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Multi-Agent Negotiation of Virtual Machine Migration Using the - - PowerPoint PPT Presentation

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KES AMSTA 2012 Multi-Agent Negotiation of Virtual Machine Migration Using the Lightweight Coordination Calculus Paul Anderson <dcspaul@ed.ac.uk> Shahriar Bijani <S.Bijani@sms.ed.ac.uk> Alexandros Vichos Friday, 22 June 2012

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SLIDE 1

Using the Lightweight Coordination Calculus

Paul Anderson <dcspaul@ed.ac.uk> Shahriar Bijani <S.Bijani@sms.ed.ac.uk> Alexandros Vichos

Multi-Agent Negotiation of Virtual Machine Migration

KES AMSTA 2012

Friday, 22 June 2012

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SLIDE 2

Text

Specification Current State Requirements

Text

Final State Plan Deployment

Friday, 22 June 2012

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SLIDE 3

Centralised Configuration?

◼ Centralised configuration

  • allows a global view with complete knowledge

◼ But ...

  • it is not scalable
  • it is not robust against communication failures
  • federated environments have no obvious centre
  • different security policies may apply to different

subsystems

◼ The challenge ...

  • devolve control to an appropriately low level
  • but allow high-level policies to determine the behaviour

Friday, 22 June 2012

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SLIDE 4

◼ Distributed configuration with centralised policy ◼ Subsystem-specific mechanisms

GPrint (2003)

PRINT CONTROLLER

Print Manager Print Monitor SmartFrog Daemon SLP printer announcements

GLOBUS SERVER

Gprint OGSA Portal

PRINT SERVER

SmartFrog Daemon LCFG lpd component Print Server

Printer

Heartbeat SLP print queue announcements LCFG LCFG LCFG Friday, 22 June 2012

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SLIDE 5

“OpenKnowledge” & LCC

◼ Agents execute “interaction models” ◼ Wrien in a “lightweight coordination calculus” (LCC) ◼ This provides a very general mechanism for doing

distributed configuration

◼ Policy is determined by the interaction models

themselves which can be managed and distributed from a central point of control

◼ The choice of interaction model and the decision to

participate in a particular “role” remains with the individual peer

  • and hence, the management authority

Friday, 22 June 2012

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SLIDE 6

A Simple LCC Example

a(buyer, B) :: ask(X) => a(shopkeeper, S) then price(X,P) <= a(shopkeeper, S) then buy(X,P) => a(shopkeeper, S) ← afford(X, P) then sold(X,P) <= a(shopkeeper, S)

a(shopkeeper, S) :: ask(X) <= a(buyer, B) then price(X, P) => a(buyer, B) ← in_stock(X, P)then buy(X,P) <= a(buyer, B) then sold(X, P) => a(buyer, B)

Friday, 22 June 2012

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SLIDE 7

An Example: VM Allocation

◼ Policy 1 - power saving

  • pack VMs onto the minimum number of physical machines

◼ Policy 2 - agility

  • maintain an even loading across the physical machines
  • verloaded

underloaded migrate

Friday, 22 June 2012

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SLIDE 8

A Prototype

Virtual Machines OK peer Physical machine libvirt OS Distributed Discovery Service Interaction models Components Peer matching Virtual Machines OK peer Physical machine libvirt OS

Friday, 22 June 2012

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SLIDE 9

An Idle Host

a(idle, ID1) :: null ← overloaded(Status) then a(overload(Status), ID1) ) or ( null ← underloaded(Status) then a(underload(Status), ID1) ) or ( a(idle, ID1) )

Friday, 22 June 2012

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SLIDE 10

An Overloaded Host

a(overloaded(Need), ID2) :: readyToMigrate(Need) => a(underloaded, ID3) then migration(OK) <= a(underloaded, ID3) then null ← migration(ID2, ID3) then a(idle, ID2)

Friday, 22 June 2012

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SLIDE 11

An Underloaded Host

a(underloaded(Capacity), ID3) :: readyToMigrate(Need) <= a(overloaded, ID2) then migration(OK) => a(overloaded, ID2) ← canMigrate(Capacity, Need) then null ← waitForMigration() then a(idle, ID3)

Friday, 22 June 2012

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SLIDE 12

Migration Example

1 2 3 4 5 6 7 8 9 3 4 5 2 6 7 1 8 9 PM1 PM2 PM3 PM4 PM1 PM2 PM3 PM4

Friday, 22 June 2012

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SLIDE 13

Simulation

5000 10000 15000 20000

Time (ms)

50 100 150 200

Physical Machine Load Average (%)

120% average load 80% average load

Friday, 22 June 2012

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SLIDE 14

Protocol Issues

◼ The simple protocols described here are very naive

  • But the flexibility of the framework is the real contribution
  • LCC can easily be used to implement more sophisticated

protocols - such as “auctions” which are ideal for many configuration scenarios

◼ The protocols so far rely rather heavily on the

“discovery service”

  • This seems against the spirit of peer negotiation
  • We are investigating other protocols which (for example)

search for suitable exchange candidates by passing requests to a local peer group who will then forward them if they cannot satisfy them

Friday, 22 June 2012

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SLIDE 15

Measurement Issues

◼ Unpredictability of virtual machine performance is a

significant problem

  • Latency is high for machine migrations
  • We are looking at machine learning techniques to identify

(for example) “stable” and “unstable” machines

◼ In practice, machine “load” is multi-dimensional

  • We may want to consider cpu usage, memory usage and

network (for example)

  • we are looking at ways of incorporating different factors

Friday, 22 June 2012

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SLIDE 16

General Issues

◼ We would like to evaluate the approach in a more

production environment, but ...

  • Handling errors and timeouts in an unreliable distributed

system is hard

  • We have been using a research implementation of lcc which

is not very robust

◼ Some things are hard to do without global knowledge

  • balance the system so that all the machines have exactly

the same load?

Friday, 22 June 2012

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SLIDE 17

Using the Lightweight Coordination Calculus

Paul Anderson <dcspaul@ed.ac.uk> Shahriar Bijani <S.Bijani@sms.ed.ac.uk> Alexandros Vichos

Multi-Agent Negotiation of Virtual Machine Migration

KES AMSTA 2012

Friday, 22 June 2012