Motivating Application

Transcription

1 Applying to Decision- Making in Autonomic Systems Presented by Andres J. Ramirez Authors: Andres J. Ramirez, David B. Knoester, Betty H.C. Cheng, and Philip K. McKinley 1 Motivating Application Remote data mirroring - prevent data loss & data unavailability. - multiple competing objectives [Keeton06, Wilkes03, Wilkes04]: Reliability Performance Cost 2

2 Motivating Application Consider the following network of remote data mirrors: - what if a link fails? - parts of the network become disconnected - severe financial penalties - network must be reconfigured! 3 Motivating Application How should we reconfigure the network? - must consider tradeoffs... - more than a trillion possible candidates! 4

3 Main Challenge How can we automatically identify target reconfigurations? - searching the entire solution space may not be practical - must consider competing objectives must deal with uncertainty!environment may change at run time 5 Requirements Automatic generation of new reconfigurations - respond to current environmental conditions - considers tradeoffs between functional and non-functional requirements - new solutions produced and applied in real-time 6

4 But How? Darwinian evolution has produced countless examples of self-adaptive behavior. - Survival of the fittest guides evolution. We can harness the power of natural selection to evolve target reconfigurations during execution.! leverage genetic algorithms! 7 Traditional Approaches Related Work - balance multiple objectives [Kephart03,Walsh04]. Evolutionary Approaches - genetic algorithms for dynamic networks [Fabregat05, Lu07, Tseng06,Wang08].!methods not applied at run time. - reconfiguration of a network s topology [Montana02].!expensive forms of evaluating solutions.!results did not support online reconfiguration. 8

5 Evolutionary decision-making approach to evaluate tradeoffs automatically in real-time. Monitor Monitor... Monitor Evolutionary Framework Reconfiguration Monitor Evolve an overlay network to diffuse data across a set of remote mirrors. 9 Start Encode Crossover Individual maps Solution Mutation Evaluation Population Selection Best 50% Terminate End [Yes] [No] Mutations New Individuals Individuals 10

6 Use graph to represent network. -edges represent active/ inactive connections -propagation methods [Keeton04,Keeton06,Wilkes03] Encoding!synchronous: each write must be acknowledged b at destination.!asynchronous: batches of data are sent at periodic intervals. With 25 remote mirrors, d a 7 * 2^300 candidate solutions! Encoding = <ab, = <1, bc, 0, cd, 0, 1, ad, 1, ac, 0> bd> c 11 Crossover Objective: exchange parts of the overall solution -transfer edge properties A A B B D D C C A A B B D D C C Network I Network J Network IJ Network JI Effect: combine key parts of the overall solution to generate fitter solutions. 12

7 Mutation duce variation in the solution space. -randomly activate / deactivate links -randomly reassign a propagation method A B Network IJ' Effect: explore additional areas of the solution space D C 13 Fitness Functions FF =!1*Fc +!2*(Fe1 + Fe2) +!3*(Fr1 + Fr2) Cost Performance Reliability Fc = (100 * cost/budget) latencyavg Fe1 =50 - (50 * ) latencywc Fe2 =50 * ( bandsys - bandeff bandsys linksused Fr1 =50 * ( ) linksmax datalosspot Fr2 =50 * ( ) datalosswc + bound) 14

8 Multidimensional Reconfiguration Original design optimized only for: -cost !1= 1,!2 = 0,!3 = 0 - what if a link fails? Multidimensional Reconfiguration 17 1 Reconfigured design optimizes for: -reliability, performance, cost !1= 1,!2 = 2,!3 = 2 - what if a link fails?

9 Fitness throughout reconfiguration process -initial overlay fails at generation 2500 /+0123-,)(44 Max. Fitness %"" #"" "!#""!%"" 2 -suitable reconfigurations found by generation 3500 < 30 seconds in a laptop!!""" #""" $""" %""" &""" '()(*+,-.) 3-,)( Number of active links in overlay network -initial overlay design fails with single link Number of Links Number of Links failure -reconfigured design increases the number of links in the overlay network Generation Num. of Links 18

10 Potential data loss throughout overlay network -initial overlay design did not consider reliability Potential Avg. Data Loss reconfigured overlay design reduces potential data loss Generation Potential for Data Loss 19 Automatic generation of target reconfigurations -conforms to current environmental conditions. -analysis of tradeoffs and design decisions. -demonstration on real-world application!remote data mirroring.!runs in real time on a laptop. Future Work: -look at cost of reconfiguration. -apply approach to other problem domains. 20

11 Acknowledgements This work has been supported in part by NSF grants CCF , CNS , CCF , CNS , IIP , and CCF , Army Research Office W911NF , Ford Motor Company, and a grant from Michigan State University s Quality Fund. 21 References [Fabregat05] R. Fabregat, Y. Donoso, B. Baran, F. Solano, and J. L. Marzo. Multi-objective optimization scheme for multicast flows: A survey, a model and a MOEA solution. In proceedings of the 3rd International IFIP/ACM Latin American Conference on Networking, pages 73-86, 2005 [Holland92] J. Holland. Adaptation in Natural and Artificial Systems. MIT Press, Cambridge, MA, USA, [Kephart03] J. O. Kephart and D. M. Chess. The Vision of Autonomic Computing. Computer, 36(1):41-50, [Lu07] J. Lu and W. Cheng. A -Algorithm-based Routing Optimization Scheme for Overlay Network. In Proceedings of the 3rd International Conference on Natural Computation, pages , [Montana02] D. Montana, T. Hussain, and T. Saxena. Adaptive Reconfiguration of Data Networks Using. In Proceedings of the and Evolutionary Computation Conference, pages , [Keeton04] K. Keeton, C. Santos, D. Beyer, J. Chase, and J. Wilkes. Designing for Disasters. In the Proceedings of the 3rd USENIX Conference on File and Storage Technologies, pages 59-62, Berkeley, CA, USA, [Keeton06] K. Keeton, D. Beyer, E. Brau, and A. Merchant. On the Road to Recovery: Restoring Data After Disasters. SIGOPS Operating Systems Review, 33(4):4-10, [Tseng06] S. Y. Tseng, Y. M. Huang, and C. C. Lin. Algorithm for Delay and Degree Constrained Multimedia Broadcasting on Overlay Networks. Computer Communications, 29(17): , [Walsh04] W. E. Walsh, G. Tesauro, J. O. Kephart, and R. Das. Utility Functions in Autonomic Systems. In Proceedings of the First IEEE International Conference on Autonomic Computing, pages 70-77, [Wang08] D. Wang, J. Gan, and D. Wang. Heuristic Algorithm for Multicast Overlay Network Link Selection. In Proceedings of the Second International Conference on and Evolutionary Computing, pages 38-41, [Wilkes03] M. Ji, A. Veitch, and J. Wilkes. Seneca: Remote mirroring done write. In USENIX 2003 Annual Technical Conference, pages , Berkeley, CA. 22

12 Questions? Andres J. Ramirez 23 Solution Space Optimal Solutions Acceptable Solutions All Possible Solutions 24

13 Setup First three experiments explore single dimensional concerns. -optimize only for cost, performance, or reliability -validate technique Fourth experiment explores multidimensional dynamic reconfiguration concerns. For every experiment, we conducted 30 trials. -account for stochastic nature. 25