Seeing the (Near) Future: Data Centers, So6ware, and SDN

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1 Seeing the (Near) Future: Data Centers, So6ware, and SDN David Meyer CTO and Chief Brocade Director, Advanced Technology Center, University of Oregon ONF/SDN Workshop Upperside NFV SDN Summit net, } hwp:// 5.net/~dmm/talks/dc_upperside_2014.pdf 1

2 Agenda A Few Macro Trends What Do These Trends Mean For DC Operators? What s Next? Q&A 2

3 Danger Will Robinson!!! This talk might be controversial/provoca5ve (and perhaps a bit sciencey ) 3

4 Agenda A Few Macro Trends What Do These Trends Mean For DC Operators? What s Next? Q&A 4

5 Trend: The Evolu.on of Intelligence R- complex Brain) to Neocortex à Hardware to So6ware HARDWARE SOFTWARE Key Architectural Features of Scalable/Evolvable Systems Turing, Bode, and Shannon RYF- Complexity (behavior) Layered Architecture Bow.es and Hourglasses Horizontal Transfer (H*T) Protocol Based Architectures Once you have HW its all about code hwp://

6 Trend: Open Source (hardware *and* software) Community building is a core objective Code is the coin of the realm Engineering systems are as important as artifacts Putting this all together à 6

7 Trend: Engineering ar.facts are no longer the source of sustainable advantage and/or innova.on - FluWer Perhaps surprisingly, the hyper- scale and open source have taught me that actual (in our case network as well as HW/SW) are ephemeral and that the only source of sustainable consists of Engineering Systems 1 Culture People/Process Mul@- disciplinary Approaches 1 Note that our Engineering Systems evolve using the same mechanisms that are used to build ar@facts. This is architecturally analogous to Horizontal Gene Transfer (HGT) and the acquisi@on of an@- bacterial resistance in the bacteria biosphere; the same mechanisms used to create the ar@fact (plasmid) are used to 7 evolve the Engineering System (transcrip@onal network). Consider: Horizontal Applica@on Transfer?

8 Agenda A Few Macro Trends What Do These Trends Mean For DC Operators? What s Next? Q&A 8

9 So What Does All This Mean For Data Center Operators? Today s Hyper- scale is tomorrow s Enterprise So what s happening in say, Google (MSFT, FB, ) today is a view into the future of enterprise (and SP) data centers à Everything needs to scale and evolve Build for failure, not for <n>- nines s/w and associated engineering more important than h/w Want Robustness *and* Evolvability 9

10 Layered architectures make robustness and evolvability compa5ble Deconstrained Diverse Apps Constrained/ Hidden? OS Minimal diversity HW Diverse Deconstrained (Hardware) Network OS + SDN Controller 10

11 Plauorm vs. Point OpenDaylight Controller Architecture 11

12 Agenda A Few Macro Trends What Do These Trends Mean For DC Operators? What s Next? Q&A 12

13 So What s Next? 13

14 Artificial Intelligence The Wave of the (Very Near) Future Today s control and automa@on techniques are passive, low power, primi@ve Use of AI techniques is itself accelera@ng while at the causing accelera@on Major breakthroughs in machine learning circa 2006 Deep Learning More code is wri@ng and deploying code every day New forms of machine learning a very hot topic in DC control and automa@on Google, Facebook, Amazon, NetFlix, Apple and Microso6 (among others) con@nue to make significant investments in AI hwp://techcrunch.com/2014/01/26/google- deepmind/ hwp:// google- is- inves@ng- in- deep- learning hwp:// omega- is- it- alive.html hwp:// ar@ficial- intelligence- is- shaping- the- future- of- facebook hwp://techblog.neulix.com/2014/02/distributed- neural- networks- with- gpus.html Complexity of DC control & resource alloca@on problem(s) outstripping human ability Literally 10^9s of interac@ng dynamic processes in typical hyper- scale data center Enterprise not far behind 14

15 Deep Learning? Big data, control and systems all subject of advancements in AI Deep Learning (DL) where all the is 1 Differs from tradi@onal machine learning in that DL systems engineer their own features ( unsupervised ) High- throughput technologies (*- omics) RFC Project Already seeing new applica@ons in diverse domains such as EPC, DCI, Obvious growth opportunity Gartner es@mates TAM to be as much as $20 billion by 2016 But also table stakes in the very near future 1 Y. Bengio, A. Courville, and P. Vincent., "Representa@on Learning: A Review and New Perspec@ves, IEEE Trans. PAMI, special issue Learning Deep Architectures,

16 Deep Learning In One Slide V4 V2 V1 16

17 Agenda A Few Macro Trends What Do These Trends Mean For DC Operators? What s Next? Q&A 17

18 Q&A Thanks! 18

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