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Resource Allocation in Cloud

Resource Allocation in Cloud. By Geetha Priya Balasubramanian. Overview. Introduction Challenges Mechanisms for allocation Concept I Concept II Conclusion. Introduction. Cloud computing Complex system Shared resources Why resource management is important?. Challenges.

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Resource Allocation in Cloud

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  1. Resource Allocation in Cloud By GeethaPriyaBalasubramanian

  2. Overview • Introduction • Challenges • Mechanisms for allocation • Concept I • Concept II • Conclusion

  3. Introduction • Cloud computing • Complex system • Shared resources • Why resource management is important?

  4. Challenges • Lean allocation of resources • Unpredictable requests • Shared resources • Resource usage – time variant • Dynamic availability of resources

  5. Mechanisms for allocation Allocation techniques • Control theory • Feedback mechanism to guarantee system stability, predict transient behavior • Machine learning • No performance model • Utility-based • Performance model to correlate user-level performance with cost

  6. YagızOnatYazır, Chris Matthews, RoozbehFarahbod Stephen Neville, Adel Guitouni, SudhakarGanti and Yvonne Coady. Dynamic Resource Allocation in Computing Clouds through Distributed Multiple Criteria DecisionAnalysis

  7. Concept • Centralized global arbiter • Two level architecture • Application agents • mapping between performance level and resource level requirements per application environment • Node agents • Configuration changes in the resource requirements • Local re-distribution of the resources • Moving suitable components to other computational units

  8. Node program and Task life cycle PROMETHEE Method

  9. Results and conclusion

  10. Xavier Dutreilh, Nicolas Rivierre, Aur´elien Moreau, Jacques Malenfant and Isis Truck From Data Center Resource Allocation to Control Theory and Back

  11. Concept • Resource allocation and policies • threshold-based policies, where upper and lower bounds on the performance trigger adaptations, where some amount of resources are allocated or deallocated • sequential decision policies based on Markovian decision processes (MDP) models and computed using, for example, reinforcement learning.

  12. Experiment

  13. Observations Resource allocation as automatic control • Measure patterns of evolution of the performance against time since the start of the adaptation action • Adaptations at a faster rhythm than the time required to stabilize the performance of the system leads to instability • Analyze the workload patterns of variation to so that • New stabilization performance has small cope up time • Maximum performance with adaption actions

  14. Observations Finding good resource allocation policies 1) Adaptation actions, measure the new stabilized performance after crossing lower threshold, and make sure the upper (resp. lower) threshold is strictly less than (resp. greater than) this measure. 2) From the maximal time ts to stabilize the performance after any adaptation action, compute the difference between the two thresholds so that the time to pass from one threshold to another is larger than t s even for the maximal slope in the workload variation.

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