Deriving traffic demands for operational IP networks: methodology and experience
IEEE/ACM Transactions on Networking (TON)
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
New directions in traffic measurement and accounting: Focusing on the elephants, ignoring the mice
ACM Transactions on Computer Systems (TOCS)
OpenFlow: enabling innovation in campus networks
ACM SIGCOMM Computer Communication Review
Fast monitoring of traffic subpopulations
Proceedings of the 8th ACM SIGCOMM conference on Internet measurement
EvoCaches: Application-specific Adaptation of Cache Mappings
AHS '09 Proceedings of the 2009 NASA/ESA Conference on Adaptive Hardware and Systems
Experience with high-speed automated application-identification for network-management
Proceedings of the 5th ACM/IEEE Symposium on Architectures for Networking and Communications Systems
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Several important network applications cannot easily scale to higher data rates without requiring focusing just on the large traffic flows. Recent works have discussed algorithmic solutions that trade-off accuracy to gain efficiency for filtering and tracking the so-called "heavyhitters". However, a major limit is that flows must initially go through a filtering process, making it impossible to track state associated with the first few packets of the flow. In this paper, we propose a different paradigm in tracking the large flows which overcomes this limit. We view the problem as that of managing a small flow cache with a finely tuned replacement policy that strives to avoid evicting the heavy-hitters. Our scheme starts from recorded traffic traces and uses Genetic Algorithms to evolve a replacement policy tailored for supporting seamless, stateful traffic-processing. We evaluate our scheme in terms of missed heavy-hitters: it performs close to the optimal, oracle-based policy, and when compared to other standard policies, it consistently outperforms them, even by a factor of two in most cases.