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Pond: the OceanStore Prototype Sean Rhea, Patrick Eaton, Dennis Geels, Hakim Weatherspoon,

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Presentation on theme: "Pond: the OceanStore Prototype Sean Rhea, Patrick Eaton, Dennis Geels, Hakim Weatherspoon,"— Presentation transcript:

1 Pond: the OceanStore Prototype {srhea,eaton,geels,hweather,ravenben,kubitron} Sean Rhea, Patrick Eaton, Dennis Geels, Hakim Weatherspoon, Ben Zhao, and John Kubiatowicz University of California, Berkeley

2 OceanStore Assumptions Untrusted Infrastructure: –The OceanStore is comprised of untrusted components –Only ciphertext within the infrastructure Responsible Party: –Some organization (i.e. service provider) guarantees that your data is consistent and durable –Not trusted with content of data, merely its integrity Mostly Well-Connected: –Data producers and consumers are connected to a high- bandwidth network most of the time –Exploit multicast for quicker consistency when possible Promiscuous Caching: –Data may be cached anywhere, anytime Optimistic Concurrency via Conflict Resolution: –Avoid locking in the wide area –Applications use object-based interface for updates

3 The OceanStore “Vision” HotOS Attendee me Paul Hogan

4 The Challenges Maintenance –Many components, many administrative domains –Constant change –Must be self-organizing –Must be self-maintaining –All resources virtualized—no physical names Security –High availability is a hacker’s target-rich environment –Must have end-to-end encryption –Must not place too much trust in any one host

5 Talk Outline Introduction System Overview –Tapestry –Erasure codes –Byzantine agreement –Putting it all together Implementation and Deployment Performance Results Conclusion

6 The Technologies: Tapestry Tapestry performs Distributed Object Location and Routing From any host, find a nearby… –replica of a data object Efficient –O(log N ) location time, N = # of hosts in system Self-organizing, self-maintaining

7 Two-levels of Routing Fast, probabilistic search for “routing cache”: –Built from attenuated bloom filters –Approximation to gradient search –Not going to say more about this today Redundant Plaxton Mesh used for underlying routing infrastructure: –Randomized data structure with locality properties –Redundant, insensitive to faults, and repairable –Amenable to continuous adaptation to adjust for: Changing network behavior Faulty servers Denial of service attacks

8 Automatic Maintenance All Tapestry state is Soft State –State maintained during transformations of network –Periodic restoration of state Self-Tuning of link structure Dynamic insertion: –New nodes contact small number of existing nodes –Integrate themselves automatically –Later, introspective optimization will move data to new servers Dynamic deletion: –Node detected as unresponsive –Pointer state routed around faulty node (signed deletion requests authorized by servers holding data)

9 The Technologies: Tapestry (con’t.) HotOS Attendee Paul Hogan

10 The Technologies: Erasure Codes More durable than replication for same space The technique: Z WW ZY X f f -1

11 More on Erasure Coding Much higher fault tolerance for same storage cost as replication Divide block into m fragments Encode these into n fragments, where n>m Original object can be reconstructed from any m fragments Rate of encoding r = m/n Prototype uses Cauchy Reed-Solomon code with m=16, n=32

12 More on Erasure Coding Apply an update at the primary replica All newly-created blocks are erasure-coded Resulting fragments are distributed via the Tapestry overlay to OceanStore archive servers for storage To reconstruct a block, host uses Tapestry to discover m fragments and perform decoding A technique that is being used in increasing number of distributed file systems

13 The Technologies: Byzantine Agreement Guarantees all non-faulty replicas agree –Given N =3f +1 replicas, up to f may be faulty/corrupt –All non-faulty participants reach same decision as long as more than 2/3 of participants follow protocol correctly Expensive –Requires O(N 2 ) communication Combine with primary-copy replication –Small number participate in Byzantine agreement –Multicast results of decisions to remainder

14 More on Byzantine Agreement Implement primary replica as small set of cooperating servers called “inner ring” –Primary replica is a virtual resource –Can be mapped to different physical servers at different times Avoid giving single machine complete control over user’s data Inner ring servers use Byzantine-fault-tolerant protocol to agree on all updates to the data object Digitally sign the result

15 More on Byzantine Agreement Requires that participants authenticate messages they send Use symmetric-key message authentication codes (MACs) on inner ring –Authenticate messages between two fixed machines Use public key cryptography to communicate with other machines Digital signature certifies each agreement results So secondary replicas can locally verify authenticity of data received from other replicas Most read traffic can be satisfied by second tier of replicas Generating signatures is expensive; amortized over the number of replicas that receive the result

16 Cached Data Objects Reconstructing a block from erasure codes is expensive Oceanstore also employs whole-block caching When a host reads a block from the archive, it queries Tapestry for block –If not available, reconstructs from fragments Host caches a copy that can then be discovered later by other clients –host publishes its possession of the block in Tapestry Reconstructed blocks are soft state; can be discarded by LRU policies

17 Updating Secondary Replicas Efficient, push-based update of secondary replicas Organized into an application-level multicast tree –Rooted at the primary replica for the object When a primary replica applies an update to create a new version, sends update and heartbeat down dissemination tree –Multicast directly to secondary replicas

