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Unveiling VM Dependencies through CPU Utilization Monitoring in Cloud Environments

This study explores how monitoring CPU utilization can reveal interdependencies between virtual machines, aiding in better resource allocation, disaster recovery, and troubleshooting in cloud environments.

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Unveiling VM Dependencies through CPU Utilization Monitoring in Cloud Environments

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  1. Look Who’s Talking: Discovering Dependencies between Virtual Machines Using CPU Utilization HotCloud 10 Presented by Xin

  2. Problem with current cloud • Hard to monitor, manage and debug • Complex dependency causes butterfly effect

  3. We need to know VM Interdependencies • Better VM placement and migration decisions • Being not restricted to the physical machine, isn’t it what we want at the beginning? • Better resource allocation • Reduce intra cloud traffic. • Better disaster recovery automation • What can we do about this? • Better troubleshooting

  4. How to infer VM Interdependencies? • In a multi-tier application, VMs have request-response interactions • The server’s workload is determined by the clients workload

  5. How to infer VM Interdependencies? • Monitor • Sample ‘per VM’ CPU utilization • Model • Estimate an model for CPU utilization of each VM • Cluster • K-means clusters VMs with similar models VMs together

  6. Monitoring • CPU utilization sampled per VM • Sampling Period • Too small : increases computation Too large : Might miss relevant spikes • Sample size • 300 seconds

  7. Modeling • Auto Regressive modeling

  8. Modeling • If VM1 and VM2 have the same coefficients, we say they are interdependent • Why do we need this model? • Can we just compare the time series using a scale factor?

  9. Clustering

  10. Evaluation • 31 VMs spread over 5 physical servers • Applications • RUBiS : eBay like benchmark • 4VMs – Apache, Tomcat, MySQL and RUBiS client • Hadoop MapReduce Framework • 3 VMs – 1 master and 3 slave nodes • Iperf : Network testing tool • 2 VMs – sender and receiver

  11. Evaluation – 91.67% true positives – 99.08% true negatives

  12. Why it works ? • RUBiS • 100% accuracy • Lot of request-response interaction between the VMs • ‘n-tier’ application model

  13. Why it fails ? • Hadoop • false positive and false negative. • Mappers and reducers communicate intermediate results via files

  14. How does the system work?

  15. Discussion • Security concerns • Who can monitor the CPU utilization of all VMs? • The provider can ues “xentop” command to display real-time information about a Xen system • Can one VM infer the CPU utilization of another VM located at the same physical machine? • Infer from CPU cache utilization? • What can an attacker do when it detects the interdependencies of other VMs? • Attack the bottleneck • Estimate the workload

  16. Discussion • Is identifying VM interdependency important? • Yes, debugging, resource allocation • Is locating interdependent VMs on the same machine a good idea? • For provider: yes. For user: no • What can we do about disaster recovery? • What kind of disaster? • What causes it? • Can we rank the top 10 reasons? Is VM interdependence on the list?

  17. Discussion • Is CPU utilization a good metric? • No. What is a VM runs multiple applications? • No. We cannot infer who depends on whom. • What metrics can we use?

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