Concurrent programming in ERLANG (2nd ed.)
Concurrent programming in ERLANG (2nd ed.)
The space complexity of approximating the frequency moments
STOC '96 Proceedings of the twenty-eighth annual ACM symposium on Theory of computing
Simple, fast, and practical non-blocking and blocking concurrent queue algorithms
PODC '96 Proceedings of the fifteenth annual ACM symposium on Principles of distributed computing
Models and issues in data stream systems
Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
On the duality of operating system structures
ACM SIGOPS Operating Systems Review
IBM Systems Journal
X10: an object-oriented approach to non-uniform cluster computing
OOPSLA '05 Proceedings of the 20th annual ACM SIGPLAN conference on Object-oriented programming, systems, languages, and applications
Adaptive Control of Extreme-scale Stream Processing Systems
ICDCS '06 Proceedings of the 26th IEEE International Conference on Distributed Computing Systems
A near-optimal algorithm for computing the entropy of a stream
SODA '07 Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms
SPC: a distributed, scalable platform for data mining
Proceedings of the 4th international workshop on Data mining standards, services and platforms
An approach to QoS aware resource scheduling in data stream systems
SSPS '08 Proceedings of the 2nd international workshop on Scalable stream processing system
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Certain streaming applications are required to perform sophisticated analytics within bounded time on arriving streams of data. Such applications have the interesting characteristic that the total amount of work that could be performed is unbounded. We show how recent result from algorithmic theory are useful in scheduling such applications as they allow the efficient creation of synopses of unprocessed data. These synopses can then be used to schedule the processing of the stream. In particular, we describe a scheduler that optimizes the information rate available to applications by estimating the entropy of arriving streams. We describe the theory underlying such a scheduler and show how existing programming models can be extended to accommodate it.