18 Putting it all together: the Path of a Write Primary Replicas HotOS Attendee Other Researchers Archival Servers (for durability) Secondary Replicas (soft state)

19 Talk Outline Introduction System Overview Implementation and Deployment Performance Results Conclusion

20 Prototype Implementation All major subsystems operational –Self-organizing Tapestry base –Primary replicas use Byzantine agreement –Secondary replicas self-organize into multicast tree –Erasure-coding archive –Application interfaces: NFS, IMAP/SMTP, HTTP Event-driven architecture –Built on Staged Event-Driven Architecture (SEDA) 280K lines of Java (J2SE v1.3) –JNI libraries for cryptography, erasure coding

21 Deployment on PlanetLab –~100 hosts, ~40 sites –Shared.ssh/authorized_keys file Pond: up to 1000 virtual nodes –Using custom Perl scripts –5 minute startup Gives global scale for free

22 Talk Outline Introduction System Overview Implementation and Deployment Performance Results –Andrew Benchmark –Stream Benchmark Conclusion

23 Performance Results: Andrew Benchmark Built a loopback file server in Linux –Translates kernel NFS calls into OceanStore API Lets us run the Andrew File System Benchmark Andrew Benchmark Linux Kernel Loopback Server Pond Daemon fwrite syscallNFS Write Pond API Msg to Primary Network

24 OceanStore PhaseNFS5121024 I0.92.86.6 II9.416.840.4 III8.31.81.9 IV6.91.5 V21.532.070.7 Total47.054.9120.3 (times in milliseconds) Performance Results: Andrew Benchmark Pond faster on reads: 4.6x –Phases III and IV –Only contact primary when cache older than 30 seconds Ran Andrew on Pond –Primary replicas at UCB, UW, Stanford, Intel Berkeley –Client at UCB –Control: NFS server at UW But slower on writes: 7.3x –Phases I, II, and V –Only 1024-bit are secure –512-bit keys show CPU cost

25 Closer Look: Write Cost Byzantine algorithm adapted from Castro & Liskov –Gives fault tolerance, security against compromise –Fast version uses symmetric cryptography Pond uses threshold signatures instead –Signature proves that f +1 primary replicas agreed –Can be shared among secondary replicas –Can also change primaries w/o changing public key Big plus for maintenance costs –Results good for all time once signed –Replace faulty/compromised servers transparently

26 Closer Look: Write Cost Small writes –Signature dominates –Threshold sigs. slow! –Takes 70+ ms to sign –Compare to 5 ms for regular sigs. Phase 4 kB write 2 MB write Validate0.30.4 Serialize6.126.6 Apply1.5113.0 Archive4.5566.9 Sign Result77.875.8 (times in milliseconds) Large writes –Encoding dominates –Archive cost per byte –Signature cost per write

27 Closer Look: Write Cost (run on cluster)

28 Closer Look: Write Cost Throughput in the wide area: Primary location Client locationTput (MB/s) Cluster 2.59 ClusterPlanetLab1.22 Bay AreaPlanetLab1.19 (archive on) Wide Area Throughput –Not limited by signatures –Not limited by archive –Not limited by Byzantine process bandwidth use –Limited by client-to-primary replicas bandwidth

29 Talk Outline Introduction System Overview Implementation and Deployment Performance Results –Andrew Benchmark –Stream Benchmark Conclusion

30 Closer look: Dissemination Tree Primary Replicas HotOS Attendee Other Researchers Archival Servers Secondary Replicas

31 Closer look: Dissemination Tree Self-organizing application-level multicast tree –Connects all secondary replicas to primary ones –Shields primary replicas from request load –Save bandwidth on consistency traffic Tree joining heuristic (“first-order” solution): –Connect to closest replica using Tapestry Take advantage of Tapestry’s locality properties –Should minimize use of long-distance links –A sort of poor man’s CDN

32 Performance Results: Stream Benchmark Goal: measure efficiency of dissemination tree –Multicast tree between secondary replicas Ran 500 virtual nodes on PlanetLab –Primary replicas in SF Bay Area –Other replicas clustered in 7 largest PlanetLab sites Streams writes to all replicas –One content creator repeatedly appends to one object –Other replicas read new versions as they arrive –Measure network resource consumption

33 Performance Results: Stream Benchmark Dissemination tree uses network resources efficiently –Most bytes sent across local links as second tier grows Acceptable latency increase over broadcast (33%)

34 Related Work Distributed Storage –Traditional: AFS, CODA, Bayou –Peer-to-peer: PAST, CFS, Ivy Byzantine fault tolerant storage –Castro-Liskov, COCA, Fleet Threshold signatures –COCA, Fleet Erasure codes –Intermemory, Pasis, Mnemosyne, Free Haven Others –Publius, Freenet, Eternity Service, SUNDR

35 Conclusion OceanStore designed as a global-scale file system Design meets primary challenges –End-to-end encryption for privacy –Limited trust in any one host for integrity –Self-organizing and maintaining to increase usability Pond prototype functional –Threshold signatures more expensive than expected –Simple dissemination tree fairly effective –A good base for testing new ideas

36 More Information and Code Availability More OceanStore work –Overview: ASPLOS 2000 –Tapestry: SPAA 2002 More papers and code for Pond available at

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