migration of groups of virtual machines in distributed data centers to reduce cost
TRANSCRIPT
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/
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/[email protected]