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Progress Report - PowerPoint PPT Presentation

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

04/25/2012

• 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.

• 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.

• VM_A:90%

• VM_B:99%

• VM_C:100%

• CPU threshold: 70%

• VM needed:

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

• Majority Vote can not distinguish “how busy”.

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

• Our workload changes too often and too drastic.

PCA

CPU Indicator

pkg_in, pkg_out, byte_in, byte_out

PCA

Network Indicator

Memory

Memory Indicator

• Apply PCA to the metrics from each component.

• CPU:

• Memory:

• Memory(%)

• Network:

• pkg_in, pkg_out, byte_in, byte_out

• Form a new metric for each component.

• CPU’, Memory’, Network’

• 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

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

• Predict performance =

• a*CPU’ + b*Network’ + c*Memory + d

• CGO 2012: Compiling for Niceness