COMMA: Coordinating the Migration of Multi-tier applications 1 Jie Zheng* T.S Eugene Ng* Kunwadee Sripanidkulchai† Zhaolei Liu* *Rice University, USA †NECTEC, Thailand Presenter: Zhaolei (Fred) Liu

Live migration in cloud For cloud providers Hardware maintenance Resource relocation 2 For users Provision services Price and performance

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 3 Presentation tier Logic tier Data tier

How to migrate a group of VMs? Sequential migration: migrate VMs one by one 4

How to migrate a group of VMs? 5 Parallel migration: migrate all VMs at the same time

Problem with sequential migration 6 Application performance degradation caused by component split during migration

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. 7

COMMA: Coordinating the Migration of Multi-tier Applications Formulation & objective System design Algorithms Implementation & evaluation 8

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 t i : the migration finish time of the i th VM 9 Minimize Not ideal!

COMMA: Periodic adaptation and coordination 10 Hypervisor Controller Controller 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.

Coordination in the first stage of pre- copy Coordinate pre-copy of all VMs to finish at the same time 11 vm1 vm2vm3 vm Communication Graph (KBps) VM2 Pre-copy VM3 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 12 VM3 VM4 VM1 VM2 vm1 vm2 vm3 20MBps 5MBps 10MBps vm4 20MBps 30MBps vm1 vm2vm3 vm Communication Graph (KBps) VM2 Pre-copy VM3 Pre-copy VM4 Pre-copy VM1 Pre-copy Time Stage 1 Stage 2 Migration Start Migration End Maximal dirty rate Available bandwidth

Maximize bandwidth utilization and minimize performance degradation by scheduling dirty iteration inside each group in the second stage 13 VM3 VM2 VM3 VM2 VM1 VM3 VM2 VM1 Bandwidth No Performance Degradation ； Long Migration Time Bandwidth No Performance Degradation ； Short Migration Time Performance Degradation ； Short Migration Time Bandwidth Time 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

Implementation & Evaluation: Fully implemented COMMA on KVM platform, QEMU version Used SpecWeb and RUBiS as the application Fully Evaluated COMMA on various scenarios 14

15 COMMA is able to reduce application performance degradation Time(s) Average response latency (ms) Migrating 3-VM RUBiS using COMMA

16 Migrating 3-VM RUBiS using sequential migration Compared to COMMA, sequential migration incurs high application performance degradation Time(s) Average response latency (ms)

COMMA is able to minimize migration impact MB More results: vary the VM number, placement, workload, and migration to EC2

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 18