A pattern fusion model for multi-step-ahead CPU load prediction

Authors:
Dingyu Yang;Jian Cao;Jiwen Fu;Jie Wang;Jianmei Guo
Affiliations:
Department of Computer Science and Engineering, Shanghai Jiao Tong University, 800 Dong Chuan Road, Minhang, Shanghai 200240, China;Department of Computer Science and Engineering, Shanghai Jiao Tong University, 800 Dong Chuan Road, Minhang, Shanghai 200240, China;Department of Computer Science and Engineering, Shanghai Jiao Tong University, 800 Dong Chuan Road, Minhang, Shanghai 200240, China;Department of Civil and Environmental Engineering, Stanford University, Stanford, CA 94305, USA;Department of Electrical and Computer Engineering, University of Waterloo, 200 University Avenue West, Waterloo, Ontario, Canada
Venue:
Journal of Systems and Software
Year:
2013

Citing 16
Cited 0

Boosting a weak learning algorithm by majority

Information and Computation
Host load prediction using linear models

Cluster Computing
Homeostatic and Tendency-Based CPU Load Predictions

IPDPS '03 Proceedings of the 17th International Symposium on Parallel and Distributed Processing
Design, Implementation, and Performance of an Extensible Toolkit for Resource Prediction in Distributed Systems

IEEE Transactions on Parallel and Distributed Systems
Adaptive control of virtualized resources in utility computing environments

Proceedings of the 2nd ACM SIGOPS/EuroSys European Conference on Computer Systems 2007
Predict task running time in grid environments based on CPU load predictions

Future Generation Computer Systems
CPU Load Prediction Model for Distributed Computing

ISPDC '09 Proceedings of the 2009 Eighth International Symposium on Parallel and Distributed Computing
A view of cloud computing

Communications of the ACM
Mixture of ANFIS systems for CPU load prediction in metacomputing environment

Future Generation Computer Systems
Service Level Checking in the Cloud Computing Context

CLOUD '10 Proceedings of the 2010 IEEE 3rd International Conference on Cloud Computing
A novel statistical time-series pattern based interval forecasting strategy for activity durations in workflow systems

Journal of Systems and Software
CPU load prediction using neuro-fuzzy and Bayesian inferences

Neurocomputing
Accurate latency estimation in a distributed event processing system

ICDE '11 Proceedings of the 2011 IEEE 27th International Conference on Data Engineering
Energy Time Series Forecasting Based on Pattern Sequence Similarity

IEEE Transactions on Knowledge and Data Engineering
An enhanced load balancing mechanism based on deadline control on GridSim

Future Generation Computer Systems
DENFIS: dynamic evolving neural-fuzzy inference system and its application for time-series prediction

IEEE Transactions on Fuzzy Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

In distributed systems, resource prediction is an important but difficult topic. In many cases, multiple prediction is needed rather than only performing prediction at a single future point in time. However, traditional approaches are not sufficient for multi-step-ahead prediction. We introduce a pattern fusion model to predict multi-step-ahead CPU loads. In this model, similar patterns are first extracted from the historical data via calculating Euclidean distance and fluctuation pattern distance between historical patterns and current sequence. For a given pattern length, multiple similar patterns of this length can often be found and each of them can produce a prediction. We also propose a pattern weight strategy to merge these prediction. Finally, a machine learning algorithm is used to combine the prediction results obtained from different length pattern sets dynamically. Empirical results on four real-world production servers show that this approach achieves higher accuracy on average than existing approaches for multi-step-ahead prediction.