Misalignments To Reduce Resource Over-Provisioning in Cloud - - PowerPoint PPT Presentation

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Misalignments To Reduce Resource Over-Provisioning in Cloud - - PowerPoint PPT Presentation

Aug 30, 2023 255 likes •491 views

Considering Resource Demand Misalignments To Reduce Resource Over-Provisioning in Cloud Datacenters Liuhua hua Chen en Haiyi ying ng Shen en Dept pt. of E f Elec ectric trical al and d Co Compu puter ter Eng. g. Dept pt. of Co

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SLIDE 1

Considering Resource Demand Misalignments To Reduce Resource Over-Provisioning in Cloud Datacenters Haiyi ying ng Shen en

Dept

  • pt. of Co

f Compu pute ter r Sc Science ence University ersity of f Vi Virgi ginia, nia, USA SA

Liuhua hua Chen en

Dept

  • pt. of E

f Elec ectric trical al and d Co Compu puter ter Eng. g. Cl Clems mson

  • n University,

ersity, USA SA

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SLIDE 2

Cloud Computing

  • Prof. Haiying Shen, University of Virginia

2

  • Cloud computing: large groups of remote servers networked to

allow centralized data storage and online access to computer services or resources

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SLIDE 3

Cloud Providers

  • Prof. Haiying Shen, University of Virginia

3

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SLIDE 4

Cloud Customers

  • Prof. Haiying Shen, University of Virginia

4

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SLIDE 5

Research Problem and Goal

5

  • Prof. Haiying Shen, University of Virginia

Virtual machines (VMs) Physical machines (PMs)

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SLIDE 6

Motivation

  • Prof. Haiying Shen, University of Virginia

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  • Over-loaded PMslow QoSSLO violationpenalty
  • Under-loaded PMs resource wastehigh system cost
  • Problem: reduce over-loaded and under-loaded PMs
  • Goal: high QoS, high resource efficiency, high profit

PM VM VM VM VM VM VM VM PM VM PM VM VM VM Virtual machine (VM) VM VM VM Physical machine (PM)

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SLIDE 7

PM

Initial Complementary VM Consolidation

  • Previous work (CompVM):

VM VM VM VM PM PM PM PM

  • How to achieve ?

VM VM VM VM

  • Prof. Haiying Shen, University of Virginia

7

  • L. Chen and H. Shen, Consolidating Complementary VMs with Spatial/Temporal-

awareness in Cloud Datacenters, Proc. of the 33rd Annual IEEE International Conference on Computer Communications (INFOCOM'14), Toronto, Canada, 2014

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SLIDE 8

CPU utilization (%)

VM1 VM2

100 100 Memory utilization (%) Time Utilization (%) T 100

VM1 VM2 VM3

Spatial Temporal

High CPU low MEM Low CPU high MEM

Initial Complementary VM Consolidation – Motivation

  • CompVM: load balance in the long term

consolidate complementary VMs

VM2 VM1 VM1 VM1

Patterns?

Demand Capacity

Resource waste Under-loaded PMs

Demand Capacity

Over-loaded PMs

Demand Capacity

No under/over-loaded PMs

  • Prof. Haiying Shen, University of Virginia

8

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SLIDE 9

Initial Complementary VM Consolidation – VM Utilization Pattern

  • Measurement:

– MapReduce jobs: TeraSort, TestDFSIO read/write – cluster trace, trace

  • Periodic resource utilization patterns exist in many VMs running

– the same short-term job – a long-term job

TestDFSIO read Google cluster trace TestDFSIO read

  • Prof. Haiying Shen, University of Virginia

9

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SLIDE 10

Initial Complementary VM Consolidation – Utilization Pattern Detection

0.0 1.0 2.0 3.0 4.0 5.0 12 24 36 48 60 72

Time (hr) CPU utilization (%)

trace 2 trace 1 pattern f3 f1 f2 u

  • Prof. Haiying Shen, University of Virginia

10

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SLIDE 11

Initial Complementary VM Consolidation – VM Allocation Method

For each PM: check if it has sufficient capacity for the VM Select the PM: with the least remaining resource after allocating the VM Detect resource utilization pattern of the VM

