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Mobility
Presented by:
Mohamed Elhawary
Mobility
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Distributed file systems increase availability
Remote failures may cause serious troubles
Server replication (higher quality)
Client replication, inferior, needs periodic
revalidation
Cache coherence to combine performance,
availability and quality
Mobility (Cont’d)
Client server
as Coda
Pairwise as
Bayou
Two level
hierarchy as
Oracle
consistent
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strong
locks
low availability
weakly
cache coherence
high availability
connected
weakly connected
application adaptation
disconnected
optimistic
data exchange
pessimistic
operation exchange
Disconnected Operation in
Coda
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Enable clients to continue accessing
data during failures
Cashing data on the clients with right
back upon reconnection
Designed for a large num. of clients and
a smaller num. of servers
Server replication and call back break
Preserve transparency
Scalability
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Whole file cashing, cash miss on open
only
Simple but involve communication
overhead
Placing functionality on the clients
unless violating security or integrity
Avoids system wide rapid change
Optimistic replica control
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Disconnection is involuntary
Extended disconnections
Leases place time bound on locks but
disconnected client loses control after
expiration
Low degree of write sharing in Unix
Applied to server replication for
transparency
Client structure
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Venus is a user level
process
Works as a cache
manager
Venus States
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Hoarding state: normal
operation relying on
server replication
Reintegration state:
resynchronize its state
Venus can be in
different states with
respect to different
volumes
Hoarding
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Hoard useful data in anticipation of
disconnection
Combines implicit and explicit source of
information
File reference behavior can’t be predicted
Disconnections are unpredictable
Priority of cached objects is a function of user
given priority (unsafe) and recent usage
(hard to manage)
Hierarchical cache management
Hoard walking
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Periodically restores cache equilibrium
Unexpected disconnection before a
scheduled walk is a problem
Revaluate name bindings
Revaluate priorities of all entries
Solve the callback break problem
Policies for directories and files
Emulation
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Venus functions as a pseudo server
Generates temporary file identifiers
Behaves as faithful as possible, but no
guarantee
Cache entries of modified assume infinite
priority, except when cache is full
Use a replay log
Use nonvolatile storage
Meta data changes through atomic
transactions
Reintegration
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Venus propagates changes made in emulation
Update suspended in the volume being
integrated until completion, it may take time!
Replace temporary ids with permanent ones,
but may be avoided
Ship replay log
Execute replay as a single transaction, is that
fair?
Replay algorithm
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Log is parsed
Log is validated using store id
Store create a shadow file and data transfer
is deferred
Data transfer
Commits and release locks
In case of failures, user can replay selectively
Using dependence graphs may enhance
reintegration
Evaluation
Evaluation (cont’d)
Evaluation (cont’d)
Flexible update propagation in
Bayou
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Weekly consistent replication can
accommodate propagation policies
Don’t assume client server as in Coda
Exchange update operations and not
the changes, suite low B/W networks
Relies on the theory of epidemics
Storage vs. bandwidth
Basic Anti-entropy
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Storage consists of an ordered log of updates
and a database
Two replicas bring each other up-to-date by
agreeing on the set of writes in their logs
Servers assigns monotonically increasing
accept stamp to new writes
Write propagates with stamps and server id
Partial accept order to maintain the prefix
property
Needs to be implemented over TCP
Basic Anti-entropy (Cont’d)
Effective write-log
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Each replica discard stable writes as a pruned
prefix from write log
They may have not fully propagated and may
require full database transfer
Storage bandwidth tradeoff
Assigns monotonically increasing CSN to
committed writes and infinity to tentative
writes
Write A precedes write B if…
Anti-entropy with com. writes
Write log truncation
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To test if a server is missing writes, S.O
version vector
Penalty involve database transfer and
roll back
Write log truncation (Cont’d)
Session guarantees
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Causal order as a refinement of accept
order
Each server maintains a logical clock
that advances with new writes
Total order through <CSN, accept order,
server id>
Light weight creation and
retirement
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Versions vectors are updated to include
or exclude servers
Creation and retirement are handled as
a write that propagates via anti-entropy
<Tk,i , Sk> is Si ’s server id
Tk,i + 1 initialize accept stamp counter
Linearly creation increase id’s and
version vectors dramatically
Disadvantages
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When num of replicas is larger than
update rate, cost of exchanging version
vectors is high
If the update rate is larger than the
commit rate, the write log will grow
large
Policies
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When to reconcile
Which replicas to reconcile
How to truncate the write log
Selecting a server to create a new
replica
Depend on network characteristics and
threshold values
Evaluation
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Experiments were taken for an e-mail
application, how far is that really represent
the general dist. File system
Only committed writes are propagated
Sparc running SunOS (SS)
486 laptops running linux (486)
3000 and 100 byte messages between 3
replicas
520 public key overheads and 1316 bytes
update schema and padding
Execution vs. writes
propagated
Execution for 100 writes
Network independent
Effect of server creations
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When servers created from initial one,
20K version vector for 1000 servers
When linearly created, 4 MB version
vectors for 1000 servers
Time to test if a write is among those
represented by version vector may grow
cubically if linearly created
Conclusion
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Disconnected operation through Cedar
and PCMAIL is less transparent than
Coda
Golding’s time stamped anti entropy
protocol is similar to bayou, with
heavier weight mechanism to create
replicas and less aggressive to claim
storage