Slow convergence in the lnternet can be directly attributed to the "path exploration" phenomenon, inherent in aU path vector protocols. The root cause €or path exploration is the dependency among paths propagated through the network. Addressing this problem in BGP is particularly dimcult as the AS paths exchanged between BGP routers are highly summarized. In this paper, we describe why path exploration cannot be countered effectively within the existing BGP framework, and propose a simple, novel mechanism-forward edge sequence numbersto annotate the AS paths with additional "path dependency'' information. We then develop an enhanced path vector algorithm, EPIC, shown to limit path expIoration and lead to faster convergence. In contrast to other solutions, ours is shown to be correct on a very general model of Internet topology end BGP operation. Using theoretical analysis and simulations, we demonstrate that EPlC can achieve a dramatic improvement in routing convergence, compared to BGP and other existing solutions.

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Abstract-The Distributed Denial of Services (DDoS) attack is a serious threat to the legitimate use of the Internet. Prevention mechanisms are thwarted by the ability of attackers to forge, or spoof, the source addresses in IP packets. By employing IP spoofing, attackers can evade detection and put a substantial burden on the destination network for policing attack packets.In this paper we propose an inter-domain packet filter (IDPF) architecture that can mitigate the level of IP spoofing on the Internet. IDPFs are constructed from the information implicit in BGP route updates and are deployed in network border routers. A key feature of the scheme is that it does not require global routing information. Based on extensive simulation studies, we show that even with partial deployment on the Internet, IDPFs can proactively limit the spoofing capability of attackers. In addition, they can help localize the origin of an attack packet to a small number of candidate networks.

Abstract-We propose and develop a novel virtual time reference system as a unifying scheduling framework to provide scalable support for guaranteed services. This virtual time reference system is designed as a conceptual framework upon which guaranteed services can be implemented in a scalable manner using the DiffServ paradigm. The key construct in the proposed virtual time reference system is the notion of packet virtual time stamps, whose computation is core stateless, i.e., no per-flow states are required for its computation. In this paper, we lay the theoretical foundation for the definition and construction of packet virtual time stamps. We describe how per-hop behavior of a core router (or rather its scheduling mechanism) can be characterized via packet virtual time stamps, and based on this characterization, establish end-to-end per-flow delay bounds. Consequently, we demonstrate that, in terms of its ability to support guaranteed services, the proposed virtual time reference system has the same expressive power and generality as the IntServ model. Furthermore, we show that the notion of packet virtual time stamps leads to the design of new core stateless scheduling algorithms, especially work-conserving ones. In addition, our framework does not exclude the use of existing scheduling algorithms such as stateful fair queuing algorithms to support guaranteed services.

Knowledge graph completion (KGC) is a hot topic in knowledge graph construction and related applications, which aims to complete the structure of knowledge graph by predicting the missing entities or relationships in knowledge graph and mining unknown facts. Starting from the definition and types of KGC, existing technologies for KGC are analyzed in categories. From the evolving point of view, the KGC technologies could be divided into traditional and representation learning based methods. The former mainly includes rule-based reasoning method, probability graph model, such as Markov logic network, and graph computation based method. The latter further includes translation model based, semantic matching model based, representation learning based and other neural network model based methods. In this paper, different KGC technologies are introduced, including their advantages, disadvantages and applicable fields. Finally the main challenges and problems faced by the KGC are discussed, as well as the potential research directions.