Using predictions in online optimization: Looking forward with an eye on the past

SIGMETRICS/ Performance 2016 - Proceedings of the SIGMETRICS/Performance Joint International Conference on Measurement and Modeling of Computer Science

Volumn , Issue , 2016, Pages 193-206

  Chen, Niangjun ^a   Comden, Joshua ^b   Liu, Zhenhua ^b   Gandhi, Anshul ^b   Wierman, Adam ^a  

^a CALIFORNIA INSTITUTE OF TECHNOLOGY   (United States)

^b STONY BROOK UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CONVEX OPTIMIZATION; FORECASTING;

AVERAGE-CASE ANALYSIS; CORRELATION STRUCTURE; DESIGN OF ALGORITHMS; ON-LINE ALGORITHMS; ONLINE CONVEX OPTIMIZATIONS; ONLINE OPTIMIZATION; RECEDING HORIZON CONTROL; WORST-CASE PERFORMANCE;

OPTIMIZATION;

EID: 84978711389     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2896377.2901464     Document Type: Conference Paper

References (54)

1. M. A. Adnan, R. Sugihara, and R. K. Gupta. Energy efficient geographical load balancing via dynamic deferral of workload. In IEEE Int. Conf. Cloud Computing (CLOUD), pages 188-195, 2012.
2. Hölder condition. https://en.wikipedia.org/wiki/Holdercondition.
3. G. Ananthanarayanan, A. Ghodsi, S. Shenker, and I. Stoica. Effective straggler mitigation: Attack of the clones. In Proc. NSDI, volume 13, pages 185-198, 2013.
4. L. Andrew, S. Barman, K. Ligett, M. Lin, A. Meyerson, A. Roytman, and A. Wierman. A tale of two metrics: Simultaneous bounds on competitiveness and regret. In Conf. on Learning Theory (COLT), pages 741-763, 2013.
5. M. Asawa and D. Teneketzis. Multi-armed bandits with switching penalties. IEEE Transactions on Automatic Control, 41(3):328-348, 1996.
6. N. Bansal, A. Gupta, R. Krishnaswamy, K. Pruhs, K. Schewior, and C. Stein. A 2-competitive algorithm for online convex optimization with switching costs. In LIPIcs-Leibniz International Proceedings in Informatics, volume 40. Schloss Dagstuhl-Leibniz-Zentrum fuer Informatik, 2015.
7. A. Bartolini, M. Cacciari, A. Tilli, and L. Benini. A distributed and self-calibrating model-predictive controller for energy and thermal management of high-performance multi cores. In Design, Automation & Test in Europe Conference & Exhibition (DATE), 2011, pages 1{6. IEEE, 2011.
8. A. Bemporad and M. Morari. Robust model predictive control: A survey. In Robustness in identification and control, pages 207-226. Springer, 1999.
9. A. Blum, H. Karloff, Y. Rabani, and M. Saks. A decomposition theorem and bounds for randomized server problems. In Proc. Symp. Found. Comp. Sci. (FOCS), pages 197-207, Oct 1992.
10. A. Borodin, N. Linial, and M. E. Saks. An optimal on-line algorithm for metrical task system. J. ACM, 39(4):745-763, 1992.
11. S. Boucheron, G. Lugosi, and O. Bousquet. Concentration inequalities. In Advanced Lectures on Machine Learning, pages 208-240. Springer, 2004.
12. E. F. Camacho and C. B. Alba. Model predictive control. Springer, 2013.
13. N. Chen, A. Agarwal, A. Wierman, S. Barman, and L. L. H. Andrew. Online convex optimization using predictions. In Proc. ACM SIGMETRICS, pages 191-204. ACM, 2015.
14. N. Chen, L. Gan, S. H. Low, and A. Wierman. Distributional analysis for model predictive deferrable load control. In Decision and Control (CDC), 2014 IEEE 53rd Annual Conference on, pages 6433-6438. IEEE, 2014.
