FAWN. A Fast Array of Wimpy Nodes. David Andersen, Jason Franklin, Michael Kaminsky*, Amar Phanishayee, Lawrence Tan, Vijay Vasudevan

Transcription

1 FAWN A Fast Array of Wimpy Nodes David Andersen, Jason Franklin, Michael Kaminsky*, Amar Phanishayee, Lawrence Tan, Vijay Vasudevan Carnegie Mellon University *Intel Labs Pittsburgh

2 Energy in computing Power is a significant burden on computing 3-year TCO soon to be dominated by power Hydroelectric Dam 2

3 3

4 Google s power consumption... would incur an annual electricity bill of nearly $38 million [Qureshi:sigcomm09] Energy consumption by data centers could nearly double... (by 2011) to more than 100 billion kwh, representing a $7.4 billion annual electricity cost [EPA Report 2007] Annual cost of energy for Google, Amazon, Microsoft = Annual cost of all first-year CS PhD Students 4

5 Can we reduce energy use by a factor of ten? Still serve the same workloads Avoid increasing capital cost 5

6 FAWN Fast Array of Wimpy Nodes Improve computational efficiency of data-intensive computing using an array of well-balanced low-power systems. 34-5"6"78-, 9&4:&4 +;1< ()* %&' +,-#. ()* ()* ()* %&' +,-#. ()* %&' +,-#. ()* ()*!"#$ %&' ()* %&' +,-#. ()* %&' +,-#. ()* %&' +,-#. ()* %&' +,-#. ()* %&' +,-#. AMD Geode 256MB DRAM 4GB CompactFlash /

7 Goal: reduce peak power Traditional Datacenter FAWN 100% Power 1000W 100W Cooling Servers (good){ Distribution 20% 20% energy loss 750W <100W 7

8 Overview Background FAWN Principles FAWN-KV Design Evaluation Conclusion 8

9 Towards balanced systems 1E+08 1E+07 1E+06 1E+05 Disk Seek Wasted Rebalancing Options Nanoseconds 1E+04 1E+03 1E+02 1E+01 resources DRAM Access 1E+00 CPU Cycle 1E Year Today s CPUs Array of Fastest Disks Slower CPUs Fast Storage Slow CPUs Today s Disks 9

10 Targeting the sweet-spot in efficiency 2500 Speed vs. Efficiency Fastest processors exhibit superlinear power usage Instructions/sec/W in millions Custom ARM Mote XScale 800Mhz Atom Z500 Xeon7350 Fixed power costs can dominate efficiency for slow processors FAWN targets sweet spot in system efficiency when including fixed costs Instructions/sec in millions (Includes 0.1W power overhead) 10

11 Targeting the sweet-spot in efficiency Instructions/sec/W in millions FAWN Today s CPU Array of Fastest Disks Slower CPU Fast Storage Slow CPU Today s Disk 11 Instructions/sec in millions XScale 800Mhz Custom ARM Mote Atom Z500 Xeon7350 More efficient

12 Overview Background FAWN Principles FAWN-KV Design Architecture Constraints Evaluation Conclusion 12

13 Data-intensive Key Value Critical infrastructure service Service level agreements for performance/latency Random-access, read-mostly, hard to cache 13

14 FAWN-KV: Our Key Value Proposition Energy-efficient cluster key-value store Goal: improve Queries/Joule Prototype: Alix3c2 nodes with flash storage 500MHz CPU, 256MB DRAM, 4GB CompactFlash 14

15 FAWN-KV: Our Key Value Proposition Unique Challenges: Energy-efficient cluster key-value store Efficient and fast failover Goal: improve Queries/Joule Wimpy CPUs, limited DRAM Flash poor at small random writes Prototype: Alix3c2 nodes with flash storage 500MHz CPU, 256MB DRAM, 4GB CompactFlash 15

16 FAWN-KV Architecture Manages Backends Back-end Acts as Gateway Routes Requests Back-end FAWN Back-end FAWN-DS Front-end X Back-end Back-end KV Ring Back-end Consistent hashing 16

17 FAWN-KV Architecture Back-end FAWN Back-end FAWN-DS Front-end X Back-end Back-end Back-end Back-end FAWN-DS FAWN-KV Limited Resources Avoid random writes Efficient Failover Avoid random writes 17

18 From key to value KeyFrag!= Key Potential collisions! 160-bit key Log Entry Key Len Data Low probability of multiple Flash reads Hashtable Hash Index Data region KeyFrag Valid. { 12 bytes per entry Offset (a) FAWN-DS FAWN-KV Limited Resources Avoid random writes Efficient Failover Avoid random writes 18

19 Log-structured Datastore Log-structuring avoids small random writes Get Put Delete Random Read Append FAWN-DS FAWN-KV Limited Resources Avoid random writes Efficient Failover Avoid random writes 19

20 On a node addition Hash Index Values H A (H,B] G B F C D Node additions, failures require transfer of key-ranges 20

21 Nodes stream data range Data in original range Data in new range Stream from B to A Concurrent Inserts, B Minimizes locking Compact Datastore A Background operations sequential Continue to meet SLA FAWN-DS FAWN-KV Limited Resources Avoid random writes Efficient Failover Avoid random writes 21

22 FAWN-KV Take-aways Log-structured datastore Avoids random writes at all levels Minimizes locking during failover Careful resource use but high performing Replication and strong consistency Variant of chain replication (see paper) 22

23 Overview Background FAWN principles FAWN-KV Design Evaluation Conclusion 23

24 Evaluation Roadmap Key-value lookup efficiency comparison Impact of background operations TCO analysis for random read workloads 24

25 FAWN-DS Lookups System QPS Watts QPS Watt Alix3c2/Sandisk(CF) Desktop/Mobi (SSD) MacbookPro / HD Desktop / HD Our FAWN-based system over 6x more efficient than 2008-era traditional systems 25

26 Impact of background ops Queries per second Queries per second Peak Compact Split Merge 0 Peak Compact Split Merge Peak query load 30% of peak query load Background operations have: Moderate impact at peak load Negligible impact at 30% load 26

27 When to use FAWN for random access workloads? TCO = Capital Cost + Power Cost ($0.10/kWh) Traditional (200W) FAWN (10W each) Five 2 TB disks 160GB PCI-e Flash SSD 64GB FBDIMM per node 2 TB disk 64GB SATA Flash SSD 2GB DRAM per node ~$ per node ~$ per node 27

28 Architecture with lowest TCO for random access workloads %&'&()'!*+,)!+-!./!$""""!$"""!$""!$"!$ 0.12*+*,% *+*0)#35!"#$%&%'(#)*+*,-./ 0.12*+*,-./!"#$!"#$!$!$"!$""!$""" 01)23!4&')! (9():; Ratio of query rate to dataset size determines storage technology Graph ignores management, cooling, networking... FAWN-based systems can provide lower cost per {GB, QueryRate} 28

29 Conclusion FAWN architecture reduces energy consumption of cluster computing FAWN-KV addresses challenges of wimpy nodes for key value storage Log-structured, memory efficient datastore Efficient replication and failover Meets energy efficiency and performance goals Each decimal order of magnitude increase in parallelism requires a major redesign and rewrite of parallel code - Kathy Yelick 29