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From Static Scheduling Towards Understanding Uncertainty Andrei Tchernykh CICESE Research Center , Ensenada, Baja California, Mxico chernykh@cicese.mx http://usuario.cicese.mx/~chernykh/ Algorithms and Scheduling Techniques to Manage


  1. From Static Scheduling Towards Understanding Uncertainty Andrei Tchernykh CICESE Research Center , Ensenada, Baja California, México chernykh@cicese.mx http://usuario.cicese.mx/~chernykh/ Algorithms and Scheduling Techniques to Manage Resilience and Power Consumption in Distributed Systems Dagstuhl – July 7, 2015

  2. Baja California, México

  3. Ensenada, Baja California, México

  4. Sonora Yaqui deer dancer

  5. Research Areas HPC Real Time Systems Grid Computing Resource optimization Scheduling Multiobjective online offline Optimization List Scheduling Stealing Computational Scheduling with Intelligence Service Levels Approximation Knowledge Free Algorithms Scheduling with Workflow Scheduling Uncertainty Cloud Computing

  6. Collaboration Universidad Autónoma de Baja California Germany Universidad Autónoma de Nuevo León Mexico Tecnológico de Monterrey Instituto Tecnológico de Morelia Dortmund University Centro de Estudios Superiores del Estado Prof. Uwe Schwiegelshohn de Sonora University of Göttingen USA Prof. Ramin Yahyapour University of Notre Dame Luxembourg Dr. Jarek Nabrzyski University of California – Irvine, CA, USA University of Luxembourg Prof. Isaac Scherson, Prof. Pascal Bouvry Prof. Jean Luc Gaudiot Dr. Dzmitry Kliazovich Uruguay Universidad de la República Russia Dr. Sergio Nesmachnow Institute for System France Spain Programming , RAS Prof. Arutyun Avetisyan BSC Prof. Nikolay Kuzurin Prof. Vassil Alexandrov Institute of Informatics and Moscow Institute of Applied Mathematics of Grenoble Physics and Technology Prof. Denis Trystram Prof. Alexander Drozdov INRIA Lille - Nord Europe Prof. El-ghazali Talbi

  7. Team CICESE Parallel Computing Laboratory 8

  8. Towards Understanding Uncertainty in Cloud Computing Resource Provisioning Andrei Tchernykh CICESE Research Center, Mexico Uwe Schwiegelshohn University of Dortmund, Germany El-ghazali Talbi University of Lille, France Vassil Alexandrov Barcelona Supercomputing Centre, Spain ICCS-SPU 2015. Procedia Computer Science, Elsevier, 2015 CICESE Parallel Computing Laboratory 9

  9. Uncertainty Can be classified in several different ways according to their nature: 1. Long-term uncertainty is due to the object is poorly understood and inadvertent factors can influence its behavior. 2. Retrospective uncertainty is due to the lack of information about the behavior of the object in the past. 3. Technical uncertainty is a consequence of the impossibility of predicting the exact results of decisions 4. Stochastic uncertainty is a result of probabilistic (stochastic) nature of the studied processes and phenomena. • there is a reliable statistical information; • statistical information is not available; • hypothesis on the stochastic nature requires verification. Tychinsky 2006 CICESE Parallel Computing Laboratory 10

  10. Uncertainty 5. Constraint uncertainty - partial or complete ignorance of the conditions. 6. Participant uncertainty - conflict of main stakeholders: cloud providers, users and administrators. • own preferences, incomplete, inaccurate information about the motives and behavior of opposing parties. 7. Goal uncertainty • inability to select one goal • conflicts in building multi objective optimization model. • competing interests 8. Condition uncertainty occurs when a failure or a complete lack of information about the conditions under which decisions are made. CICESE Parallel Computing Laboratory 11

  11. Uncertainty 9. Action uncertainty occurs when there is no ambiguity when choosing solutions. • Single objective case o determine the best solution among all feasible ones; • In multiple objective case, o there exists a (possibly infinite) number of Pareto optimal solutions. o There is the problem of finding a good element of this set . CICESE Parallel Computing Laboratory 12

  12. Uncertainty Can be grouped into: parameter (parametric) uncertainties 1. arise from the incomplete knowledge and variation of the parameters 2. estimated using statistical techniques system uncertainties. 1. arise from an incomplete understanding of the processes that control service provisioning 2. incomplete information about a system CICESE Parallel Computing Laboratory 13

  13. Uncertainty in Clouds Services and resources are subject to considerable uncertainty during provisioning. Uncertainty brings additional challenges to • End-users • Resource providers • Brokering It requires • waiving habitual computing paradigms • adapting current computing models • designing novel resource management strategies to handle uncertainty in an effective way The question is: How to deliver scalable and robust cloud behavior under uncertainties and specific constraints, such as budgets, QoS, SLA, energy costs; etc. CICESE Parallel Computing Laboratory 14

