Location-aware load prediction in Edge Data Centers

2017 2nd International Conference on Fog and Mobile Edge Computing, FMEC 2017

Volumn , Issue , 2017, Pages 25-31

  Le Tan, Chanh Nguyen ^a   Klein, Cristian ^a   Elmroth, Erik ^a  

^a UMEÅ UNIVERSITY   (Sweden)

Author keywords

Mobile Edge Cloud; Proactive Resource Management; User Mobility; VAR Model; Workload Prediction

Indexed keywords

BASE STATIONS; DECISION MAKING; DIGITAL STORAGE; DISTRIBUTED COMPUTER SYSTEMS; FORECASTING; REGRESSION ANALYSIS; TIME SERIES; VALUE ENGINEERING;

EDGE CLOUDS; RESOURCE MANAGEMENT; USER MOBILITY; VAR MODELS; WORKLOAD PREDICTIONS;

QUALITY OF SERVICE;

EID: 85028528661     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/FMEC.2017.7946403     Document Type: Conference Paper

References (18)

1. F. Bonomi, R. Milito, J. Zhu, and S. Addepalli, "Fog computing and its role in the internet of things, " in Proceedings of the first edition of the MCC workshop on Mobile cloud computing. ACM, 2012, pp. 13-16.
2. Nokia, "Multi-access Edge Computing(MEC), " https://networks.nokia.com/solutions/multi-access-edge-computing, 2016.
3. H. Lütkepohl, Vector autoregressive models. Springer, 2011.
4. J. R. Hosking, "The multivariate portmanteau statistic, " Journal of the American Statistical Association, vol. 75, no. 371, pp. 602-608, 1980.
5. B. Pfaff et al., "Var, svar and svec models: Implementation within r package vars, " Journal of Statistical Software, vol. 27, no. 4, pp. 1-32, 2008.
6. M. Piorkowski, N. Sarafijanovic-Djukic, and M. Grossglauser, "CRAWDAD dataset epfl/mobility (v. 2009-02-24), " Downloaded from http://crawdad.org/epfl/mobility/20090224, Feb. 2009.
7. M. Piorkowski, N. Sarafijanovic-Djukic, and M. Grossglauser, "A parsimonious model of mobile partitioned networks with clustering, " in 2009 First International Communication Systems and Networks and Workshops, Jan 2009, pp. 1-10.
8. V. G. Tran, V. Debusschere, and S. Bacha, "Hourly server workload forecasting up to 168 hours ahead using seasonal arima model, " in 2012 IEEE International Conference on Industrial Technology, March 2012, pp. 1127-1131.
9. W. Fang, Z. Lu, J. Wu, and Z. Cao, "Rpps: A novel resource prediction and provisioning scheme in cloud data center, " in 2012 IEEE Ninth International Conference on Services Computing, June 2012, pp. 609-616.
10. G. E. Box, G. M. Jenkins, G. C. Reinsel, and G. M. Ljung, Time series analysis: forecasting and control. John Wiley & Sons, 2015.
11. R. J. Hyndman, Y. Khandakar et al., "Automatic time series for forecasting: the forecast package for, " Tech. Rep., 2007.
12. H. Zhang, G. Jiang, K. Yoshihira, H. Chen, and A. Saxena, "Intelligent workload factoring for a hybrid cloud computing model, " in 2009 Congress on Services - I, July 2009, pp. 701-708.
13. N. Roy, A. Dubey, and A. Gokhale, "Efficient autoscaling in the cloud using predictive models for workload forecasting, " in 2011 IEEE 4th International Conference on Cloud Computing, July 2011, pp. 500-507.
14. E. Caron, F. Desprez, and A. Muresan, "Forecasting for grid and cloud computing on-demand resources based on pattern matching, " in 2010 IEEE Second International Conference on Cloud Computing Technology and Science, Nov 2010, pp. 456-463.
15. M. Sladescu, A. Fekete, K. Lee, and A. Liu, "Event aware workload prediction: A study using auction events, " in International Conference on Web Information Systems Engineering. Springer, 2012, pp. 368-381.
16. S. Islam, J. Keung, K. Lee, and A. Liu, "Empirical prediction models for adaptive resource provisioning in the cloud, " Future Generation Computer Systems, vol. 28, no. 1, pp. 155-162, 2012.
17. C. A. Sims and T. Zha, "Bayesian methods for dynamic multivariate models, " International Economic Review, pp. 949-968, 1998.
18. S. R. Chandra and H. Al-Deek, "Predictions of freeway traffic speeds and volumes using vector autoregressive models, " Journal of Intelligent Transportation Systems, vol. 13, no. 2, pp. 53-72, 2009.