Intelligent cloud computing

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Intelligent cloud computing

  1. 1. IntelligentCloud Computing Julie Kim(kjulee114@gmail.com) 1
  2. 2. Cloud Computing • The use of computing resources (hardware and software) that are delivered as a service over a networkhttp://upload.wikimedia.org/wikipedia/commons/thumb/b/b5/Cloud_computing.svg/400px-Cloud_computing.svg.png 2
  3. 3. “X” As A Service• SaaS – Software A Service – Common delivery model for business• PaaS – Platform As A Service – Providing computing platform and solution stack• IaaS – Infrastructure As A Service – Providing physical or virtual related resources 3
  4. 4. Platform As A Service • Elastic Computing – Scale up/down • Parallel control • Resource management • Load balancing • Failover • ON DEMAND…http://upload.wikimedia.org/wikipedia/commons/3/3c/Cloud_computing_layers.png 4
  5. 5. Elastic computing Scale up or out Which node is busy? Which network is congestion? How to detectHOW TO congestion? Traffic Congestion Problem 5
  6. 6. Traffic Congestion• A condition on road network that occurs as use increases – Slower speeds – Longer trip times – Increased vehicular queuing Longer waiting time Response Delay System Overload Decrease QoS 6
  7. 7. Intelligent Transport System• Applied various area• Wireless communication• Computational technologies – Hardware memory management – Process control• How about apply it to Cloud system..? 7
  8. 8. Keyword: Intelligent• Grid Load Balancing Using Intelligent Agents• Prediction-based Virtual Instance Migration for Balanced Workload in the Cloud Datacenters 8
  9. 9. ACM Future Generation Computer Systems 2005 Pages 135 - 149GRID LOAD BALANCINGUSING INTELLIGENT AGENTS 9
  10. 10. Agent• Managing processor of local resource• Scheduling incoming tasks• Hierarchy of homogeneous agents • Communication Layer • Heterogeneous networks interface • Coordination Layer • How act on the request according to its own knowledge • Local Management Layer • Performs functions for local grid load balancing 10
  11. 11. Performance Prediction• A KEY – Resource scheduling – Load balancing• PACE – A toolset for the performance prediction of parallel and distributes systems• Local/Global load balancing – Resource scheduling 11
  12. 12. Load Balancing• Local – First-come-first-served algorithm – Genetic algorithm • Reorder tasks for optimal execution time• Global – Agent Capability Tables • This ACT/Local ACT/Global ACT – Data-pull/Data-push 12
  13. 13. RITPREDICTION-BASED VIRTUAL INSTANCEMIGRATION FOR BALANCED WORKLOADIN THE CLOUD DATACENTERS 13
  14. 14. Load Balancer • Xen Hypervisor based – Monitoring the loads of the servers – Detecting indications of overloading – Migrating virtual instances • Modeled as.. – A multidimensional knapsack optimization – Bin packinghttp://www.websters-online-dictionary.org/images/wiki/wikipedia/commons/thumb/f/fd/Knapsack.svg/250px-Knapsack.svg.png 14http://www.astrokettle.com/b_y3r1x.gif
  15. 15. Reactive-Predictive Load Balancer Polling # of virtual instances CPU Memory I.O Network utilization 1) Which virtual machines on the overloaded server to migrate 2) The new destination server 1 to migrate to 2 15
  16. 16. Conclusion• Key problem – How to be Intelligent? – How to control data congestion? – Traditional approach • Prediction performance of each virtual node • Task migration – Future work • Allocate request before congestion • Data flow monitoring and request scheduling 16
  17. 17. Q&A 17
  18. 18. THANKS 18
  19. 19. References• http://en.wikipedia.org/wiki/Infrastructure_as_a_service• http://en.wikipedia.org/wiki/Platform_as_a_service• http://en.wikipedia.org/wiki/Intelligent_transportation_system• Junwei Cao, Daniel P. Spooner, Stephen A. Jarvis, Grahan R. Nudd, Grid load balancing using intelligent agents, ACM Future Generation Computer Systems, 2005, Page 135-149• Shibu Daniel, Minseok Kwon, Prediction-based Virtual Instance Migration for Balanced Workload in the Cloud Datacenters, RIT, 2011• Fei-Yue Wang, Parallel Control and Management for Intelligent Transportation Systems: Concepts, Architectures, and Applications, IEEE Intelligent Transportation Systems, 2010, Page 630-638 19

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