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FAWN: A Fast Array of Wimpy Nodes

Presented by: Clint Sbisa & Irene Haque. FAWN: A Fast Array of Wimpy Nodes. Motivation. Large-scale data-intensive applications         Facebook, LinkedIn, Dynamo CPU-I/O Gap         storage, network and memory bottlenecks         low CPU utilization CPU Power

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FAWN: A Fast Array of Wimpy Nodes

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  1. Presented by: Clint Sbisa & Irene Haque FAWN: A Fast Array of Wimpy Nodes

  2. Motivation Large-scale data-intensive applications         Facebook, LinkedIn, Dynamo CPU-I/O Gap         storage, network and memory bottlenecks         low CPU utilization CPU Power         slower CPUs execute more queries per second per Watt         1 billion vs. 100 million instructions per Joule         inefficient energy saving techniques Memory Power

  3. FAWN Data-intensive, computational simple workloads Small objects - 100B - 1KB Cluster of embedded CPUs using flash storage         Efficient         Fast random reads         Slow random writes FAWN-KV          Key-value storage         Consistent Hashing FAWN-DS         Data store         Log structured

  4. FAWN - DS Log-structure key-value store Contains all values in a key range for each virtual ID Maps 160-bit key         Hash Index bucket = i low order index bits         key fragment = next 15 low order bits 6 byte in-memory Hash Index stores frag and pointer 

  5. Virtual Node Maintenance:     Split     Merge     Compact FAWN - DS Basic Functions:         Store         Lookup         Delete                                Concurrent operations

  6. FAWN - KV Consistent hashing of back-end VIDs Management node         assigns each front-end to circular key space Front-end nodes         manages its key space         forwards out-of-range request Back-end nodes - VIDs         contacts front-end when joining         owns a key range

  7. FAWN - KV Chain replication

  8. FAWN - KV Join     split key range     pre-copy     chain insertion     log flush Leave     merge key range     Join into each chain

  9. Individual Node Performance • Lookup speed • Bulk store speed: 23.2 MB/s, or 96% of raw speed

  10. Individual Node Performance • Put speed • Compared to BerkeleyDB: 0.07 MB/s – shows necessity of log-based filesystems

  11. Individual Node Performance • Read- and write-intensive workloads

  12. System Benchmarks • System throughput and power consumption

  13. Impact of Ring Membership Changes • Query throughput during node join and maintenance operations

  14. Impact of Ring Membership Changes • Query latency

  15. Alternative Architectures • Large Dataset, Low Query → FAWN+Disk • Small Dataset, High Query → FAWN+DRAM • Middle Range → FAWN+SSD

  16. Conclusion • Fast and energy efficient processing of random read-intensive workloads • Over an order of magnitude more queries per Joule than traditional disk-based systems

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