CSE598C Project: Dynamic virtual server placement

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CSE598C ProjectDynamic virtual server placement

• Yoojin Hong

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Mobile VM servers (From the vMatrix)

• Static mirroring vs. Mobile virtual machine
  servers

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Mobile VM servers

• Basic ideas
• VM servers can be hosted in any real machines
  across networks
• VM servers can be instantiated on demand
• VM servers can move closer to end users
• Advantages of mobile virtual machine servers
• Higher availability
• Better response time
• Absorbing flash crowds
• Network bandwidth savings
• Lower cost of ownership of server machines

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Mobile VM servers

• Two-tier architectures

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Mobile VM servers

• Disadvantages of mobile virtual machine servers
• Difficulty to virtualize large size of database
  server
• Closer front-end VM server is beneficial only
  when of packets via connection A is larger than
  that via connection B
• Overhead of migrating OS and applications for VM
  servers

Connection A
Connection B
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Scenarios

• The service is provided mainly by
  compute-intensive application server
• Locations of majority of end users are changing
• Suppose users of CNN.com has an access pattern to
  visit the site around 4-5p.m.
• Time difference between east coast and west coast
  

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Problem description

• Dynamic provisioning Placement of web server
  replicas
• Problems
• Determine when a new VM server needs to be added
  (When)
• Change of locations of end users
• Select a real machine, which is located in an
  optimal location from end users, to host the new
  VM server (How)
• Location of real machines available
• Location of end users
• Location of back-end server relative to real
  machines
• Size of VM files relative to size of server
  requests
• Cost of bandwidth during different times of day
• Location of real machines to host VM servers
  currently

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Algorithms

• When
• When locations of end users are changing
• of users located where current server cannot
  guarantee a certain response time is increasing

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Algorithms

• How Modified k-mean algorithm
• 1. Find k number of centroids at random
• 2. Assign each end user location to its closest
  centroid
• 3. Update the centroids as follows
• of end user locations assigned to
  cluster j
• location of end user j
• application-specific constant weight (
  0)
• B location of back-end server
• 4. Repeat 2, 3 steps until the centroids are
  converged
• 5. Select RMs located closest to the centroids

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Experiments

• Simulation
• Comparison