Journal ArticleDOI
Self-adaptive workload classification and forecasting for proactive resource provisioning
TLDR
This paper proposes a novel self‐adaptive approach that selects suitable forecasting methods for a given context based on a decision tree and direct feedback cycles together with a corresponding implementation and shows that the implementation of this approach provides continuous and reliable forecast results at run‐time.Abstract:
As modern enterprise software systems become increasingly dynamic, workload forecasting techniques are gaining an importance as a foundation for online capacity planning and resource management. Time series analysis offers a broad spectrum of methods to calculate workload forecasts based on history monitoring data. Related work in the field of workload forecasting mostly concentrates on evaluating specific methods and their individual optimisation potential or on predicting QoS metrics directly. As a basis, we present a survey on established forecasting methods of the time series analysis concerning their benefits and drawbacks and group them according to their computational overheads. In this paper, we propose a novel self-adaptive approach that selects suitable forecasting methods for a given context based on a decision tree and direct feedback cycles together with a corresponding implementation. The user needs to provide only his general forecasting objectives. In several experiments and case studies based on real-world workload traces, we show that our implementation of the approach provides continuous and reliable forecast results at run-time. The results of this extensive evaluation show that the relative error of the individual forecast points is significantly reduced compared with statically applied forecasting methods, for example, in an exemplary scenario on average by 37%. In a case study, between 55 and 75% of the violations of a given service level objective can be prevented by applying proactive resource provisioning based on the forecast results of our implementation. Copyright © 2014 John Wiley & Sons, Ltd.read more
Citations
More filters
Proceedings Article
AGILE: elastic distributed resource scaling for Infrastructure-as-a-Service
TL;DR: AGILE uses wavelets to provide a medium-term resource demand prediction with enough lead time to start up new application server instances before performance falls short, and it uses dynamic VM cloning to reduce application startup times.
Journal ArticleDOI
Auto-Scaling Web Applications in Clouds: A Taxonomy and Survey
TL;DR: A taxonomy of auto-scalers according to the identified challenges and key properties is presented and new future directions that can be explored in this area are proposed.
Journal ArticleDOI
Q-aware
Sukhpal Singh,Inderveer Chana +1 more
TL;DR: The experimental results demonstrate that QoS metric based resource provisioning technique is efficient in reducing execution time and execution cost of cloud workloads along with other QoS parameters.
Journal ArticleDOI
A reliable and cost-efficient auto-scaling system for web applications using heterogeneous spot instances
TL;DR: The proposed fault-tolerant model for web applications provisioned by spot instances can greatly reduce resource cost and still achieve satisfactory Quality of Service (QoS) in terms of response time and availability.
Journal ArticleDOI
Auto-scaling web applications in clouds: A cost-aware approach
TL;DR: A cost saving super professional executor is provided which reduces the cost of renting virtual machines by 7% while improves the final service level agreement of the application provider and controls the mechanism's oscillation in decision-making.
References
More filters
Journal Article
R: A language and environment for statistical computing.
TL;DR: Copyright (©) 1999–2012 R Foundation for Statistical Computing; permission is granted to make and distribute verbatim copies of this manual provided the copyright notice and permission notice are preserved on all copies.
Journal ArticleDOI
Time series analysis, forecasting and control
P. Young,S. Shellswell +1 more
TL;DR: Time series analysis san francisco state university, 6 4 introduction to time series analysis, box and jenkins time seriesAnalysis forecasting and, th15 weeks citation classic eugene garfield, proc arima references 9 3 sas support, time series Analysis forecasting and control pambudi, timeseries analysis forecasting and Control george e.
Journal ArticleDOI
The vision of autonomic computing
TL;DR: A 2001 IBM manifesto noted the almost impossible difficulty of managing current and planned computing systems, which require integrating several heterogeneous environments into corporate-wide computing systems that extend into the Internet.
Journal ArticleDOI
Another look at measures of forecast accuracy
Rob J. Hyndman,Anne B. Koehler +1 more
TL;DR: In this paper, the mean absolute scaled error (MESEME) was proposed as the standard measure for comparing forecast accuracy across multiple time series across different time series types, and was used in the M-competition as well as the M3competition.
Journal ArticleDOI
Automatic Time Series Forecasting: The forecast Package for R
Rob J. Hyndman,Yeasmin Khandakar +1 more
TL;DR: Two automatic forecasting algorithms that have been implemented in the forecast package for R, based on innovations state space models that underly exponential smoothing methods, are described.