The art of computer programming, volume 2 (3rd ed.): seminumerical algorithms
The art of computer programming, volume 2 (3rd ed.): seminumerical algorithms
Mining high-speed data streams
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Learning Changing Concepts by Exploiting the Structure of Change
Machine Learning
Clustering Data Streams: Theory and Practice
IEEE Transactions on Knowledge and Data Engineering
BOINC: A System for Public-Resource Computing and Storage
GRID '04 Proceedings of the 5th IEEE/ACM International Workshop on Grid Computing
Concurrency and Computation: Practice & Experience - Third IEEE International Workshop on High Performance Computational Biology (HiCOMB 2004)
Anytime Classification Using the Nearest Neighbor Algorithm with Applications to Stream Mining
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Autonomic runtime manager for adaptive distributed applications
HPDC '05 Proceedings of the High Performance Distributed Computing, 2005. HPDC-14. Proceedings. 14th IEEE International Symposium
Simulation and the Monte Carlo Method (Wiley Series in Probability and Statistics)
Simulation and the Monte Carlo Method (Wiley Series in Probability and Statistics)
DELAY: A Lazy Approach for Mining Frequent Patterns over High Speed Data Streams
ADMA '07 Proceedings of the 3rd international conference on Advanced Data Mining and Applications
Models and Issues on Probabilistic Data Streams with Bayesian Networks
SAINT '08 Proceedings of the 2008 International Symposium on Applications and the Internet
Modeling Job Lifespan Delays in Volunteer Computing Projects
CCGRID '09 Proceedings of the 2009 9th IEEE/ACM International Symposium on Cluster Computing and the Grid
Autonomic Computing Paradigm to Support System's Development
DESE '09 Proceedings of the 2009 Second International Conference on Developments in eSystems Engineering
Simultaneous performance exploration and optimized search with volunteer computing
Proceedings of the 19th ACM International Symposium on High Performance Distributed Computing
Stock Price Index Prediction Based on Mobile Data Mining
ICEE '10 Proceedings of the 2010 International Conference on E-Business and E-Government
Study on Application of Bayesian Classifier Model in Data Stream
ICCIS '10 Proceedings of the 2010 International Conference on Computational and Information Sciences
Enabling Fast Lazy Learning for Data Streams
ICDM '11 Proceedings of the 2011 IEEE 11th International Conference on Data Mining
Incremental Learning From Stream Data
IEEE Transactions on Neural Networks - Part 1
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An important challenge faced by high-throughput, multiscale applications is that human intervention has a central role in driving their success. However, manual intervention is in-efficient, error-prone and promotes resource wasting. This paper presents an application-aware modular framework that provides self-management for computational multiscale applications in volunteer computing (VC). Our framework consists of a learning engine and three modules that can be easily adapted to different distributed systems. The learning engine of this framework is based on our novel tree-like structure called KOTree. KOTree is a fully automatic method that organizes statistical information in a multidimensional structure that can be efficiently searched and updated at runtime. Our empirical evaluation shows that our framework can effectively provide application-aware self-management in VC systems. Additionally, we observed that our KOTree algorithm is able to predict accurately the expected length of new jobs, resulting in an average of 85% increased throughput with respect to other algorithms.