Fast Rerouting Against Multi-Link Failures Without Topology Constraint - 2018 PROJECT TITLE :Fast Rerouting Against Multi-Link Failures Without Topology Constraint - 2018ABSTRACT:Multi-link failures might incur heavy packet loss and degrade the network performance. Fast rerouting has been proposed to address this issue by enabling routing protections. However, the effectiveness and potency issues of fast rerouting are not well addressed. In particular, the protection performance of existing approaches isn't satisfactory whether or not the overhead is high, and topology constraints need to be met for the approaches to realize a whole protection. To optimize the efficiency, we tend to first answer the question that whether label-free routing will provide a complete protection against arbitrary multi-link failures in any networks. We propose a model for interface-specific-routing that will be seen as a general label-free routing. We tend to analyze the conditions underneath that a multi-link failure will induce routing loops. And then, we tend to present that there exist some networks in that no interface-specific-routing (ISR) will be created to shield the routing against any k-link failures (k = 2). Then, we tend to propose a tunneling on demand (TOD) approach, that covers most failures with ISR, and activate tunneling solely when failures can't be detoured around by ISR. We tend to develop algorithms to compute ISR properly so as to minimize the quantity of activated tunnels, and compute the protection tunnels if necessary. We prove that TOD will defend routing against any single-link failures and twin-link failures. We tend to evaluate TOD by simulations with real-world topologies. The results show that TOD will achieve a near one hundredp.c protection ratio with small tunneling overhead for multi-link failures, creating a better tradeoff than the state-of-the-art label-based mostly approaches. Did you like this research project? To get this research project Guidelines, Training and Code... Click Here facebook twitter google+ linkedin stumble pinterest Enhancing Localization Scalability and Accuracy via Opportunistic Sensing - 2018 FINE: A Framework for Distributed Learning on Incomplete Observations for Heterogeneous Crowdsensing Networks - 2018