This document proposes optimizing virtual machine consolidation in virtualized datacenters by considering the resource sensitivity of VMs. It designs a theoretical model that minimizes interference during VM migrations by using sensitivity values for different resource types like CPU and disk. The model is evaluated using various applications with different resource demands and sensitivity profiles, showing that considering sensitivity reduces performance degradation and improves fairness of resource allocation compared to a sensitivity-oblivious approach. Future work includes evaluating the impact of sensitivity estimation accuracy and designing online heuristics that leverage sensitivity.

1. OPTIMIZING VIRTUAL MACHINE CONSOLIDATION
IN VIRTUALIZED DATACENTERS USING RESOURCE
SENSITIVITY
ROBAYET NASIM, JAVID TAHERI, ANDREAS KASSLER
KARLSTAD UNIVERSITY
ROBAYET.NASIM@KAU.SE
IEEE CLOUDCOM 2016, LUXEMBOURG, DECEMBER 2016.

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VIRTUALIZED DATACENTERS
VM
Different applications
running on virtual machines
(VMs) share same physical
machine (PM)
Why ?
Improved resource utilization
VM
VM
VM
VM
VM
VM

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VM CONSOLIDATION
VM
But?
VMs share many resources.
Hard to ensure proper isolation.
Contention?
Specially, when VMs compete for
the same resource type.
Why ?
VM workload varies over time
VM
VM
VM
VM
VM
VM
VM
VM
Reduce energy consumption

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 VM Co-location interference depends on
– VM Placement
– VMs workload demand
– Sensitivity of VMs in relation to
different resource types
PERFORMANCE DEGRADATION
How can we improve the VM
placement/migration given we have
knowledge of VM sensitivity values
to different resource types?

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VM MIGRATION/PLACEMENT – SENSITIVITY ?
VMVM
VM
VM
VM sensitive to CPU
VM sensitive to disk
VM
VM
Which migration/placement
option should we choose?

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RESOURCE SENSITIVITY AND PERFORMANCE PREDICTION
VMware vSphere
VMware vCenter Server
Manage
VMware vSphere
vmBBProfiler
vmProfiler vmDataAnalyzer
𝑆𝑆𝑆𝑆𝑆𝑆𝑐𝑐
𝑆𝑆𝑆𝑆𝑆𝑆 𝑚𝑚
𝑆𝑆𝑆𝑆𝑆𝑆𝑑𝑑
ProfTablevmLimiter vmDataCollector
vmBBThrPred
vmModeler vmPredictor
�
𝑇𝑇𝑇𝑇𝑇(𝐶𝐶, 𝑀𝑀, 𝐷𝐷)
𝑃𝑃𝑃𝑃(𝐶𝐶, 𝑀𝑀, 𝐷𝐷)

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 Design a theoretical optimal interference-aware VM live migration
strategy.
 Using sensitivity values for the VMs for different resource types in
order to minimize co-location performance penalty for a given
energy budget.
 Validate the proposal using well known applications with various
resource signatures – varying from pure CPU/Mem/Disk-intensive
to mixed of them.
MAIN CONTRIBUTIONS

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SENSITIVITY AWARE VM CONSOLIDATION MODEL
 Power consumption of a PM can be
modeled as linear function of resource
utilization (such as CPU load, etc.)
 Utilization of the VMs allocated to a PM
 Budget constraint
Limit on maximum power consumption
Allocated resource to the VMs
Utilization of PMs
Resource demands of old assignments VM migrating towards server VM migrating away
Overbooking factor

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SENSITIVITY AWARE VM CONSOLIDATION MODEL
 Sensitivity calculation
 Objective is to
– Calculate the sum of sensitivity of all
the VMs for all resources allocated
to a PM after consolidation.
– Minimize variance of total sensitivity
for all resources among PMs.
– The limit on energy consumption
should not be violated.
Sensitivity of resource i for VM k
Mean of sensitivity for resource i
New VM allocations

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 Ensure valid migrations
– Migrating within same PM is not
possible, etc.
 Other constraints …
SENSITIVITY AWARE VM CONSOLIDATION MODEL

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 Matlab is used as problem modeler with IBM CPLEX as solver.
– Tune “epgap” parameter to accept relaxed integer solution.
 Small scenario to demonstrate model capabilities
– 20 PMs, 45 VMs.
– 5 selected applications from Phoronix test suite (v5.2.1) (168 available) .
– Different levels of resource demands for the same application.
– Different allowed overbooking on PMs – 50%, 100%, 150%, 200% .
 Solved the model twice for each configuration.
– Sen-Aware: Quantitative measures to reflect the sensitivity of the applications.
– Sen-Oblivious: Equal sensitivity for all the applications.
EVALUATION

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SELECTED APPLICATIONS

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 Case 1: Impact on performance degradation,
– Three different levels of resource demands,
 Case 2: Impact on fair share of available resources
 Case 3: Impact of resource demands on throughput.
EVALUATION SCENARIOS

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CASE 1: PERFORMANCE DEGRADATION
Considering Sensitivity value during Migration
Helps to reduce Performance degradation
Resource demand -

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CASE 1: PERFORMANCE DEGRADATION
Considering Sensitivity value during Migration
Helps to reduce Performance degradation
Resource demand -

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CASE 1: PERFORMANCE DEGRADATION
Considering Sensitivity value during Migration
Helps to reduce Performance degradation
Resource demand -

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CASE 2: FAIR ALLOCATION OF RESOURCES
Considering Sensitivity value during Migration
Also ensures allocation fairness
Resource demand - , Overbooking - 200%, Energy limit - 2725 W

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CASE 3: RESOURCE DEMANDS VS THROUGHPUT
Amount of Reserve resources for the applications
has noticeable impact on their throughput
Resource demand -

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 Conclusions
– Importance of using sensitivity information of VMs/applications to their
resources for the VM consolidation problem.
– Efficient migrations by significantly reducing performance degradation of
VMs especially for higher levels of overbooking.
– Not intended for online optimization but can be used to compare the
performance of any heuristic.
 Future work
– Evaluate how the accuracy of the sensitivity estimation
affect the overall performance.
– Design fast heuristic while taking into account the
sensitivity values and integrate them into online
optimization tool such as OpenStack Watcher.
CONCLUSIONS AND FUTURE WORK

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