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Analysis of VM Usage in Google Cloud Computing at Tufts University

Explore the utilization of Virtual Machines (VM) in Google Cloud at Tufts University focusing on Transmission Latency, Traffic Analysis, and Reliability. Learn how SaaS, PaaS, and IaaS play crucial roles in the cloud computing platform. Discover the impact of network latency, traffic weight, and reliability on cloud infrastructure.

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Analysis of VM Usage in Google Cloud Computing at Tufts University

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  1. Tufts University Virtual Machine Usage in Cloud Computing in Google Yaoshen Yuan EE-126

  2. Tufts University Google Cloud Computing Platform • SaaS (Software as a Service) • Clients can download software and other resources or create documents and save resources through SaaS. • PaaS (Platform as a Service) • provides clients with the platform that allow them to deploy the virtual development environment. • IaaS (Infrastructure as a Service) • shares the Internet infrastructure and consumers have the ability to configure the operating system, storage, applications.

  3. Tufts University Diagram for Platform

  4. Tufts University Analysis of VM usage in Google Cloud • Transmission Latency • Traffic Analysis • Reliability

  5. Tufts University Transmission Latency

  6. Tufts University Transmission Latency virtual machine will access the nearest data center, so the distance should be

  7. Tufts University Transmission Latency P reflects the advantage of cloud computing compared to the server station model

  8. Tufts University Traffic Analysis the augmentation of the number of request a data center receive increases the network latency, so it is necessary to consider the network traffic

  9. Tufts University Traffic Analysis mean traffic weight of each data center under the condition that request is produced randomly over the world during a day

  10. Tufts University Reliability it is important that when one or some of the data center collapse, VM instances can still access their resources

  11. Tufts University Reliability

  12. Tufts University Reliability

  13. Tufts University Reliability

  14. Tufts University Reliability

  15. Tufts University Reliability

  16. Tufts University Conclusion • Because of the lack of data of real channel connecting the world, the model (using straight line in sphere to replace channel) used to analyze is not accurate. • Model built under the condition that resources of one VM instance are saved in all data center. • Less latency, higher traffic tolerance, higher reliability. • Building server on Google Cloud using VM instance is sensible when large Page View (PV) is estimated.

  17. Tufts University REFERENCE [1] Niyato D. Optimization-based virtual machine manager for private cloud computing[C]//Cloud Computing Technology and Science (CloudCom), 2011 IEEE Third International Conference on. IEEE, 2011: 99-106. [2] Rajan S, Jairath A. Cloud computing: The fifth generation of computing[C]//Communication Systems and Network Technologies (CSNT), 2011 International Conference on. IEEE, 2011: 665-667. [3] Ye K, Huang D, Jiang X, et al. Virtual machine based energy-efficient data center architecture for cloud computing: a performance perspective[C]//Proceedings of the 2010 IEEE/ACM Int'l Conference on Green Computing and Communications & Int'l Conference on Cyber, Physical and Social Computing. IEEE Computer Society, 2010: 171-178. [4] Savu L. Cloud computing: Deployment models, delivery models, risks and research challenges[J]. Computer, 2011. [5] Managed VM, https://cloud.google.com/appengine/docs/managed-vms/ [6] Shang Z, Chen W, Ma Q, et al. Design and implementation of server cluster dynamic load balancing based on OpenFlow[C]//Awareness Science and Technology and Ubi-Media Computing (iCAST-UMEDIA), 2013 International Joint Conference on. IEEE, 2013: 691-697. [7] Google Data Center http://www.google.com/about/datacenters/inside/locations/index.html

  18. Tufts University Thank you!

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