@Article{Posch2017a, author = {Posch, Daniel and Rainer, Benjamin and Hellwagner, Hermann}, journal = {IEEE/ACM Transactions on Networking}, title = {SAF: Stochastic Adaptive Forwarding in Named Data Networking}, year = {2017}, month = {apr}, number = {2}, pages = {14}, volume = {25}, abstract = {Forwarding decisions in classical IP-based networks are predetermined by routing. This is necessary to avoid loops, inhibiting opportunities to implement an adaptive and intelligent forwarding plane. Consequently, content distribution efficiency is reduced due to a lack of inherent multi-path transmission. In Named Data Networking (NDN) instead, routing shall hold a supporting role to forwarding, providing sufficient potential to enhance content dissemination at the forwarding plane. In this paper we design, implement, and evaluate a novel probability-based forwarding strategy, called Stochastic Adaptive Forwarding (SAF) for NDN. SAF imitates a self-adjusting water pipe system, intelligently guiding and distributing Interests through network crossings circumventing link failures and bottlenecks. Just as real pipe systems, SAF employs overpressure valves enabling congested nodes to lower pressure autonomously. Through an implicit feedback mechanism it is ensured that the fraction of the traffic forwarded via congested nodes decreases. By conducting simulations we show that our approach outperforms existing forwarding strategies in terms of the Interest satisfaction ratio in the majority of the evaluated scenarios. This is achieved by extensive utilization of NDN's multipath and content-lookup capabilities without relying on the routing plane. SAF explores the local environment by redirecting requests that are likely to be dropped anyway. This enables SAF to identify new paths to the content origin or to cached replicas, circumventing link failures and resource shortages without relying on routing updates.}, address = {New York, USA}, doi = {10.1109/TNET.2016.2614710}, language = {EN}, pdf = {https://www.itec.aau.at/bib/files/saf.pdf}, publisher = {IEEE}, url = {https://dx.doi.org/10.1109/TNET.2016.2614710} }