Martin McKendry - Open Parallel, USA

“The Contribution of MultiCore to Server Architecture: Part II -Implications for the Future”

Transcript

1. The Contribution of MultiCore to Server Architecture Part 2: Implications for the Future 1 Martin McKendry Steve Friedl
2. What I do • Work in Silicon Valley – Best place to work in the world • Work at the intersection of market capitalization and technology – Growth companies: FileNet and Siebel – Value plays: Avaya and Openwave • Constantly figuring out what building blocks to use, how and where to get teams to use them 2
3. Outline • Resources today: MultiCore Everywhere • Application Architecture • The World According to SQL • Breaking Assumptions – NoSQL – Flash • New Application Architecture • Startups Exploiting Technology Trends 3
4. The New Age • We have plenty of hardware resources now – MultiCore Processors, Networking, Flash Storage • But we are restricted by legacy interfaces – Virtualization – Block Device Protocols – SQL 4
5. On the eve of the release of Ivy Bridge, Intel is finally bringing its server chips up to speed by introducing theSandy Bridge-based E5-2600 family of CPUs. The company claims its latest processors outperform the previous generation of Xeons by up to 80 percent in raw speed, while improving per-watt performance by 50 percent. A grand total of 17 different Xeons will be available, ranging in price from $198 to $2,050. The eight-core chips support up to 768GB of RAM, PCI Express 3.0, Hyper-Threading, Turbo Boost, Intel Virtualization -- basically the whole Chipzilla portfolio of tricks. We have plenty of CPU power and addressable memory 5
6. Specification FlashArray Controller (each) CPU 2x Intel Xeon 6-core CPUs RAM 48GB (working cache) BOOT DRIVES Redundant hot-swappable boot SSDs FRONT-END PORTS - 4x 8Gb/s Fibre Channel (SFP) - 1x empty expansion slot BACK-END PORTS - 4x 6Gb/s SAS Storage Shelf Interconnect - 2x 40Gb/s QDR Infiniband Controller Interconnect MANAGEABILITY - 2x GbE (RJ-45) - Serial and USB KVM access POWER SUPPLY Redundant, hot-swappable power supp Storage uses serious processor power 6
7. Specification FA-320 (2 controllers, 2 storage shelves) FA-310 (1 controller, 1 storage shelf) IOPS (TYPICAL 4K RANDOM) 300,000 200,000 SUSTAINED WRITE IOPS 180,000 140,000 BANDWIDTH 3 GB/sec 2 GB/sec SUSTAINED WRITE BANDWIDTH 1GB/sec 500 MB/sec LATENCY < 1 ms average latency < 1 ms average latency EFFECTIVE CAPACITY* - AT 5-TO-1 DATA REDUCTION - AT 10-TO-1 DATA REDUCTION Up to 100 TB Up to 200 TB Up to 50 TB Up to 100 TB To produce serious throughput 7
8. Cisco Consolidates 40 customized packet-processor cores (900 MHz to 1.2 GHz) into a single piece of silicon. Plenty of processing at the network interface 8
9. We have the processor power, but… • Application software architectures are based on – Lack of CPU power – Lack of memory – Large distance to secondary storage – Speed of rotating media 9
10. Application Architecture Permanent Storage Media (Disk) Memory CPU SQL Database Middleware Applications: HR, Finance, Sales, Service… 10 SQ L Block Device
11. Oracle as Example • 2003 Buys JD Edwards $1.8B • 2005 buys PeopleSoft $10.3B • 2005 Buys Siebel $5.8 B • 2008 buys BEA $8.5B – BEA Weblogic begat Fusion middleware All Based on SQL 11
12. Application Architecture Permanent Storage Media (Disk) Memory CPU Oracle BEA JD Edwards, PeopleSoft, Siebel 12
13. Evolution of Databases • 1970: Codd develops relational model, SQL • 1978: Oracle V1-- 128K memory • 1983: PC XT—10 MB drive • 1984: Oracle V5 – 512K memory • 2012: The same SQL? 13
14. Tables and Columns • Structure defines relationships • Direct naming limited to tables, columns • Table and column names are in metadata Effect: Small namespaces • Refer to metadata to extract working data (join, select) • Working data limited in size 14
15. We are Patching Application Structure Old Patches: • Oracle: Oracle Releases Oracle TimesTen In-Memory Database 11g Release 2. • SAP: HANA: The Next Wave of In-Memory Computing Technology New Patches • Memcached Memcached is an in-memory key-value store for small chunks of arbitrary data (strings, objects) from results of database calls, API calls, or page rendering. 15
