COMMA Coordinating the Migration of Multitier applications Presenter

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

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

Multi-tier application in cloud Presentation tier • In cloud, a multi-tier application runs on multiple VMs • The VMs hosting a multi-tier application need to communicate with each other Logic tier Data tier 3

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

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

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

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 COMMA Parallel Sequential 0 200 400 600 800 1000 Component Split time(s) 1200 1400 • 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 Not ideal! • Define impact as the volume of traffic impacted by migration • TM: traffic matrix • ti: the migration finish time of the i th VM Minimize 9

COMMA: Periodic adaptation and coordination Controller Hypervisor Messages: • Migration progress • Available bandwidth • Disk dirty rate and memory dirty rate (Pacer*) Controller Messages: • Start migration • Set migration speed Hypervisor * J. Zheng, T. S. E. Ng, K. Sripanidkulchai, and Z. Liu. Pacer: A progress management system for live virtual machine migration in cloud computing. IEEE Transactions on Network and Service Management, 10(4): 369– 382, Dec 2013. 10

Coordination in the first stage of pre-copy • Coordinate pre-copy of all VMs to finish at the same time Stage 2 Stage 1 80 vm 1 vm 2 20 vm 4 30 vm 3 VM 1 Pre-copy VM 2 Pre-copy VM 3 Pre-copy 50 VM 4 Pre-copy Communication Graph (KBps) Migration Start Time Migration End 11

Inter-group scheduling in the second stage of dirty iteration and memory migration • Meet the bandwidth limit by dividing VMs into different groups Stage 2 Stage 1 80 vm 1 30 vm 2 VM 1 Pre-copy VM 2 Pre-copy VM 3 Pre-copy vm 3 20 vm 4 50 Communication Graph (KBps) VM 3 VM 4 Pre-copy Migration Start vm 1 20 MBps vm 3 10 MBps vm 2 5 MBps vm 4 20 MBps Maximal dirty rate Time 30 MBps Available bandwidth VM 1 VM 2 Migration End 12

Intra-group scheduling VM 3 Time Performance Start at the same Degradation； time; Short Migration Finish at different Time time VM 1 VM 2 VM 3 Time Start at the same No Performance time; Degradation； Finish. Migration at the same Long time Time Bandwidth VM 1 VM 2 Bandwidth • Maximize bandwidth utilization and minimize performance degradation by scheduling dirty iteration inside each group in the second stage VM 1 VM 2 VM 3 Time No Performance Start at different Degradation； time; Short Finish Migration at the same Time time 13

Implementation & Evaluation: • Fully implemented COMMA on KVM platform, QEMU version 0. 12. 50 • Used Spec. Web and RUBi. S as the application • Fully Evaluated COMMA on various scenarios 14

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

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

COMMA is able to minimize migration impact B M 3. 6 COMMA Parallel Sequential 0 500 1000 1500 Migration Impact (MB) 2000 2500 More results: vary the VM number, placement, workload, and migration to EC 2 17

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