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B99705021 資管三 李奕德 http://ppt.cc/41rH. improving the Scalability of Data Center Networks with Traffic-aware Virtual Machine Placement. Outline . Introduction Background Virtual machine placement Algorithm Algorithm evaluation Result Discussion and future work. introduction.

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b99705021 http ppt cc 41rh
B99705021 資管三 李奕德

http://ppt.cc/41rH

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

outline
Outline
  • Introduction
  • Background
  • Virtual machine placement
  • Algorithm
  • Algorithm evaluation
  • Result
  • Discussion and future work
introduction
introduction
  • Scalability issue
  • Aim to solve different problem

- Dcell, Bcube, PortLand, VL2……

  • No thinking of traffic issue

- high traffic from end to end

introduction1
introduction
  • 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
introduction2
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- 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
outline1
Outline
  • Introduction
  • Background
  • Virtual machine placement
  • Algorithm
  • Algorithm evaluation
  • Result
  • Discussion and future work
background traffic analysis
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 analysis1
Background- traffic analysis
  • Uneven between VMs
  • 80% of VM’s traffic < 800kb/sec
  • 4% of VM’s traffic > 8mb/sec
background traffic analysis2
Background- traffic analysis
  • Stays almost the same
background traffic analysis3
Background- traffic analysis
  • Low correlation between average pairwise traffic rate & end-to-end cost
  • Correlation : -0.32
outline2
Outline
  • Introduction
  • Background
  • Virtual machine placement
  • Algorithm
  • Algorithm evaluation
  • Result
  • Discussion and future work
virtual machine placement cost function
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 function1
Virtual machine placement- cost function
  • is equivalent of finding
  • Dummy VM is assigned when no. slot > no. VM
virtual machine placement complexity
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
outline3
Outline
  • Introduction
  • Background
  • Virtual machine placement
  • Algorithm
  • Algorithm evaluation
  • Result
  • Discussion and future work
algorithm
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 complexity
Algorithm - complexity
  • Complexity determine by SlotClustering and VMMinKcut
  • Slotclustering: O(nk)
  • VMMinKcut: O(n4)
  • Total complexity = O(n4)
outline4
Outline
  • Introduction
  • Background
  • Virtual machine placement
  • Algorithm
  • Algorithm evaluation
  • Result
  • Discussion and future work
algorithm evaluation cluster and cut
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
outline5
Outline
  • Introduction
  • Background
  • Virtual machine placement
  • Algorithm
  • Algorithm evaluation
  • Result
  • Discussion and future work
result
Result
  • Cost matrix
  • Compare with random assign
result1
Result
  • Traffic is assumed to be in normal distribution
  • Variance is change to show difference
  • Different architecture & variance affect result
result2
Result
  • View as VM cluster
  • GLB prediction
result3
Result
  • GLB prediction VS. optimal solution
conclusion
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
outline6
Outline
  • Introduction
  • Background
  • Virtual machine placement
  • Algorithm
  • Algorithm evaluation
  • Result
  • Discussion and future work
discussion and future
Discussion and future
  • Dynamic VM placement
  • Other VM placement with different goal