Decomposition Based Algorithm for State Prediction in Large Scale Distributed Systems

Istin, Mihai; Visan, Andreea; Pop, Florin; Cristea, Valentin

doi:10.1109/ispdc.2010.13

Cited by 9 publications

(3 citation statements)

References 6 publications

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“…In order to achieve this goal the monitoring module has to: provide accurate information for all relevant parameters, support various data delivery models, offer extensible data representation, have access to real-time and history data and to be easily ported. The monitoring data is used also by the prediction mechanisms that forecast the next value based on a set of previous values for a certain parameter, and send it to the resource manager [9].…”

Section: Related Workmentioning

confidence: 99%

A Bio-Inspired Prediction Method for Water Quality in a Cyber-Infrastructure Architecture

Pop

Ciolofan

Negru

et al. 2014

2014 Eighth International Conference on Complex, Intelligent and Software Intensive Systems

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The water quality is critical as it sustains life. In order to avoid catastrophic situations a decision support system in the water pollution scenario must offer reliable and on time information. Prediction plays in this case a very important role. The paper presents a biologically inspired method to predict values of a temporal series and how this can be applied to the specific case of water quality monitoring. Historically, the prediction methods evolved from statistical to biologically inspired. The proposed method is based on neural networks and represents the core part of the prediction module of the decision support system we designed. The experimental data was gathered on a major river in Romania and in this paper we exemplify with values for pH and Turbidity.

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Section: Related Workmentioning

confidence: 99%

A Bio-Inspired Prediction Method for Water Quality in a Cyber-Infrastructure Architecture

Pop

Ciolofan

Negru

et al. 2014

2014 Eighth International Conference on Complex, Intelligent and Software Intensive Systems

Self Cite

View full text Add to dashboard Cite

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“…Researchers have looked at the problem of predicting resource utilization in a long-term framework [5,9] by extending existing CS scheduling methods only to discover that predictions were unreliable. Using those traditional methods, samples of resource utilization were gathered and complex algorithms were used to predict the results multiple steps into the future.…”

Section: Experiments Setupmentioning

confidence: 99%

“…Operating systems and distributed systems have had a long history of research into optimizing [8,9,11], and those techniques don't directly translate into long-term resource utilization predictions [5,9]. Without a basis to know if longterm prediction for resource utilization was possible, a new way of looking at the problem was needed.…”

Section: Conclusion and Future Researchmentioning

confidence: 99%

Using time-series analysis to provide long-term CPU utilization prediction

Yoas¹,

Simco

2014

Proceedings of the 3rd Annual Conference on Research in Information Technology

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Time-series analysis has been a recognized method of prediction for years. In computing, prediction has revolved around scheduling problems and is used in computing time to manage resources in the short-term. Long-term prediction using these methods has proven to be problematic and of little use. However, with the rise of Information Technology (IT) and the need to manage resources in a business atmosphere, the need to provide long-term resource management remains a difficult problem without a good solution. At this time, IT professionals use experience and their best judgment to manage equipment and frequently purchase systems based on maximum requirements. The need to take the knowledge gained by computer science in scheduling now needs to be expanded into the realm of IT to facilitate a more economical use of resource availability. This work is just the first step and provides one path to accomplishing long-term prediction in computing.

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TMRCP: A Trend-Matching Resources Coupled Prediction Method over Data Stream

Wang

et al. 2017

Neural Information Processing

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Decomposition Based Algorithm for State Prediction in Large Scale Distributed Systems

Cited by 9 publications

References 6 publications

A Bio-Inspired Prediction Method for Water Quality in a Cyber-Infrastructure Architecture

A Bio-Inspired Prediction Method for Water Quality in a Cyber-Infrastructure Architecture

Using time-series analysis to provide long-term CPU utilization prediction

TMRCP: A Trend-Matching Resources Coupled Prediction Method over Data Stream

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