Algorithms for clustering data
Algorithms for clustering data
Efficient Hardware Hashing Functions for High Performance Computers
IEEE Transactions on Computers
Sublinear time algorithms for metric space problems
STOC '99 Proceedings of the thirty-first annual ACM symposium on Theory of computing
Signature files: an access method for documents and its analytical performance evaluation
ACM Transactions on Information Systems (TOIS)
Space/time trade-offs in hash coding with allowable errors
Communications of the ACM
Trajectory sampling for direct traffic observation
IEEE/ACM Transactions on Networking (TON)
Introduction to algorithms
IEEE/ACM Transactions on Networking (TON)
Better streaming algorithms for clustering problems
Proceedings of the thirty-fifth annual ACM symposium on Theory of computing
A Sublinear Time Approximation Scheme for Clustering in Metric Spaces
FOCS '99 Proceedings of the 40th Annual Symposium on Foundations of Computer Science
FOCS '01 Proceedings of the 42nd IEEE symposium on Foundations of Computer Science
Simple network performance tomography
Proceedings of the 3rd ACM SIGCOMM conference on Internet measurement
The Bloomier filter: an efficient data structure for static support lookup tables
SODA '04 Proceedings of the fifteenth annual ACM-SIAM symposium on Discrete algorithms
An algebraic approach to practical and scalable overlay network monitoring
Proceedings of the 2004 conference on Applications, technologies, architectures, and protocols for computer communications
Fast hash table lookup using extended bloom filter: an aid to network processing
Proceedings of the 2005 conference on Applications, technologies, architectures, and protocols for computer communications
Towards unbiased end-to-end network diagnosis
Proceedings of the 2006 conference on Applications, technologies, architectures, and protocols for computer communications
VL2: a scalable and flexible data center network
Proceedings of the ACM SIGCOMM 2009 conference on Data communication
Every microsecond counts: tracking fine-grain latencies with a lossy difference aggregator
Proceedings of the ACM SIGCOMM 2009 conference on Data communication
NetReplay: a new network primitive
ACM SIGMETRICS Performance Evaluation Review
Two samples are enough: opportunistic flow-level latency estimation using netflow
INFOCOM'10 Proceedings of the 29th conference on Information communications
Proceedings of the ACM SIGCOMM 2010 conference
Proceedings of the ACM SIGCOMM 2010 conference
Hedera: dynamic flow scheduling for data center networks
NSDI'10 Proceedings of the 7th USENIX conference on Networked systems design and implementation
Seawall: performance isolation for cloud datacenter networks
HotCloud'10 Proceedings of the 2nd USENIX conference on Hot topics in cloud computing
Network traffic characteristics of data centers in the wild
IMC '10 Proceedings of the 10th ACM SIGCOMM conference on Internet measurement
Least squares quantization in PCM
IEEE Transactions on Information Theory
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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). To satisfy the need, researchers have proposed new architectures such as LDA and RLI that can provide fine-grained latency measurements. However, these architectures are fundamentally ossified in their design as they are designed to provide only a specific pre-configured aggregate measurement---either average latency across all packets (LDA) or per-flow latency measurements (RLI). Network operators, however, need latency measurements at both finer (e.g., packet) as well as flexible (e.g., flow subsets) levels of granularity. To bridge this gap, 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 scalable data structures with small storage needs (uses only 12.8 bits/packet), and uses a novel mechanism to reduce the query bandwidth significantly (by a factor of 17 compared to the naive method of sending packet queries individually).