Homework2

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Homework2

  1. 1. POWER-AWARE MULTIDATACENTER MANAGEMENT USING MACHINE LEARNING Presented by: Omar Sulca CLOUD COMPUTING
  2. 2. CONTENT 1. Introduction 2. What they looking for? 3. What is Multi Data Center? 4. Managing Multi DCs 5. Modeling the System 6. Conclusions
  3. 3. 1. INTRODUCTION  Cloud Computing, has become crucial for the externalization of IT resources for business, organizations and people. “everything as a service” (plataform, infrastructure and service)  Providers want in turn to optimize the use of the resources they have deployed with their own metrics
  4. 4. 1. INTRODUCTION  Factors to be optimized Revenues Costs • came from servicing the clients of the hosted web-services with reasonable Quality of Service (QoS) • operational costs for the infrastructure (Energy-realeted cost)  Consolidation - Set the maximum number of services in the least viable amount of hosting machines, so the number of on-line machines and resources is minimized.  Virtualization technology has made consolidation easier,
  5. 5. 2. WHAT THEY LOOKING FOR? “Build a model to automate (AC) an improve the process of achieve allocation of virtualized web-services, using a Machine Learning (ML) and Data Mining, to predict behavior and select “policies” to be applied in a multi-DC” Energy Saving in Cloud Self-management
  6. 6. 3. WHAT IS MULTI DATA CENTER? • Its a Networking of Data Centers (DCs) interconected Must be considerate Migration overheads Service-Client proximity Energy cost at diferent locations Modularity between inter-DC relations an information
  7. 7. 4. MANAGING MULTI DCS 3 Multi-DataCenter Business Model SLA (Service Level Agreement) 2 1 4 ensure the agreed QoS for de VM, while minimizing the cost by reducing the resorces usage
  8. 8. 5. MODELING THE SYSTEM  In this case Quality of Service = Response Time Mathematical Model (monitoring PM resources and adjusting VM placements and quotas) Using Machine Learning + Data Mining to Around the world Predict behavior and Scheduling the VM Across de DC networks
  9. 9. 5. MODELING THE SYSTEM  The Machine Learning decisive factors  Energy consumption  Resource allocation  QoS  Questions to predict  How good will each VM behave?  How much CPU/Mem/IO… will each VM demand?
  10. 10. 6. CONCLUSIONS 1. Optimizing the schedule and management of multi-DC systems requires balancing several factors, like economic revenues, Quality of Service and operational costs such as energy. 2. Using virtualization technology is presented a model to solve a multi-DC scheduling problem which balances and optimizes the economic factors above. 3. A few issues for future study are: a) How decide which VMs are excluded from inter-DC scheduling or which PMs are offered as host candidates for scheduling; b) The inclusion of more operational costs (networking, bandwidth management,etc.) c) The green energy into the scheme and the environmental impact of computation. d) The use of online learning methods to make the system react quickly to changes (application behavior, hardware or middleware changes, or workload characteristics
  11. 11. Thanks

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