Better streaming algorithms for clustering problems
Proceedings of the thirty-fifth annual ACM symposium on Theory of computing
Every microsecond counts: tracking fine-grain latencies with a lossy difference aggregator
Proceedings of the ACM SIGCOMM 2009 conference on Data communication
Proceedings of the ACM SIGCOMM 2010 conference
Proceedings of the ACM SIGCOMM 2010 conference
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Latency has become an important metric for network monitoring since the emergence of new latency-sensitive applications (e.g., algorithmic trading and high-performance computing). In this paper, to provide latency measurements at both finer (e.g., packet) as well as flexible (e.g., flow subsets) levels of granularity, we propose an architecture called MAPLE that essentially stores packet-level latencies in routers and allows network operators to query the latency of arbitrary traffic sub-populations. MAPLE is built using a scalable data structure called SVBF with small storage needs.