improving the Scalability of Data Center Networks with Traffic-aware Virtual Machine Placement

B99705021 資管三 李奕德http://ppt.cc/41rH

Outline

Introduction Background Virtual machine placement Algorithm Algorithm evaluation Result Discussion and future work

introduction

Scalability issue Aim to solve different problem

- Dcell, Bcube, PortLand, VL2…… No thinking of traffic issue - high traffic from end to end

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

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

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

Outline

Introduction Background Virtual machine placement Algorithm Algorithm evaluation Result Discussion and future work

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

Background- traffic analysis

Uneven between VMs

80% of VM’s traffic < 800kb/sec 4% of VM’s traffic > 8mb/sec

Background- traffic analysis

Stays almost the same

Background- traffic analysis

Low correlation between average pairwise traffic rate & end-to-end cost

Correlation : -0.32

Background - Achitecture

Old style

Background - Achitecture

VL2

Background - Achitecture

Portland

Bcube

Outline

Introduction Background Virtual machine placement Algorithm Algorithm evaluation Result Discussion and future work

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

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

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

Outline

Introduction Background Virtual machine placement Algorithm Algorithm evaluation Result Discussion and future work

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

Algorithm – pseudo code

Algorithm – pseudo code

Algorithm - complexity

Complexity determine by SlotClustering and VMMinKcut

Slotclustering: O(nk) VMMinKcut: O(n4) Total complexity = O(n4)

Outline

Introduction Background Virtual machine placement Algorithm Algorithm evaluation Result Discussion and future work

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

Algorithm evaluation - result

Outline

Introduction Background Virtual machine placement Algorithm Algorithm evaluation Result Discussion and future work

Result

Cost matrix

Compare with random assign

Result

Traffic is assumed to be in normal distribution Variance is change to show difference

Different architecture & variance affect result

Result

View as VM cluster GLB prediction

Result

GLB prediction VS. optimal solution

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

Outline

Introduction Background Virtual machine placement Algorithm Algorithm evaluation Result Discussion and future work

Discussion and future

Dynamic VM placement Other VM placement with different goal

Q&AThank you for your attention