15. L. Gan, A. Wierman, U. Topcu, N. Chen, and S. H. Low. Real-time deferrable load control: Handling the uncertainties of renewable generation. SIGMETRICS Perform. Eval. Rev., 41(3):77-79, Jan. 2014.
16. A. Gandhi, Y. Chen, D. Gmach, M. Arlitt, and M. Marwah. Minimizing Data Center SLA Violations and Power Consumption via Hybrid Resource Provisioning. In Proceedings of the 2011 International Green Computing Conference, pages 49-56, Orlando, FL, USA, 2011.
17. A. Gandhi, M. Harchol-Balter, and I. Adan. Server farms with setup costs. Performance Evaluation, 67:1123-1138, 2010.
18. C. E. Garcia, D. M. Prett, and M. Morari. Model predictive control: Theory and practice a survey. Automatica, 25(3):335-348, 1989.
19. D. Gmach, J. Rolia, L. Cherkasova, and A. Kemper. Workload analysis and demand prediction of enterprise data center applications. In Proc. IEEE Int. Symp. Workload Characterization, IISWC '07, pages 171-180. IEEE Computer Society, 2007.
20. S. Guha and K. Munagala. Multi-armed Bandits with Metric Switching Costs. In Automata, Languages and Programming, volume 5556 of Lecture Notes in Computer Science, pages 496-507. Springer Berlin Heidelberg, 2009.
21. E. Hazan, A. Agarwal, and S. Kale. Logarithmic regret algorithms for online convex optimization. Machine Learning, 69(2-3):169-192, 2007.
22. E. Hazan, A. Agarwal, and S. Kale. Logarithmic regret algorithms for online convex optimization. Machine Learning, 69(2-3):169-192, 2007.
23. Innovation process. https://en.wikipedia.org/wiki/Innovation (signal processing).
24. V. Joseph and G. de Veciana. Variability aware network utility maximization. arXiv preprint arXiv:1111.3728, 2011.
25. V. Joseph and G. de Veciana. Jointly optimizing multi-user rate adaptation for video transport over wireless systems: Mean-fairness-variability tradeoffs. In Proc. IEEE INFOCOM, pages 567-575, 2012.
26. T. Kailath, A. H. Sayed, and B. Hassibi. Linear estimation. Prentice-Hall, Inc., 2000.
27. A. Kalai and S. Vempala. Efficient algorithms for online decision problems. Journal of Computer and System Sciences, 71(3):291-307, 2005.
28. R. E. Kalman. A new approach to linear filtering and prediction problems. J. Fluids Engineering, 82(1):35-45, 1960.
29. S.-J. Kim and G. B. Giannakis. Real-time electricity pricing for demand response using online convex optimization. In IEEE Innovative Smart Grid Tech. Conf., pages 1-5, 2014.
30. A. Krioukov, P. Mohan, S. Alspaugh, L. Keys, D. Culler, and R. Katz. Nap SAC: Design and implementation of a power-proportional web cluster. In Proceedings of the 1st ACM SIGCOMM Workshop on Green Networking, Green Networking '10, pages 15-22, New Delhi, India, 2010.
31. D. Kusic, J. O. Kephart, J. E. Hanson, N. Kandasamy, and G. Jiang. Power and performance management of virtualized computing environments via look ahead control. Cluster computing, 12(1):1-15, 2009.
32. W. Kwon and A. Pearson. A modified quadratic cost problem and feedback stabilization of a linear system. IEEE Transactions on Automatic Control, 22(5):838-842, 1977.
33. M. Lin, Z. Liu, A. Wierman, and L. L. Andrew. Online algorithms for geographical load balancing. In Int. Green Computing Conference (IGCC), pages 1-10. IEEE, 2012.
34. M. Lin, A. Wierman, L. Andrew, and E. Thereska. Dynamic right-sizing for power-proportional data centers. IEEE/ACM Trans. Networking, 21(5):1378-1391, Oct 2013.
