Client Cache Management

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Client Cache Management

• Improving the broadcast for one probability
  access distribution will hurt the performance of
  other clients with different access
  distributions.
• Therefore the client machines need to cache pages
  obtained from the broadcast.

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Client Cache Management

• With traditional caching clients cache the data
  most likely to be accessed in the future.
• With Broadcast Disks, traditional caching may
  lead to poor performance if the servers
  broadcast is poorly matched to the clients access
  distribution.

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Client Cache Management

• In the Broadcast Disk system, clients cache the
  pages for which the local probability of access
  is higher than the frequency of broadcast.
• This leads to the need for cost-based page
  replacement.

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Client Cache Management

• One cost-based page replacement strategy replaces
  the page that has the lowest ratio between its
  probability of access (P) and its frequency of
  broadcast (X) - PIX
• PIX requires the following
• 1 Perfect knowledge of access probabilities.
• 2 Comparison of PIX values for all cache resident
  pages at cache replacement time.

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Example

• One page is accessed 1 of the time at a
  particular time and is also broadcast 1 of the
  time.
• Second page is accessed only 0.5 but is
  broadcast 0.1 of time
• Which page to be replaced, 1st will be replaced
  in favor of 2

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Client Cache Management

• Another page replacement strategy adds the
  frequency of broadcast to an LRU style policy.
  This policy is known as LIX.
• LIX maintains a separate list of cache-resident
  pages for each logical disk
• A page enters the chain corresponding to the disk
  in which it is broadcast
• Each list is ordered based on an approximation of
  the access probability (L) for each page.

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Cont.

• When a page is hit, it is moved to top of chain
  and when a new page is entered gt
• A LIX value is computed by dividing L by X, the
  frequency of broadcast for the page at the bottom
  of each chain
• The page with the lowest LIX value is replaced.

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Prefetching

• PIX/LIX for only demand driven pages
• An alternative approach to obtaining pages from
  the broadcast.
• Goal is to improve the response time of clients
  that access data from the broadcast.
• Methods of Prefetching
• Tag Team Caching
• Prefetching Heuristic

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Prefetching

• Tag Team Caching - Pages continually replace each
  other in the cache.
• For example two pages x and y, being broadcast,
  the client caches x as it arrives on the
  broadcast. Client drops x and caches y when y
  arrives on the broadcast.

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• Expected delay in demand driven
• Suppose a client is interested in accessing X and
  Y and Px Py 0.5 with one single slot for
  cache
• In demand driven , cache X and if needs Y, wait
  for Y and replace the cache by Y
• Expected delay on a cache miss is ½ of the
  rotation of the disk
• Expected delay over all accesses is
• CiMiDi, where C is the access probability, M
  is the probability of cache miss and D is the
  expected broadcast delay for page i
• For pages x and y, it is 0.5 0.50.5
  0.50.50.5 0.25

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Expected Delay in Tag-team caching

• 0.50.50.25 0.50.50.25 0.125, that is
  average cost is ½ of the demand driven scheme
• Why a miss can occur at any time in demand
  driven whereas misses can only occur during a
  half of the broadcast

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Prefetching

• Simple Prefetching Heuristic
• Performs a calculation for each page that arrives
  on the broadcast based on the probability of
  access for the page (P) and the amount of time
  that will elapse before the page will come around
  again (T).
• If the PT value of the page being broadcast is
  higher than the page in cache with the lowest PT
  value, then the page in cache is replaced.

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Example
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