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Progress Report . 04/25/2012. Scaling Algorithms. Workload-based Compare the number of VM needed to the number of running VM. Can scale multiple VMs once. Majority Vote Scale if most of the VMs are over/under-load. Scale only one VM at a time. Not sure how many to scale.

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Progress report

Progress Report

04/25/2012


Scaling algorithms
Scaling Algorithms

  • Workload-based

    • Compare the number of VM needed to the number of running VM.

    • Can scale multiple VMs once.

  • Majority Vote

    • Scale if most of the VMs are over/under-load.

    • Scale only one VM at a time.

      • Not sure how many to scale.


Improvement for majority vote
Improvement for Majority Vote

  • Assume that the workloads on each running VM is inversely proportional to the number of running VM.

  • We sum up the loading of each VM, and divide by threshold to get the number of running VM needed.


Example
Example

  • CPU loading:

    • VM_A:90%

    • VM_B:99%

    • VM_C:100%

  • CPU threshold: 70%

  • VM needed:

  • Ceil((90+99+100)/70) = Ceil(4.128…)=5


Rationale
Rationale

  • Majority Vote can not distinguish “how busy”.

    • “100 requests/sec and 1,000 requests/sec both make CPU loading 100%.”

  • Scale multiple times instead of one to get the right number of running VM.



Possible reason
Possible Reason

  • Our workload changes too often and too drastic.


Dimension reduction
Dimension Reduction

CPU, Load_one, Load_five, Load_fifteen

PCA

CPU Indicator

pkg_in, pkg_out, byte_in, byte_out

PCA

Network Indicator

Memory

Memory Indicator


Pca on each component
PCA on Each Component

  • Apply PCA to the metrics from each component.

    • CPU:

      • CPU(%), Load_one, Load_five, Load_fifteen

    • Memory:

      • Memory(%)

    • Network:

      • pkg_in, pkg_out, byte_in, byte_out

  • Form a new metric for each component.

    • CPU’, Memory’, Network’


Principle component analysis 1
Principle Component Analysis[1]

  • A mathematical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of uncorrelated variables called principal components.

[1] http://en.wikipedia.org/wiki/Principal_component_analysis




Progress report
Next

  • Use regression analysis to predict performance from these new metrics.

    • Predict performance =

    • a*CPU’ + b*Network’ + c*Memory + d


Paper study
Paper Study

  • CGO 2012: Compiling for Niceness