An efficient algorithm for concurrent priority queue heaps
Information Processing Letters
Deriving traffic demands for operational IP networks: methodology and experience
IEEE/ACM Transactions on Networking (TON)
Frequency Estimation of Internet Packet Streams with Limited Space
ESA '02 Proceedings of the 10th Annual European Symposium on Algorithms
A simple algorithm for finding frequent elements in streams and bags
ACM Transactions on Database Systems (TODS)
Unix Systems Programming: Communication, Concurrency and Threads
Unix Systems Programming: Communication, Concurrency and Threads
New directions in traffic measurement and accounting: Focusing on the elephants, ignoring the mice
ACM Transactions on Computer Systems (TOCS)
Controlling High-Bandwidth Flows at the Congested Router
ICNP '01 Proceedings of the Ninth International Conference on Network Protocols
Finding frequent items in data streams
Theoretical Computer Science - Special issue on automata, languages and programming
Data streaming algorithms for efficient and accurate estimation of flow size distribution
Proceedings of the joint international conference on Measurement and modeling of computer systems
An improved data stream summary: the count-min sketch and its applications
Journal of Algorithms
An integrated efficient solution for computing frequent and top-k elements in data streams
ACM Transactions on Database Systems (TODS)
Fast data stream algorithms using associative memories
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
OSDI'04 Proceedings of the 6th conference on Symposium on Opearting Systems Design & Implementation - Volume 6
Approximate frequency counts over data streams
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Finding frequent items in data streams
Proceedings of the VLDB Endowment
CAM conscious integrated answering of frequent elements and top-k queries over data streams
Proceedings of the 4th international workshop on Data management on new hardware
CoTS: A Scalable Framework for Parallelizing Frequency Counting over Data Streams
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
Space-optimal heavy hitters with strong error bounds
Proceedings of the twenty-eighth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Thread cooperation in multicore architectures for frequency counting over multiple data streams
Proceedings of the VLDB Endowment
The Art of Multiprocessor Programming
The Art of Multiprocessor Programming
Scalable identification and measurement of heavy-hitters
Computer Communications
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Identifying frequent items in high-speed network is important for a variety of network applications ranging from traffic engineering to anomaly detection such as detection of denial of service attacks. To deal with high packet arrival rate, it is desirable that such systems are able to support very high update throughput. The advent of multi-core processors calls for efficient parallel designs which can effectively utilize the parallelism of the multi-cores. In this paper, we address the problem of parallelizing weighted frequency counting in the context of multi-core processors. We discuss the challenges in designing an efficient parallel system. Our evaluation and analysis reveals that the naive fine-grained lock design results in excessive overhead and wait, which in turn leads to severe performance degradation in multi-core architectures. Based on our analysis, we propose a novel method: precision integrated method (PRIM). PRIM makes use of the temporal imprecision concept to significantly reduce the merge overhead at the cost of relatively large memory space used. Both the theoretical analysis and real traffic experiments demonstrate that PRIM delivers almost linear speedup.