The Escrow transactional method
ACM Transactions on Database Systems (TODS)
Safe memory reclamation for dynamic lock-free objects using atomic reads and writes
Proceedings of the twenty-first annual symposium on Principles of distributed computing
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Gigascope: a stream database for network applications
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Load management and high availability in the Medusa distributed stream processing system
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
A Unified Framework for Monitoring Data Streams in Real Time
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Finding (Recently) Frequent Items in Distributed Data Streams
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Fast and approximate stream mining of quantiles and frequencies using graphics processors
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Proceedings of the eleventh ACM SIGPLAN symposium on Principles and practice of parallel programming
Continuous monitoring of top-k queries over sliding windows
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Split-ordered lists: Lock-free extensible hash tables
Journal of the ACM (JACM)
An integrated efficient solution for computing frequent and top-k elements in data streams
ACM Transactions on Database Systems (TODS)
On Hit Inflation Techniques and Detection in Streams of Web Advertising Networks
ICDCS '07 Proceedings of the 27th International Conference on Distributed Computing Systems
Approximate frequency counts over data streams
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Ad-hoc top-k query answering for data streams
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Executing stream joins on the cell processor
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Vectorized data processing on the cell broadband engine
DaMoN '07 Proceedings of the 3rd international workshop on Data management on new hardware
Relational joins on graphics processors
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
OLTP through the looking glass, and what we found there
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Finding frequent items in data streams
Proceedings of the VLDB Endowment
CoTS: A Scalable Framework for Parallelizing Frequency Counting over Data Streams
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
Parallelizing weighted frequency counting in high-speed network monitoring
Computer Communications
Parallel skyline computation on multicore architectures
Information Systems
PLP: page latch-free shared-everything OLTP
Proceedings of the VLDB Endowment
Scalable splitting of massive data streams
DASFAA'10 Proceedings of the 15th international conference on Database Systems for Advanced Applications - Volume Part II
Efficient frequent item counting in multi-core hardware
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Scalable and dynamically balanced shared-everything OLTP with physiological partitioning
The VLDB Journal — The International Journal on Very Large Data Bases
Hi-index | 0.00 |
Many real-world data stream analysis applications such as network monitoring, click stream analysis, and others require combining multiple streams of data arriving from multiple sources. This is referred to as multi-stream analysis. To deal with high stream arrival rates, it is desirable that such systems be capable of supporting very high processing throughput. The advent of multicore processors and powerful servers driven by these processors calls for efficient parallel designs that can effectively utilize the parallelism of the multicores, since performance improvement is possible only through effective parallelism. In this paper, we address the problem of parallelizing multi-stream analysis in the context of multicore processors. Specifically, we concentrate on parallelizing frequent elements, top-k, and frequency counting over multiple streams. We discuss the challenges in designing an efficient parallel system for multi-stream processing. Our evaluation and analysis reveals that traditional "contention" based locking results in excessive overhead and wait, which in turn leads to severe performance degradation in modern multicore architectures. Based on our analysis, we propose a "cooperation" based locking paradigm for efficient parallelization of frequency counting. The proposed "cooperation" based paradigm removes waits associated with synchronization, and allows replacing locks by much cheaper atomic synchronization primitives. Our implementation of the proposed paradigm to parallelize a well known frequency counting algorithm shows the benefits of the proposed "cooperation" based locking paradigm when compared to the traditional "contention" based locking paradigm. In our experiments, the proposed "cooperation" based design outperforms the traditional "contention" based design by a factor of 2--5.5X for synthetic zipfian data sets.