improving the Scalability of Data Center Networks with Traffic-aware Virtual Machine Placement
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Transcript of improving the Scalability of Data Center Networks with Traffic-aware Virtual Machine Placement
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improving the Scalability of Data Center Networks with Traffic-aware Virtual Machine Placement
B99705021 資管三 李奕德http://ppt.cc/41rH
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Outline
Introduction Background Virtual machine placement Algorithm Algorithm evaluation Result Discussion and future work
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introduction
Scalability issue Aim to solve different problem
- Dcell, Bcube, PortLand, VL2…… No thinking of traffic issue - high traffic from end to end
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introduction
three character of all traffic1. average pairwise traffic rate & end-to-end
cost has low correlation2. Uneven between VMs3. Stays almost the same Traffic-aware placement may be beneficial
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introduction
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
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NP- hard
NP: by nondeterministic algorithms in polynomial time
nondeterministic -Every “guess by hunch” is right
at least as hard as the hardest problems in NP
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Outline
Introduction Background Virtual machine placement Algorithm Algorithm evaluation Result Discussion and future work
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Background – traffic analysis
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
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Background- traffic analysis
Uneven between VMs
80% of VM’s traffic < 800kb/sec 4% of VM’s traffic > 8mb/sec
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Background- traffic analysis
Stays almost the same
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Background- traffic analysis
Low correlation between average pairwise traffic rate & end-to-end cost
Correlation : -0.32
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Background - Achitecture
Old style
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Background - Achitecture
VL2
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Background - Achitecture
Portland
Bcube
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Outline
Introduction Background Virtual machine placement Algorithm Algorithm evaluation Result Discussion and future work
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Virtual machine placement- cost function
n VM to assign n slot for VM static and single-path routing Cost and traffic matrix from historical data
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Virtual machine placement- cost function
is equivalent of finding
Dummy VM is assigned when no. slot > no. VM
ini
inji
jiij geCDCost
,...,1,...,1,
TTTT
XgeXXCDXtr
min
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Virtual machine placement- complexity
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
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Outline
Introduction Background Virtual machine placement Algorithm Algorithm evaluation Result Discussion and future work
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Algorithm
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
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Algorithm – pseudo code
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Algorithm – pseudo code
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Algorithm - complexity
Complexity determine by SlotClustering and VMMinKcut
Slotclustering: O(nk) VMMinKcut: O(n4) Total complexity = O(n4)
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Outline
Introduction Background Virtual machine placement Algorithm Algorithm evaluation Result Discussion and future work
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Algorithm evaluation- cluster and cut
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
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Algorithm evaluation - result
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Outline
Introduction Background Virtual machine placement Algorithm Algorithm evaluation Result Discussion and future work
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Result
Cost matrix
Compare with random assign
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Result
Traffic is assumed to be in normal distribution Variance is change to show difference
Different architecture & variance affect result
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Result
View as VM cluster GLB prediction
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Result
GLB prediction VS. optimal solution
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conclusion
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
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Outline
Introduction Background Virtual machine placement Algorithm Algorithm evaluation Result Discussion and future work
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Discussion and future
Dynamic VM placement Other VM placement with different goal
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Q&AThank you for your attention