced mesh results to be ported to a more implementable platform. We present two methods for imparting tolerance to bus and processor faults in binary-tree MBNs. One of the methods is general, and can be used with any MBN and for both processor and bus faults. A key feature of this method is that it permits the network designer to designate a set of buses as unimportant and consider all faulty buses as unimportant. This minimizes the impact of faulty elements on the MBN. The second method is specific to bus faults in binary-tree MBNs, whose features it exploits to produce faster solutions. We also derive a series of results that distill the lower bound on the perimeter layout area of optimal-time, binary-tree MBNs to a single conjecture. Based on this we believe that optimal-time, binary-tree MBNs require no less area than a balanced tree topology even though such MBNs can reuse buses over various steps of the algorithm

LIPIcs, Volume 244, ESA 2022, Complete Volume

LIPIcs, Volume 244, ESA 2022, Complete Volum