35. Z. Liu, I. Liu, S. Low, and A. Wierman. Pricing data center demand response. In Proc. ACM Sigmetrics, 2014.
36. Z. Liu, A. Wierman, Y. Chen, B. Razon, and N. Chen. Data center demand response: Avoiding the coincident peak via workload shifting and local generation. Performance Evaluation, 70(10):770-791, 2013.
37. L. Lu, J. Tu, C.-K. Chau, M. Chen, and X. Lin. Online energy generation scheduling for micro grids with intermittent energy sources and co-generation. In Proc. ACM SIGMETRICS, pages 53-66. ACM, 2013.
38. D. Q. Mayne, J. B. Rawlings, C. V. Rao, and P. O. Scokaert. Constrained model predictive control: Stability and optimality. Automatica, 36(6):789-814, 2000.
39. C. McDiarmid. On the method of bounded differences. Surveys in combinatorics, 141(1):148-188, 1989.
40. B. Narayanaswamy, V. K. Garg, and T. Jayram. Online optimization for the smart (micro) grid. In Proc. ACM Int. Conf. on Future Energy Systems, page 19, 2012.
41. D. Niu, H. Xu, B. Li, and S. Zhao. Quality-assured cloud bandwidth auto-scaling for video-on-demand applications. In Proc. IEEE INFOCOM, pages 460-468, March 2012.
42. C. Rhemann, A. Hosni, M. Bleyer, C. Rother, and M. Gelautz. Fast Cost-volume Filtering for Visual Correspondence and Beyond. In Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition, CVPR '11, pages 3017-3024, 2011.
43. A. P. Sage and J. L. Melsa. Estimation theory with applications to communications and control. Technical report, DTIC Document, 1971.
44. S. Sastry and M. Bodson. Adaptive control: stability, convergence and robustness. Courier Dover Publications, 2011.
45. J. Schulman, Y. Duan, J. Ho, A. Lee, I. Awwal, H. Bradlow, J. Pan, S. Patil, K. Goldberg, and P. Abbeel. Motion planning with sequential convex optimization and convex collision checking. The International Journal of Robotics Research, 33(9):1251-1270, 2014.
46. S. Shalev-Shwartz. Online learning and online convex optimization. Foundations and Trends in Machine Learning, 4(2):107-194, 2011.
47. H. Wang, J. Huang, X. Lin, and H. Mohsenian-Rad. Exploring smart grid and data center interactions for electric power load balancing. ACM SIGMETRICS Performance Evaluation Review, 41(3):89-94, 2014.
48. X. Wang and M. Chen. Cluster-level feedback power control for performance optimization. In High Performance Computer Architecture, pages 101-110. IEEE, 2008.
49. N. Wiener. Extrapolation, interpolation, and smoothing of stationary time series, volume 2. MIT press, Cambridge, MA, 1949.
50. L. Xiao. Dual averaging methods for regularized stochastic learning and online optimization. J. Machine Learning Research, 9999:2543-2596, 2010.
51. F. Zanini, D. Atienza, L. Benini, and G. De Micheli. Multicore thermal management with model predictive control. In Proc. IEEE. European Conf. Circuit Theory and Design (ECCTD), pages 711-714, 2009.
52. F. Zanini, D. Atienza, G. De Micheli, and S. P. Boyd. Online convex optimization-based algorithm for thermal management of MPSoCs. In Proc. ACM Great Lakes Symp. VLSI, pages 203-208, 2010.
53. X. Y. Zhou and D. Li. Continuous-time mean-variance portfolio selection: A stochastic LQ framework. Applied Mathematics & Optimization, 42(1):19-33, 2000.
54. M. Zinkevich. Online convex programming and generalized infinitesimal gradient ascent. In Proc. Int. Conf. Machine Learning (ICML), pages 928-936. AAAI Press, 2003.