16. NoSQL offers varying approaches • Often based on key-value indices • Often no fixed table or column structure • Usually weaker consistency • Often optimized for append and retrieve • Exploits larger memory, distributed storage • Takes us past historic limits • But there is no uniform approach 16
17. With JSON as the document format. Error records might look like this: { “ID”: 1, “ERR”: “Out of Memory”, “TIME”: “2004-09-16T23:59:58.75”, “DC”: “NYC”, “NUM”: “212-223-2332” } { “ID”: 2, “ERR”: “ECC Error”, “TIME”: “2004-09-16T23:59:59.00”, “DC”: “NYC”, “NUM”: “212-223-2332” } • Denormalized and self-contained • Requires referential ID • Requires and exploits a large name space 17
18. Opensource Tools • Hadoop A distributed file system that provides high-throughput access • HBase™: A scalable, distributed database that supports structured data • Cassandra™: A scalable database with no single points of failure. • MongoDB™: A NoSQL database that optimizes writes • Hive™: A data warehouse infrastructure that provides data summarization and ad hoc querying. 18
19. Controversies • Write-Optimization • Consistency • Permanence • What kind of data are you storing, and how? – Internal State: Locks and tables – External State: Account values – Big Data: Who dun what when 19
20. Controversies • Not all access types are distinguished in the controversy – http://www.youtube.com/watch?v=b2F-DItXtZs 20 Note: A lot of big data concepts come from Amazon, Google, and Facebook. MySQL is a free relational database.
21. New approaches with old assumptions – Legacy Interfaces are good for short-term market traction • Emulate an old interface, and your startup gets volume – But they are not optimal at a system level • Old interfaces were designed for old constraints – Will ultimately yield to completely new software architectures 21
22. Application Architecture Permanent Storage Media (Disk) Memory CPU SQL Database Middleware Applications: HR, Finance, Sales, Service… 22 SQ L Block Device
23. 23 Exploits larger RAM Assumes secondary storage is far from RAM
24. Still doing key lookups in main memory 24
25. Flash Improves Storage • Brings secondary storage closer to RAM, processor – IO times drop from 2 milliseconds to 20 microseconds • 2000 microseconds down to 20 – 100x improvement • There is processing power close to the device • Several “startups” fit flash to historic interfaces – XIO – Fusion IO – Pure Storage 25
26. 26 Flash fronting for disks
27. 27 Optimizing to legacy interfaces
28. Application Architecture Permanent Storage Media (Disk) Memory CPU SQL Database Middleware Applications: HR, Finance, Sales, Service… 28
29. A New Application Architecture Flash Storage Memory CPU No SQL Database New Middleware Applications: HR, Finance, Sales, Service… 29 Storage CPU
30. NoSQL direct to Flash How much faster could real-world key-value stores (typical in NoSQL databases) go if their indices could be updated in non-volatile memory and not block while waiting on kernel I/O? Fusion IO’s Brent Compton, Blog, Jan 18 2012 30
31. A New Application Architecture Flash Storage Memory CPU No SQL Database New Middleware Applications: HR, Finance, Sales, Service… 31 Storage CPU All New All unstable
32. Conclusion • With MultiCore, we have plenty of CPU now – It’s deployed in specialized controllers – It’s available for general processing • Storage is a problem – But Flash brings it closer to the CPU • But we are limited by legacy application structure and SQL – Block device architecture limits access to logic closer to storage – NoSQL offers paths – Middleware has yet to emerge • Major players have not kept up – Oracle, SAP, Microsoft will have to adapt or acquire • The world is full of opportunity 32
33. 33 Collaboration Applications
36. 36 Communications
39. 39 SaaS IT
42. 42 Big Data Applications
44. Enterprise and Infrastructure Startups • Storage – Fusion IO – XIO – Pure Storage • NoSQL – Couchbase – RethinkDB • Collaboration – Jive – Moxie 44 • Communications – Aeris • SaaS IT – Mobile Iron – Zenprise • SaaS VOIP – Ring Central • Big Data Apps – C3