  14. Sources of uncertainty • dynamic elasticity • dynamic performance changing • virtualization, loosely coupling application to the infrastructure • resource provisioning time variation • inaccuracy of application runtimes, variation of processing times • variation in data transmission, variable data streams, • release time and workload uncertainty • effective bandwidth variation, and other phenomenon. • workload is not predictable and can be changed dramatically • performance can be changed due to sharing of common resources with other VM CICESE Parallel Computing Laboratory 15

  15. Sources of uncertainty Providers might not know the • Quantity of transmitted data • Amount of computation Example: Every time when a user requires a status of his e-mail or bank account, it could generate • different amount of data and • take different time for delivering. CICESE Parallel Computing Laboratory 16

  16. Sources of uncertainty It is impossible to get exact knowledge about the system. Parameters such as • effective processor speed, • number of available processors, • actual bandwidth are changing over the time. Topology is unknown In general, an execution environment will differ for each program/service invocation . CICESE Parallel Computing Laboratory 17

  17. Source of uncertainty Sources of uncertainty Resource provisioning Cost (dynamic pricing) Data (volume, variety, Resource availability Energy minimization Cloud infrastructure Communication Fault tolerance Consolidation Virtualization Jobs arrival Replication Scalability Migration Elasticity value) time ● ● ● ● ● ● ● ● ● ● ● ● Effective performance Cloud computing ● ● ● ● ● ● ● ● ● ● ● ● Effective bandwidth ● ● ● ● ● ● ● ● ● ● ● ● Processing time parameters ● ● ● ● ● ● ● ● ● ● ● ● Available memory ● ● ● ● ● ● ● ● ● ● Number of processors ● ● ● ● ● ● ● ● ● ● Available storage ● ● ● ● ● ● Data transfer time ● ● ● ● ● ● ● ● Resource capacity ● ● ● ● ● ● ● Network capacity CICESE Parallel Computing Laboratory 18

  18. Approaches To treat uncertainly and dynamism we need sophisticated solutions. • Fuzzy, • Robust, • Non-clairvoyant • Knowledge-free • Stochastic • Randomized algorithms • Dynamic priority • Adaptive strategies (reactive) • Dynamic load balancing CICESE Parallel Computing Laboratory 19

  19. Preliminary results

  20. Scheduling for Cloud Computing with Different Service Levels Uwe Schwiegelshohn University of Dortmund, Germany Andrei Tchernykh CICESE Research Center, Mexico IPDPS 2012 , IEEE 26th International Parallel and Distributed Processing Symposium

  21. Quality of Service  Response time in relation to the requested processing time Deadline Service Level (slack factor) Execution time price per time unit Profit CICESE Parallel Computing Laboratory 22

  22. Competitive Factor Obtained Income Competitive Optimal income Factor CICESE Parallel Computing Laboratory 23

  23. Competitive Factor 𝒒 𝒏𝒋𝒐 𝟐 SSL-SM 𝝇 ≤ 𝟐 − (𝟐 − 𝒒 𝒏𝒃𝒚 ) Das Gupta and Palis, 2001 𝒈 𝒈 𝝇 ≤ 𝟐 + 𝒈(𝟐 − 𝒒 𝒏𝒋𝒐 Schwiegelshohn,Tchernykh 2012 SSL-MM 𝒒 𝒏𝒃𝒚 ) CICESE Parallel Computing Laboratory 24

  24. Competitive Factor 𝒒 𝒏𝒋𝒐 𝒈 𝑱 − 𝟐 + 𝒒 𝒏𝒋𝒐 𝒒 𝒏𝒃𝒚 𝒒 𝒏𝒃𝒚 𝝇 ≤ 𝒏𝒃𝒚{ 𝒈 𝑱 − 𝟐 , MSL-SM 𝒈 𝑱 − 𝟐 + 𝒗 𝑱 𝒗 𝑱𝑱 𝝇 ≤ 𝒗 𝑱𝑱 (𝟐 − 𝟐 MSL-MM ) 𝒗 𝑱 𝒈 𝑱 Schwiegelshohn,Tchernykh 2012 CICESE Parallel Computing Laboratory 25

  25. On-line Scheduling in Distributed Systems Multiple strip packing Job Stealing non-clairvoyant Uwe Schwiegelshohn University of Dortmund, Germany Andrei Tchernykh CICESE Research Center, Mexico Ramin Yahyapour University of Göttingen, Germany IEEE IPDPS 200ß

  26. Grid Scheduling Algorithm Any machine applies a priority order when selecting jobs for execution: Jobs of its group A Jobs of its group B Jobs that are enabled for execution on its previous machine. CICESE Parallel Computing Laboratory 27

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