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Mobile Computing

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  1. Mobile Computing Panos Papadimitratos Wireless Networks Lab Department of Electrical Engineering Cornell University

  2. Problem Context

  3. Mobile Computing Environment • Limited Bandwidth • High Latency • Intermittent Connectivity • Lower Reliability • Low Physical Security • Lower Processing Capability • Higher Degree of Heterogeneity

  4. Despite the adversity.. • Run Distributed Applications • Provide Distributed Services • Share Data • Remain Consistent • Remain Efficient

  5. Why are things more difficult? • Connectivity is NOT continuous • Topological Changes • Less Resources • Consequently: • Lower Availability • Potential Inconsistencies

  6. Two aspects • “…Replicated, Highly Available, Weakly Consistent Storage System…” • “Develop Mobile Applications … minimize the dependence upon continuous connectivity…”

  7. Bayou • Distributed Data Storage System • Designed for a Mobile Computing Environment • Non-Transparent • Weakly-Consistent Replication • Application-Specific Mechanisms to Detect & Resolve Conflicts • Low Usage of the Network

  8. Previous Work • Theory of Epidemics • Eventual Consistency • Coda • Disconnected Operation • Optimistic Replication • Consistency • Application-specific resolvers • Conflicts resolution based on file type • Log unresolved conflicts, create error message • Ficus • Notes, Oracle, MS Access

  9. System Model • Client/Server Architecture -Transactional System • Data are replicated to a set of servers • Applications run as clients • Two Basic Operations: Read and Write • Replication is Weakly Consistent • Read-Any-Write-Any Model

  10. System Model Server State Storage System Storage System Server State Anti-Entropy Read or Write Application Bayou API Client Stub Server State Storage System Application Bayou API Client Stub

  11. Conflict Detection & Resolution • Application-Specific • Notion of Conflict • Semantics • Granularity – example: Scheduling Application • Resolution Policy • Semantics • Automated Mechanisms • Dependency Checks • Merge Procedures

  12. Dependency Checks • Application-Supplied Query & Expected Result • Query is Run at the Server against its current data • If Check Fails, invoke Merge procedure

  13. Merge Procedures • High-level programs with application-specific knowledge • Run by the Server • Performed Atomically as part of Writes • Attempt to Resolve the Conflict • Produce a Revised Update to Apply

  14. Handling Conflicts – An Example

  15. Basic Anti-Entropy • Goal: the reconciliation of replicas’ data • Pair-wise manner • One-way Operation • Propagate Write Operations • Accept-Order Constraint • Prefix Property • Version Vectors

  16. Basic Anti-Entropy (continued) S R.V Version Vector All Writes unknown to R R For each w in S.Write_log if (R.V(w.server_ID) < w.accept_stamp) SendWrite(R,w)

  17. A More Reasonable Approach • Without an ever-growing Write Log • Need a method for Truncating the Write Log • Idea: An Update that is received by all Replicas need not be logged any more. • Allow for an independent, aggressive pruning by each Replica • The notion of Stable or Committed Write is pivotal in the pruning process

  18. Write Stability • Stable Write: iff it has been executed for the last time by a server. • Intuitively equivalent to Confirmation or Commitment • Primary Commit Scheme • Designate a Replica as Primary • Primary determines the order (position) of a Write when it first receives it. • Stable Order • Any Non-CommittedWrite is called Tentative

  19. R.CSN Highest Commit Sequence Number R.V Version Vector First, All Committed Writes unknown to R Second, All Tentative Writes unknown to R if R.CSN < S.CSN for each w in S.Write_log if (w.accept_stamp < R.V(w.server_ID)) SendCommitNotification(…) else SendWrite(…) For each w in S.Write_log if (R.V(w.server_ID) < w.accept_stamp) SendWrite(R,w) Anti-Entropy (Revisited) S R

  20. Write-Log Truncation • Stable Order maintains the Prefix Property • Replicas can truncate any stable prefix from their Write Logs • Incremental Reconciliation may not be possible • Each Replica needs to remember the omittedWrite Operations • Full-Database Transfer

