Network updates such as policy and routing changes occur frequently in Software Defined Networks (SDN). Updates should be performed consistently, preventing temporary disruptions, and should require as little overhead as possible. Scalability is increasingly becoming an essential requirement in SDN. In this paper we propose to use time-triggered network updates to achieve consistent updates. Our proposed solution requires lower overhead than existing update approaches, without compromising the consistency during the update. We demonstrate that accurate time enables far more scalable consistent updates in SDN than previously available. In addition, it provides the SDN programmer with fine-grained control over the tradeoff between consistency and scalability.
Time-of-day routing @cite_12 routes traffic to different destinations based on the time-of-day. Path calendaring @cite_45 can be used to configure network paths based on scheduled or foreseen traffic changes. The two latter examples are typically performed at a low rate and do not place demanding requirements on accuracy.
@cite_45 @cite_12
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Inferring AS-level end-to-end paths can be a valu- able tool for both network operators and researchers. A widely known technique for inferring end-to-end paths is to perform traceroute from sources to destinations. Unfortunately, traceroute requires the access to source machines and is resource consuming. In this paper, we propose two algorithms for AS-level end-to-end path inference. The key idea of our algorithm is to exploit the AS paths appeared in BGP routing tables and infer AS paths based on the ones. In addition, our algorithms infer AS paths on the granularity of destination prefix instead of destination AS. That is, we infer AS paths from any source AS to any destination prefix. This is essential since routing in the Internet is determined based on destination prefixes instead of destination ASs. The validation results show that our algorithm yields accuracy up to 95 for exact match and accuracy up to 97 for path length match. We further extend our algorithm to infer a set of potential AS paths between a source AS and a destination prefix. We find that on average, 86 of inferred AS path sets are accurate in the sense that one of the paths in the set matches the actual AS path. Note that our algorithms require BGP routing tables only and do not require additional data trace or access to either sources or destinations. In addition, we demonstrate that the accuracy of this BGP-based inference approach cannot go beyond 90 . We consider the application of route planning in large public-transportation networks (buses, trains, subways, etc). Many connections in such networks are operated at periodic time intervals. When a set of connections has sufficient periodicity, it becomes more efficient to store the time range and frequency (e.g., every 15 minutes from 8:00am-6:00pm) instead of storing each of the time events separately. Identifying an optimal frequency-compression is NP-hard, so we present a time- and space-efficient heuristic. We show how we can use this compression to not only save space but also query time. We particularly consider profile queries, which ask for all optimal routes with departure times in a given interval (e.g., a whole day). In particular, we design a new version of Dijkstra's algorithm that works with frequency-based labels and is suitable for profile queries. We evaluate the savings of our approach on two metropolitan and three country-wide public-transportation networks. On our largest network, we simultaneously achieve a better space consumption than all previous methods as well as profile query times that are about 5 times faster than the best previous method. We also improve Transfer Patterns, a state-of-the-art technique for fully realistic route planning in large public-transportation networks. In particular, we accelerate the expensive preprocessing by a factor of 60 compared to the original publication. We show how to route on very large public transportation networks (up to half a billion arcs) with average query times of a few milliseconds. We take into account many realistic features like: traffic days, walking between stations, queries between geographic locations instead of a source and a target station, and multi-criteria cost functions. Our algorithm is based on two key observations: (1) many shortest paths share the same transfer pattern, i.e., the sequence of stations where a change of vehicle occurs; (2) direct connections without change of vehicle can be looked up quickly. We precompute the respective data; in practice, this can be done in time linear in the network size, at the expense of a small fraction of non-optimal results. We have accelerated public transportation routing on Google Maps with a system based on our ideas. We report experimental results for three data sets of various kinds and sizes. In this paper, we propose a citywide and real-time model for estimating the travel time of any path (represented as a sequence of connected road segments) in real time in a city, based on the GPS trajectories of vehicles received in current time slots and over a period of history as well as map data sources. Though this is a strategically important task in many traffic monitoring and routing systems, the problem has not been well solved yet given the following three challenges. The first is the data sparsity problem, i.e., many road segments may not be traveled by any GPS-equipped vehicles in present time slot. In most cases, we cannot find a trajectory exactly traversing a query path either. Second, for the fragment of a path with trajectories, they are multiple ways of using (or combining) the trajectories to estimate the corresponding travel time. Finding an optimal combination is a challenging problem, subject to a tradeoff between the length of a path and the number of trajectories traversing the path (i.e., support). Third, we need to instantly answer users' queries which may occur in any part of a given city. This calls for an efficient, scalable and effective solution that can enable a citywide and real-time travel time estimation. To address these challenges, we model different drivers' travel times on different road segments in different time slots with a three dimension tensor. Combined with geospatial, temporal and historical contexts learned from trajectories and map data, we fill in the tensor's missing values through a context-aware tensor decomposition approach. We then devise and prove an object function to model the aforementioned tradeoff, with which we find the most optimal concatenation of trajectories for an estimate through a dynamic programming solution. In addition, we propose using frequent trajectory patterns (mined from historical trajectories) to scale down the candidates of concatenation and a suffix-tree-based index to manage the trajectories received in the present time slot. We evaluate our method based on extensive experiments, using GPS trajectories generated by more than 32,000 taxis over a period of two months. The results demonstrate the effectiveness, efficiency and scalability of our method beyond baseline approaches.