Virtual machine placement
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Transcript Virtual machine placement
B99705021 資管三 李奕德
http://ppt.cc/41rH
Introduction
Background
Virtual machine placement
Algorithm
Algorithm evaluation
Result
Discussion and future work
Scalability issue
Aim to solve different problem
- Dcell, Bcube, PortLand, VL2……
No thinking of traffic issue
- high traffic from end to end
1.
2.
3.
three character of all traffic
average pairwise traffic rate & end-to-end
cost has low correlation
Uneven between VMs
Stays almost the same
Traffic-aware placement may be beneficial
Traffic-aware VM Placement Problem
(TVMPP)
given: traffic matrix , cost matrix
Goal: minimize cost
Cost can be: Total switch used/Compute Time
An algorithm that solve the NP-hard problem
Architecture difference
NP: by nondeterministic algorithms in
polynomial time
nondeterministic
-Every “guess by hunch” is right
at least as hard as the hardest problems in NP
Introduction
Background
Virtual machine placement
Algorithm
Algorithm evaluation
Result
Discussion and future work
Data set I :
IBM Global Services’ data warehouse
About 17000 virtual machines
Data set II:
Server cluster
About Hundreds of virtual machines
round-trip latency measurement at 68 VM
Uneven between VMs
80% of VM’s traffic < 800kb/sec
4% of VM’s traffic > 8mb/sec
Stays almost the same
Low correlation between average pairwise
traffic rate & end-to-end cost
Correlation : -0.32
Old style
VL2
Portland
Bcube
Introduction
Background
Virtual machine placement
Algorithm
Algorithm evaluation
Result
Discussion and future work
n VM to assign
n slot for VM
static and single-path routing
Cost and traffic matrix from historical data
Cost
D C e g
i , j 1,...,n
ij
i
j
i 1,...,n
i
i
is equivalent of finding
min tr DX T C T X eX T g T
X
Dummy VM is assigned when no. slot > no.
VM
Quadratic Assignment Problem (NP-hard)
Impossible to find optimality when size > 15
TVMPP is a special case of QAP
reduction from Balanced Minimum K-cut
Problem (BMKP)
BMKP: extended problem from the Minimum
Bisection Problem (MBP)
BMKP & MBP are NP-hard
Introduction
Background
Virtual machine placement
Algorithm
Algorithm evaluation
Result
Discussion and future work
approximation algorithm Cluster-and-Cut
Divide VM into VM cluster
Divide slot into slot cluster
Put VM cluster into slot cluster
A smaller problem
Feasible when size is sufficient small
Complexity determine by SlotClustering and
VMMinKcut
Slotclustering: O(nk)
VMMinKcut: O(n4)
Total complexity = O(n4)
Introduction
Background
Virtual machine placement
Algorithm
Algorithm evaluation
Result
Discussion and future work
Cluster and cut VS. other benchmark
algorithms
Local Optimal Pairwise Interchange (LOPI)
Simulated Annealing (SA)
hybrid traffic model
Gravity model
compute the GLB for each settings
Introduction
Background
Virtual machine placement
Algorithm
Algorithm evaluation
Result
Discussion and future work
Cost matrix
Compare with random assign
Traffic is assumed to be in normal distribution
Variance is change to show difference
Different architecture & variance affect result
View as VM cluster
GLB prediction
GLB prediction VS. optimal solution
Thing that brings better performance:
- bigger variance
- smaller cluster (less VM in a group)
- Architecture difference
(generally) Bcube > tree > fat-tree > VL2
Good scenario: multiple service in a data
center
Bad scenario: single service / map-reduce
Introduction
Background
Virtual machine placement
Algorithm
Algorithm evaluation
Result
Discussion and future work
Dynamic VM placement
Other VM placement with different goal
Thank you for your attention