  21. ‘Extended’ Anti-Entropy • Session Guarantees • Causal Order – Accept Stamp • Reduce Client-Observed inconsistencies • Eventual Consistency • Define a Total Order using the Server ID and the Causal Order • Propagate Updates in this Total Order • Provide Guarantees on the ‘quality’ of the Replicas Data Content

  22. Other issues • Light-Weight Server Creation • Security • Update through transportable storage media, i.e. floppy disks • Link quality determines the frequency of the performed anti-entropies

  23. Experiments • Measurements on a modified EXMH (e-mailer) that uses Bayou for storing messages • Only Committed Writes are propagated • Measure the execution time for an Anti-Entropy (100 Writes) over different network links • Network Transfer • Inserting Newly Received Writes

  24. Experiments - II

  25. Bayou - Summary • Support for Arbitrary Communication Topologies • Operation over Low-Bandwidth Networks • Incremental Progress • Eventual Consistency • Efficient Storage Management • Propagation through Transportable Media • Light-weight Management of Dynamic Replica Sets • Arbitrary Policy Choices

  26. Rover Toolkit • Set of Software Tools for Development of Mobile Applications • Two approaches: • Mobile-Transparent Applications • Mobile-Aware Applications

  27. Goals: • Minimize Dependence on • Continuous Connectivity • Remotely Stored files • Optimize Utilization of Bandwidth • Dynamic Division of Work

  28. Previous Work • Cedar • Check-in Check-out Data Sharing • Locus • Type-specific Conflict Resolution • Coda • Optimistic Concurrency Control • Pre-Fetching • Bayou • Tentative Data • Session Guarantees

  29. Toolkit Design • Client-Server architecture • Mobile Communication Support • Re-locatable Dynamic Objects (RDO) • Reduce Client/Server communication • Update Shared Objects • Code Shipping • Queued Remote Procedure Call (QRPC) • Non-Blocking Calls When Disconnected

  30. Toolkit Design • Application code & data are RDOs • Rover-Applications Interface Primary Functions • Create Session • Import • Invoke • Export • RDOs are cahced • RDOs are lazily fetched

  31. Toolkit Design Client-Side Application Modify/ Resolve Object Conflict? Rover Library Import RDO RDO Cache RDO Network Scheduler Export Log QRPC Log Mobile Host Server Resolved Log

  32. Design Issues • Communication Scheduling • Computation Relocation • Separate application from data • Move computation/data: client server • Object Replication – Pre-fetching • Consistency • Primary Copy, Tentative-Update Optimistic Concurrency Control • Type-Specific Concurrency Control

  33. Architecture App3 App 1 App 2 App3 App 1 App 2 Access Manager Operation Log Access Manager Operation Log Object Cache Object Cache Network Scheduler Network Scheduler Server Mobile Host Network

  34. Implementation Issues • Rover starts as a minimal kernel • Failure Recovery – Access Manager • Log Size • Batching of QPRCs • Promises – Callback • User Notification • Application-Specific Conflict Resolution

  35. Experiments • Single Server, Multiple Clients • Different Network Options • TCP over wireless links • Three setups: • Compressed or Batched QRPCs • Mobile-Transparent Application • Mobile-Aware Applications

  36. Experiments - II

  37. Experiments - III

  38. Experiments - IV

  39. Rover - Summary • QRPC benefits: • RPCs are scheduled, batched, compressed • Increased Network Performance • RDOs migrate functionality • Minimize Data Transfer • Porting of Applications to Rover is relatively easy • Measurements show significant improvement from both approaches

  40. Topics for Discussion • Are there ‘missing’ features? • What if the semantics are not that ‘strong’? • Or, if the uncertainty about data values is not accepted? • Should Rover support some replication service? • Do we really know what should be an ‘interesting’ mobile application ?

  41. Topics for Discussion - II • In other words, are the assumptions made reasonable ? • How secure are these architectures ? How about the ‘mobile’ data ? • Nomadic Computing: Can these schemes support Nomads ? • Other peer-to-peer models? E.g. Sensor Networks?