Migration of groups of virtual machines in distributed data centers to reduce cost

Sabidur Rahman

Netlab Friday Group Meeting

Feb 17, 2017http://www.linkedin.com/in/kmsabidurrahman/

krahman@ucdavis.edu

Paper review

“Energy-aware migration of groups of virtualmachines in distributed data centers”

byRodrigo A. C. da Silvaa and Nelson L. S. da Fonseca

fromInstitute of ComputingState University of Campinas, Brazil

published inGlobal Communications Conference (GLOBECOM), 2016.

Paper review

Introduction:

Select groups of virtual machines (VMs) to be migrated

Select VM groups with network proximity in order to increase potential number of equipment to be switched off

VMs are migrated only if it results in energy savings

Consolidate workload to take advantage of underutilized servers

Switch off physical resources to gain energy savings

Novelty:

“We consider workload migration by choosing groups of VMs rather than the entire workload of a data center. Moreover, we analyze the effects of the data center network topology on energy consumption, when choosing the virtual machines to be migrated.”

da Silva, Rodrigo AC, and Nelson LS da Fonseca. "Energy-Aware Migration of Groups of Virtual Machines in Distributed Data Centers."Global Communications Conference (GLOBECOM), 2016 IEEE. IEEE, 2016.

Topology-aware VM selection

Migration algorithm

Migration decisions involve two steps:

Selection (SEL) algorithm: selection of potential sets of VMs in a data center to be migrated. SEL runs in source DCs. Output of the SEL algorithm is used by NEG algorithm.

Negotiation (NEG) algorithm: negotiation of migration of these potential sets with other data centers. NEG runs in destination DCs (potential host DCs)

SEL algorithm

For all sizes, find out all possible sets

Notations

NEG algorithm

Set with MAX savings

Remaining time has to begreater than down time

Performance evaluation

• Topology-aware threshold (TT): considers topology correlation when migration

• Random Threshold (RT): migrates random VM, no correlation

• TT and TR policies always choose a fixed fraction (10%)ofthe workload of the data center

• Algorithm is run 8 hours interval, to minimize large transfers across backbone network

Server and VM configuration

Network topology

Data center configuration

Energy consumption model

Three components:

Servers: Idle power 70% of full load power. Linearly grows with load.

Switches: Chassis, line cards and ports.

ri = Potential transmission rate.

Cooling infrastructure: Derived from PUE.

Power consumption

Traffic model

• Group size: medium and large

• Traffic intensity: low, medium, high

V. Paxson, “Fast, approximate synthesis of fractional gaussian noise for generating self-similar network traffic,” SIGCOMM Comput. Commun. Rev., vol. 27, no. 5, pp. 5–18, Oct. 1997

Results(1)

Results(2)

Questions?

http://www.linkedin.com/in/kmsabidurrahman/krahman@ucdavis.edu