Fast Conflict Detection Research Articles

Fast Conflict Detection for Multi-Dimensional Packet Filters

To support advanced network services, Internet routers must perform packet classification based on a set of rules called packet filters. If two or more filters overlap, a filter conflict will occur and lead to ambiguity in packet classification. Further, it may affect network security or even the correctness of packet routing. Hence, it is necessary to detect conflicts to avoid the above problems. In recent years, many conflict detection algorithms have been proposed, but most of them detect conflicts for only prefix fields (i.e., source/destination IP address fields) of filters. For greater practicality, conflict detection must include non-prefix fields such as source/destination IP port fields and the protocol field. In this study, we propose an efficient conflict detection algorithm for five-dimensional filters, which include both prefix and non-prefix fields. In the proposed algorithm, a tiny lookup table is created for quickly filtering out a large portion of non-conflicting filter pairs, thereby reducing the overall conflict detection time. Experimental results show that our algorithm reduces the detection time by 10% to 28% compared with other conflict detection algorithms for 20 K filter databases. More importantly, our algorithm can be used to extend any existing conflict detection algorithms for two-dimensional filters to support fast conflict detection for five-dimensional filters.

Social distance and anonymity modulate fairness consideration: An ERP study.

Previous research indicated that fairness consideration can be influenced by social distance. However, it is not clear whether social distance and anonymity have an interactive impact on fairness evaluation during asset distribution and whether these processes can be documented in brain activity. Using a modified ultimatum game combined with measures of event related potential (ERP), we examined how social distance and anonymity modulate brain response to inequality. At the behavior level, we found that acceptance rate and reaction time can be substantially modified by social distance and anonymity. Feedback-related negativity, an ERP component associated with conflict between cognitive and emotion motives, was more negative in response to unfairness than fairness from strangers; however, it showed an opposite trend for unfair offers provided by friends, suggesting that the influence of social distance on fairness perception is relatively fast. The P300 in response to fair offers was more positive when the proposers made offers when uncertain about partner identity than when certain about partner identity. These results suggest that unfairness is evaluated in a fast conflict detection stage and a slower stage that integrates more complex social contextual factors such as anonymity.

Fast conflict detection using probability flow

Probabilistic conflict detection has recently attracted much attention from researchers due to its importance in safe automated transport systems; however, current methods struggle to accurately calculate in real-time the probability of conflict for complex vehicle maneuvers in cluttered environments. We present a formulation for the general probabilistic conflict detection problem using the flow rate of conflict probability at the boundary of a conflict zone. For Gaussian distributed vehicle states, we then derive a tight upper bound for the probability of conflict over a time period, which can be calculated in real-time using adaptive numerical integration techniques. We present two examples to illustrate the performance of this method: the first example shows that this method is very efficient for simple environments and the second example shows that this method can calculate the conflict probability upper bound in real-time even for complex vehicle maneuvers in cluttered environments.

Fast and scalable conflict detection for packet classifiers

Packet filters provide rules for classifying packets based on header fields. High speed packet classification has received much study. However, the twin problems of fast updates and fast conflict detection have not received much attention. A conflict occurs when two classifiers overlap, potentially creating ambiguity for packets that match both filters. For example, if Rule 1 specifies that all packets going to CNN be rate controlled and Rule 2 specifies that all packets coming from Walmart be given high priority, the rules conflict for traffic from Walmart to CNN. There has been prior work on efficient conflict detection for two-dimensional classifiers. However, the best known algorithm for conflict detection for general classifiers is the naive O( N 2) algorithm of comparing each pair of rules for a conflict. In this paper, we describe an efficient and scalable conflict detection algorithm for the general case that is significantly faster. For example, for a database of 20 000 rules, our algorithm is 40 times faster than the naive implementation. Even without considering conflicts, our algorithm also provides a packet classifier with fast updates and fast lookups that can be used for stateful packet filtering.

The TDG Application : An Highly Interactive Tool ForRailway Timetable Planning And Traffic Monitoring

Alcatel Alsthom Recherche has developed for GEC ALSTHOM a new tool for the railway traffic management. This tool consists in an highly interactive Time Distance Graph (TDG), which may be used for (i) the timetable making and adjustment, (ii) the traffic monitoring and (iii) the historic assessment. The TDG comprises a fast conflict detection module which allows the operator to efficiently detect and solve the conflicts in a « what if » mode. As the TDG is based on a generic railway model, it can be used on a large range of different railway networks (main lines, single lines, suburban line, commuter). This paper describes, after a brief introduction of the objectives of this tool, the main functional features and the technical architecture of this new traffic managing subsystem.