presentation robayet nasim (ieee cloudcom 2016)
TRANSCRIPT
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OPTIMIZING VIRTUAL MACHINE CONSOLIDATION IN VIRTUALIZED DATACENTERS USING RESOURCE
SENSITIVITY
ROBAYET NASIM, JAVID TAHERI, ANDREAS KASSLER
KARLSTAD UNIVERSITY [email protected]
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 ?
VM VM
VM
VM
VM sensitive to CPU
VM sensitive to disk
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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
𝑆𝑆𝑆𝑆𝑆𝑆𝑐𝑐𝑆𝑆𝑆𝑆𝑆𝑆𝑚𝑚𝑆𝑆𝑆𝑆𝑆𝑆𝑑𝑑
ProfTable vmLimiter vmDataCollector
vmBBThrPred
vmModeler vmPredictor
�𝑇𝑇𝑇𝑇𝑇(𝐶𝐶,𝑀𝑀,𝐷𝐷)𝑃𝑃𝐷𝐷(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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