Distributed top-k aggregation queries at large

Top-k query processing is a fundamental building block for efficient ranking in a large number of applications. Efficiency is a central issue, especially for distributed settings, when the data is spread across different nodes in a network. This paper introduces novel optimization methods for top-k aggregation queries in such distributed environments. The optimizations can be applied to all algorithms that fall into the frameworks of the prior TPUT and KLEE methods. The optimizations address three degrees of freedom: 1) hierarchically grouping input lists into top-k operator trees and optimizing the tree structure, 2) computing data-adaptive scan depths for different input sources, and 3) data-adaptive sampling of a small subset of input sources in scenarios with hundreds or thousands of query-relevant network nodes. All optimizations are based on a statistical cost model that utilizes local synopses, e.g., in the form of histograms, efficiently computed convolutions, and estimators based on order statistics. The paper presents comprehensive experiments, with three different real-life datasets and using the ns-2 network simulator for a packet-level simulation of a large Internet-style network

Dynamic Time-Dependent Route Planning in Road Networks with User Preferences

There has been tremendous progress in algorithmic methods for computing driving directions on road networks. Most of that work focuses on time-independent route planning, where it is assumed that the cost on each arc is constant per query. In practice, the current traffic situation significantly influences the travel time on large parts of the road network, and it changes over the day. One can distinguish between traffic congestion that can be predicted using historical traffic data, and congestion due to unpredictable events, e.g., accidents. In this work, we study the \emph{dynamic and time-dependent} route planning problem, which takes both prediction (based on historical data) and live traffic into account. To this end, we propose a practical algorithm that, while robust to user preferences, is able to integrate global changes of the time-dependent metric~(e.g., due to traffic updates or user restrictions) faster than previous approaches, while allowing subsequent queries that enable interactive applications

Shortest path or anchor-based route choice: a large-scale empirical analysis of minicab routing in London

Understanding and modelling route choice behaviour is central to predicting the formation and propagation of urban road congestion. Yet within conventional literature disagreements persist around the nature of route choice behaviour, and how it should be modelled. In this paper, both the shortest path and anchor-based perspectives on route choice behaviour are explored through an empirical analysis of nearly 700,000 minicab routes across London, United Kingdom. In the first set of analyses, the degree of similarity between observed routes and possible shortest paths is established. Shortest paths demonstrate poor performance in predicting both observed route choice and characteristics. The second stage of analysis explores the influence of specific urban features, named anchors, in route choice. These analyses show that certain features attract more route choices than would be expected were individuals choosing route based on cost minimisation alone. Instead, the results indicate that major urban features form the basis of route choice planning – being selected disproportionately more often, and causing asymmetry in route choice volumes by direction of travel. At a finer scale, decisions made at minor road features are furthermore demonstrated to influence routing patterns. The results indicate a need to revisit the basis of how routes are modelled, shifting from the shortest path perspective to a mechanism structured around urban features. In concluding, the main trends are synthesised within an initial framework for route choice modelling, and presents potential extensions of this research

Strategies for Scaleable Communication and Coordination in Multi-Agent (UAV) Systems

A system is considered in which agents (UAVs) must cooperatively discover interest-points (i.e., burning trees, geographical features) evolving over a grid. The objective is to locate as many interest-points as possible in the shortest possible time frame. There are two main problems: a control problem, where agents must collectively determine the optimal action, and a communication problem, where agents must share their local states and infer a common global state. Both problems become intractable when the number of agents is large. This survey/concept paper curates a broad selection of work in the literature pointing to a possible solution; a unified control/communication architecture within the framework of reinforcement learning. Two components of this architecture are locally interactive structure in the state-space, and hierarchical multi-level clustering for system-wide communication. The former mitigates the complexity of the control problem and the latter adapts to fundamental throughput constraints in wireless networks. The challenges of applying reinforcement learning to multi-agent systems are discussed. The role of clustering is explored in multi-agent communication. Research directions are suggested to unify these components

