Mobility Presented by: Mohamed Elhawary

Mobility 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

Disconnected Operation in Coda 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 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 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 Venus is a user level process Works as a cache manager

Venus States 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 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 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 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 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 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)

Flexible update propagation in Bayou 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 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 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 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 Causal order as a refinement of accept order Each server maintains a logical clock that advances with new writes Total order through

Light weight creation and retirement Versions vectors are updated to include or exclude servers Creation and retirement are handled as a write that propagates via anti-entropy is S i ’s server id T k,i + 1 initialize accept stamp counter Linearly creation increase id’s and version vectors dramatically

Disadvantages 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 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 Experiments were taken for an 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 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 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