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FAWN: A Fast Array of Wimpy Nodes
Presented by:Clint Sbisa & Irene Haque
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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
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FAWN
Data-intensive, computational simple workloadsSmall objects - 100B - 1KB Cluster of embedded CPUs using flash storage Efficient Fast random reads Slow random writes FAWN-KV Key-value storage Consistent HashingFAWN-DS Data store Log structured
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FAWN - DS
Log-structure key-value storeContains 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 bits6 byte in-memory Hash Index stores frag and pointer
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FAWN - DS
Basic Functions: Store Lookup Delete Concurrent operations
Virtual Node Maintenance: Split Merge Compact
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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
FAWN - KV
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Chain replication
FAWN - KV
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Join split key range pre-copy chain insertion log flush Leave merge key range Join into each chain
FAWN - KV
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Individual Node Performance
• Lookup speed
• Bulk store speed: 23.2 MB/s, or 96% of raw speed
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Individual Node Performance
• Put speed
• Compared to BerkeleyDB: 0.07 MB/s – shows necessity of log-based filesystems
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Individual Node Performance
• Read- and write-intensive workloads
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System Benchmarks
• System throughput and power consumption
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Impact of Ring Membership Changes
• Query throughput during node join and maintenance operations
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Impact of Ring Membership Changes
• Query latency
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Alternative Architectures
• Large Dataset, Low Query → FAWN+Disk
• Small Dataset, High Query → FAWN+DRAM
• Middle Range → FAWN+SSD
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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