Congestion Managed Multicast Routing in Wireless Mesh Network

To provide broad band connectivity to the mobile users and to build a self-structured network, where it is not possible to have wired network, “Wireless Mesh Networks” are the most vital suitable technology. Routing in Wireless Mesh Networks is a multi-objective nonlinear optimization problem with some constraints. We explore multicast routing for least-cost, delay-sensitive and congestion-sensitive in optimizing the routing in Wireless mesh networks (WMNs). In this work different parameters are associated like edge cost, edge delay and edge congestion. The aim is to create a tree traversing which the set of target nodes are spanned, so as to make the cost and congestion to be minimum with a bounded delay over the path between every pair of source and destination. Since searching optimal routing satisfying multi constraints concurrently is an NP complete problem, we have presented a competent estimated algorithm certified with experimental results, which shows that the performance of presented algorithm is nearly optimum

Scale-free networks and scalable interdomain routing

Trabalho apresentado no âmbito do Mestrado em Engenharia Informática, como requisito parcial para obtenção do grau de Mestre em Engenharia InformáticaThe exponential growth of the Internet, due to its tremendous success, has brought to light some limitations of the current design at the routing and arquitectural level, such as scalability and convergence as well as the lack of support for traffic engineering, mobility, route differentiation and security. Some of these issues arise from the design of the current architecture, while others are caused by the interdomain routing scheme - BGP. Since it would be quite difficult to add support for the aforementioned issues, both in the interdomain architecture and in the in the routing scheme, various researchers believe that a solution can only achieved via a new architecture and (possibly) a new routing scheme. A new routing strategy has emerged from the studies regarding large-scale networks, which is suitable for a special type of large-scale networks which characteristics are independent of network size: scale-free networks. Using the greedy routing strategy a node routes a message to a given destination using only the information regarding the destination and its neighbours, choosing the one which is closest to the destination. This routing strategy ensures the following remarkable properties: routing state in the order of the number of neighbours; no requirements on nodes to exchange messages in order to perform routing; chosen paths are the shortest ones. This dissertation aims at: studying the aforementioned problems, studying the Internet configuration as a scale-free network, and defining a preliminary path onto the definition of a greedy routing scheme for interdomain routing

A Note on Hierarchical Routing Algorithms based on Traverse-oriented Road Networks

Finding the shortest route has become almost an omnipresent task in our society. This task runs across in the internet, for outdoor activities and of course within in-car navigation systems. Nowadays finding the shortest path gets more popular compared to the past because of rising fuel prices. That is why driving along an optimised route has financial benefits on the one hand and on the other hand it spares energy resources of our environment. In this paper a hierarchical approach of route computation based on traverse elements is discussed. Furthermore an experimental setup consisting of an open source database management system (PostgreSQL/PostGIS) is realised to verify the theoretical part

An Optimised Shortest Path Algorithm for Network Rotuting & SDN: Improvement on Bellman-Ford Algorithm

Network routing algorithms form the backbone of data transmission in modern network architectures, with implications for efficiency, speed, and reliability. This research aims to critically investigate and compare three prominent routing algorithms: Bellman-Ford, Shortest Path Faster Algorithm (SPFA), and our novel improved variant of Bellman-Ford, the Space-efficient Cost-Balancing Bellman-Ford (SCBF). We evaluate the performance of these algorithms in terms of time and space complexity, memory utilization, and routing efficacy, within a simulated network environment. Our results indicate that while Bellman-Ford provides consistent performance, both SPFA and SCBF present improvements in specific scenarios with the SCBF showing notable enhancements in space efficiency. The innovative SCBF algorithm provides competitive performance and greater space efficiency, potentially making it a valuable contribution to the development of network routing protocols. Further research is encouraged to optimize and evaluate these algorithms in real-world network conditions. This study underscores the continuous need for algorithmic innovation in response to evolving network demands