FAWN: A Fast Array of Wimpy Nodes

1. Presented by: Aditi Bose & Hyma Chilukuri FAWN: A Fast Array of Wimpy Nodes

2. Motivation Large-scale data-intensive applications like high performance key-value storage systems are being used by Facebook, LinkedIn, Amazon with more regularity. Being I/O, Requiring RA over large DB, performing parallel, concurrent and mostly independent operations, requiring large clusters and storing small sized objects are several common features these workloads share.System performance: queries/sec    Energy efficiency: queries/joule CPU performance and I/O bandwidth Gap : For data intensive computing workloads, storage, network and memory bandwidth bottlenecks lead to low CPU utilizationSolution: wimpy processors to reduce I/O induced idle cyclesCPU Power consumption: operating processors at higher freq requires more energy.                     techniques to mask CPU bottleneck cause energy inefficiency                     branch prediction, speculative execution – more processor  die areaSolution:  slower CPUs execute more instructions per joule               1 billion vs. 100 million instructions per Joule

3. FAWN Efficient – 1W at heavy load Vs 10W at load        Fast random reads – up to 175 times faster        Slow random writes – updating a single page means erasing an entire block before writing the modified block in its placeCluster of embedded CPUs using flash storage        Efficient – 1W at heavy load Vs 10W at load        Fast random reads – up to 175 times faster        Slow random writes – updating a single page means erasing an entire block before writing the modified block in its place FAWN-KeyValue        nodes organized into a ring using consistent Hashing        physical node is a collection of virtual nodeFAWN-DS        Log structured key-value stores        contains values for key range associated with VID 

4. FAWN - DS Uses as in-memory Hash Index to map 160-bit key to a value stored in the data logstores only a fragment of the actual key.         Hash Index bucket = i low order index bits        key fragment = next 15 low order bitsEach bucket -6 bytes - stores frag, valid bit and 4-byte pointer

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

6. FAWN - KV FAWN-KV organizes the back-end VIDs into a storage ring-structure using consistent hashingManagement node        assigns each front-end to circular key space Front-end node        manages fraction of key-space        manages the VID membership list        forwards out-of-range request Back-end nodes – VIDs        owns a key range        contacts front-end when joining

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. Alternative Architectures Large Dataset, Low Query → FAWN+Disk                 number of nodes dominated by storage capacity per node                 has the lowest total cost per GBSmall Dataset, High Query → FAWN+DRAM                number of nodes dominated by per node query capacity                has the lowest cost for queries/secMiddle Range → FAWN+SSD               best balance of storage capacity, query rate and total cost

15. 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