References
- Q. Zhang et al., Dynamic heterogeneity-aware resource provisioning in the cloud, IEEE Trans. Cloud Comput. 2 (1), 14 (2014).
- J. L. Hellerstein, and Q. Wang, Research challenges in control engineering of computing systems, IEEE Trans. Netw. Serv. Manage. 6 (4), 206 (2009).
- D. Xie, Y. C. Hu, and R. R. Kompella, On the performance projectability of Mapreduce, 4th International Conference on Cloud Computing Technology and Science, 2014, pp. 301–308.
- H. Lin, X. Ma, and W. Feng, Reliable Mapreduce computing on opportunistic resources, Cluster Computing 15 (2), 145 (2012).
- T. Charalambous et al., Distributed offline load balancing in Mapreduce networks, 52nd IEEE Conference on Decision and Control, Florence, Italy, 2013, pp. 835–840.
- A. Verma, L. Cherkasova, and R. H. Campbell, Resource provisioning framework for mapreduce jobs with performance goals, Middleware 2011: ACM/IFIP/USENIX 12th International Middleware Conference, Lisbon, Portugal, December 12–16, 2011. Proceedings 12, pp. 165–186. (Springer Berlin Heidelberg, 2011.)
- M. Zaharia et al., Improving Mapreduce performance in heterogeneous environments, Proceedings of the 8th USENIX Conference on Operating systems design and implementation, Berkeley, CA, 2008, pp. 29–42.
- S. Babu, Towards automatic optimization of mapreduce programs, Proceedings of the 1st ACM Symposium on Cloud Computing, New York, NY, 2010, pp. 137–142.
- M. Berekmeri et al., A control approach for performance of big data systems, 19th IFAC World Congress 2014, Cape-Town, South Africa, vol. 19, No. 1, pp. 152–157, 2014.
- L. Xu, Mapreduce framework optimization via performance modeling, 26th International Parallel and Distributed Processing Symposium Workshops & PhD Forum, 2012, pp. 2506–2509.
- Y. Qiao et al., Resource modeling and performance prediction of Mapreduce in cloud computing environment, J. Beijing Univ. Posts and Telecommun. 37, 115 (2014). in Chinese.
- H. Herodotou et al., Starfish: A self-tuning system for big data analytics, Proceedings of the 5th Conference on Innovative Data Systems Research, 2011, pp. 261–272.
- K. Wang, X. Lin, and W. Tang, Predator-an experience guided configuration optimizer for Hadoop Mapreduce, 4th International Conference on Cloud Computing Technology and Science, 2014, pp. 419–426.
- C. Poussot-Vassal, M. Tanelli, and M. Lovera, Linear parametrically varying MPC for combined quality of service and energy management in web service systems, 2010 American Control Conference, Baltimore, MD, 2010, pp. 3106–3111.
- C. Lu et al., Feedback control architecture and design methodology for service delay guarantees in web servers, IEEE Trans. Parallel Distrib. Syst. 17 (9), 1014 (2006).
- L. Malrait, N. Marchand, and S. Bouchenak, Modeling and control of server systems: Application to database systems, Proceedings of the European Control Conference 2009, Budapest, Hungary, 2009, pp. 2960–2965.
- J. L. Hellerstein, Challenges in control engineering of computing systems, Proceeding of the 2004 American Control Conference, Boston, MA, 2004, pp. 1970–1979.
- T. Patikirikorala et al., Hammerstein-Wiener nonlinear model based predictive control for relative QoS performance and resource management of software systems, Control Engin. Practice 20 (1), 49 (2012).
- N. B. Rizvandi et al., On modelling and prediction of total CPU usage for applications in Mapreduce environments, Algorithms and Architectures for Parallel Processing: 12th International Conference, ICA3PP 2012, Fukuoka, Japan, September 4–7, 2012, Proceedings, Part I 12, pp. 414–427. (Springer Berlin Heidelberg, 2012.)
- http://hadoop.apache.org/
- K. J. Aostro¨m, and B. Wittenmark, Computer-Controlled Systems: Theory and Design, 2nd ed. (New Jersey: Prentice Hall, 1990).
- J. L. Hellerstein et al., Feedback Control of Computing Systems (New Jersey: John Wiley& Sons, 2004).