COMMA: Coordinating the Migration of Multi-tier applications

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Jie Zheng* T.S Eugene Ng* Kunwadee Sripanidkulchai† Zhaolei Liu*

*Rice University, USA †NECTEC, Thailand

Presenter: Zhaolei (Fred) Liu

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Live migration in cloud

• For cloud providers• Hardware maintenance• Resource relocation

• For users• Provision services• Price and performance

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Multi-tier application in cloud

• In cloud, a multi-tier application runs on multiple VMs

• The VMs hosting a multi-tier application need to communicate with each other

Presentation tier

Logic tier

Data tier

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How to migrate a group of VMs?

•Sequential migration: migrate VMs one by one

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How to migrate a group of VMs?

•Parallel migration: migrate all VMs at the same time

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Problem with sequential migration•Application performance degradation caused by

component split during migration

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Problem with parallel migration

•VMs still may not finish migration at the same time • Static factors: VM disk size, memory size, etc. • Dynamic factors: network bandwidth, application workload, etc.

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COMMA: Coordinating the Migration ofMulti-tier Applications

• Formulation & objective• System design• Algorithms• Implementation & evaluation

Sequential

Parallel

COMMA

0 200 400 600 800 1000 1200 1400

Component Split time(s)

1s

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Formulation & Objective

•Minimizing the migration’s impact on application performance• Define impact as the time when VMs are split

• Define impact as the volume of traffic impacted by migration• TM: traffic matrix• ti: the migration finish time of the i th VM

Minimize

Not ideal!

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COMMA: Periodic adaptation and coordination

Hypervisor

Hypervisor

Hypervisor

ControllerController Messages:• Start migration• Set migration speed

Hypervisor Messages:• Migration progress• Available bandwidth• Disk dirty rate and memory dirty rate (Pacer*)

* J. Zheng, T. S. E. Ng, K. Sripanidkulchai, and Z. Liu. Pacer: A progress management system for live virtual machine mi-gration in cloud computing. IEEE Transactions on Network and Service Management, 10(4):369–382, Dec 2013.

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Coordination in the first stage of pre-copy• Coordinate pre-copy of all VMs to finish at the same time

vm1

vm2 vm3

vm4

80 30

2050

Communication Graph (KBps)

VM2 Pre-copyVM3 Pre-copy

VM4 Pre-copy

VM1 Pre-copy

Time

Stage 1 Stage 2

Migration Start

Migration End

• Meet the bandwidth limit by dividing VMs into different groups

Inter-group scheduling in the second stage of dirty iteration and memory migration

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VM3VM4

VM1VM2

vm1

vm2

vm320MBps

5MBps

10MBps

vm4 20MBps 30MBps

vm1

vm2 vm3

vm4

80 30

2050

Communication Graph (KBps)

VM2 Pre-copyVM3 Pre-copy

VM4 Pre-copy

VM1 Pre-copy

Time

Stage 1 Stage 2

Migration Start

Migration End

Maximal dirty rate Available bandwidth

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• Maximize bandwidth utilization and minimize performance degradation by scheduling dirty iteration inside each group in the second stage

VM3

VM2

VM3

VM2

VM1 VM1

VM3

VM2VM1

Band

wid

th

No Performance Degradation；Long Migration Time

Band

wid

th

No Performance Degradation；Short Migration Time

Performance Degradation；Short Migration Time

Band

wid

th

Time TimeTime

Start at the same time; Finish at different time

Start at the same time; Finish at the same time

Start at different time; Finish at the same time

Intra-group scheduling

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Implementation & Evaluation:

• Fully implemented COMMA on KVM platform, QEMU version 0.12.50

• Used SpecWeb and RUBiS as the application

• Fully Evaluated COMMA on various scenarios

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COMMA is able to reduce application performance degradation

Time(s)

Aver

age

resp

onse

late

ncy

(ms)

Migrating 3-VM RUBiS using COMMA

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Migrating 3-VM RUBiS using sequential migration

Compared to COMMA, sequential migration incurs high application performance degradation

Time(s)

Aver

age

resp

onse

late

ncy

(ms)

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COMMA is able to minimize migration impact

Sequential

Parallel

COMMA

0 500 1000 1500 2000 2500

Migration Impact (MB)

3.6 MB

More results: vary the VM number, placement, workload, and migration to EC2

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Summary & Advantages

• COMMA is able to minimize application performance degradation during migration

• COMMA has tiny overhead • Efficient heuristic algorithms• Computation time less than 10 ms

• COMMA is application independent• Run-time adaptation• All measurements are on hypervisor level

• COMMA has great applicability• Designed for pre-copy model (KVM, XEN)• Easily adapt to other migration models