𝐹

𝑘: ratio of aggregated resource demand

𝑁

𝑘: distance between the average resource

demand vector and the capacity vector

pattern

Memory resource waste

PM VM4 VM5 VM6 PM VM2 PM VM1 VM3 VM7 PM1 PM2 PM3 Choose a PM PM VM4 VM5 VM6 PM VM2 PM VM1 VM3 VM7 PM1 PM2 PM3 Choose a PM For each PM: check if it has sufficient capacity for the VM Select the PM: with the least remaining resource after allocating the VM Detect resource utilization pattern of the VM

consolidate complementary VMs

  • Prof. Haiying Shen, University of Virginia

11

PM VM4 VM5 VM6 PM VM2 PM VM1 VM3 VM7 PM1 PM2 PM3 Choose a PM

CPU utilization (%)

VM1 VM2

100 100 Memory utilization (%) Time Utilization (%) T 100

VM1 VM2 VM3

Spatial Temporal

High CPU low MEM Low CPU high MEM

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SLIDE 12

Reducing Prediction Error

12

  • Prof. Haiying Shen, University of Virginia

pattern

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SLIDE 13

VM Consolidation

– Reduce Provisioned Resource

  • Pulse deviation yields a pattern with a pulse width

larger than the actual pulse width

  • Resource over-provisioning
  • Not revealed or studied before

13

  • Prof. Haiying Shen, University of Virginia

deviation derived pattern derived pattern Pulse

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SLIDE 14

VM Consolidation

– Trace Study

  • Pulse deviations are common

14

  • Prof. Haiying Shen, University of Virginia

Google Cluster trace PlanetLab trace

100 jobs, 29920 tasks 1000 jobs, 4695 tasks

Average task execution time is around 100 minutes/seconds MapReduce benchmarks on a HPC cluster (Wordcount, Grep, Terasort, TestDFSIO and PiEstimator)

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SLIDE 15

VM Consolidation

– Trace Study

  • Resource efficiency: demand/capacity
  • Even using CompVM, the resource efficiency still

needs to improve

15

  • Prof. Haiying Shen, University of Virginia

Google Cluster trace PlanetLab trace

100 jobs, 1550 tasks 1000 jobs, 4695 tasks

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SLIDE 16

VM Consolidation

– Pattern Refinement Methodology

16

  • Prof. Haiying Shen, University of Virginia
  • Pattern refinement methods

– Lowering cap – Reducing pulse width – Optimal base provisioning

Time unused resource t1 t2 t3 chigh clow

: lower each value in the pattern by chigh-clow

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SLIDE 17

VM Consolidation

– Pattern Refinement Methodology

17

  • Prof. Haiying Shen, University of Virginia
  • Pattern refinement methods

– Lowering cap – Reducing pulse width – Optimal base provisioning : postpone the pulse from t1 to t3

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SLIDE 18

VM Consolidation

– Pattern Refinement Methodology

18

  • Prof. Haiying Shen, University of Virginia
  • Pattern refinement methods

– Lowering cap – Reducing pulse width – Optimal base provisioning

Time b1 b2

refine pattern based on optimal b value that maximizes resource efficiency

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SLIDE 19

VM Consolidation

– Pattern Refinement Methodology

19

  • Prof. Haiying Shen, University of Virginia
  • Pattern refinement methods

– Lowering cap – Reducing pulse width – Optimal base provisioning

  • Risk violating SLOs
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SLIDE 20

20

  • Prof. Haiying Shen, University of Virginia

Cluster experiment Google Cluster trace

Pattern refinement yields higher resource efficiency without compromising VM performance by handling pulse deviations!

VM Consolidation

– Performance Evaluation

2000 VMs, CloudSim, Palmetto HPC cluster, Traces: NAS Parallel Benchmark, cluster

Improve by 10%, 70% Improve by 40%

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SLIDE 21

Conclusion

  • Trace study

– Pulse deviations are common – Even using CompVM, the resource efficiency still needs to improve

  • Pattern refinement methods

– Lowering cap – Reducing pulse width – Optimal base provisioning

  • Experiments

– Higher resource efficiency without compromising VM performance

21

  • Prof. Haiying Shen, University of Virginia
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SLIDE 22

Future Work

  • Consider other factors (e.g., SLOs) in VM

consolidation

  • Consider VM migration

22

  • Prof. Haiying Shen, University of Virginia
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SLIDE 23

23

  • Prof. Haiying Shen, University of Virginia

Thank you! Questions & Comments?

Haiying Shen

hs6ms@virginia.edu Associate Professor Department of Computer Science University of Virginia