Conventional wisdom in the IT industry asserts that most, if not all, workloads will shift from scale-up to scale-out. Yet when we look at emerging business computing needs and vertically bespoke challenges like genomic sequencing, the benefits of holding and computing large datasets in-memory remain. Can scale-out be tuned to handle these emergent computing challenges—and perhaps more importantly, should it? Or what if scale-up could join scale-out to solve new challenges? Imagine for a moment these two platform archetypes being used together to compose powerful new applications—applications that combine the benefits of scale-up in-memory and scale-out. Today, we are already seeing the benefits with ETL on Hadoop feeding scale-up in memory business computing platforms. And also in the Internet of Things, where streaming low-latency behaviors are essential to reinforce safety culture, there is room for scale-up in-memory on the edge writing to scale-out platforms. In this session, Hitachi Data Systems Vice President & Chief Engineer Michael Hay will explore the necessary tension between in-memory, instantaneous response vs. recoverability and non-stop operations and dissect the Scale-out vs. Scale-up industry debate. Hay will discuss the virtues of both scale-up and scale-out architectures, how they are already working together today and how they will work together in the future. Attendees will learn: How the network/fabric will participate in future computing platforms, especially in-memory. What new development and IT environments are needed to realize the full potential of combining scale-out/scale-up in-memory and edge data processing environments. The benefits of a Scale-out + Scale-up + Edge approach and how to implement it.

1. © Hitachi Data Systems Corporation 2015. All rights reserved.1 © Hitachi Data Systems Corporation 2015. All rights reserved.
Michael Hay – VP & Chief Engineer GISD
TWITTER: @mihay42
The Great Debate: Scale
Out vs Scale Up: Is it
either, or …or both?

2. © Hitachi Data Systems Corporation 2015. All rights reserved.2
A CONTRARIAN TIMEA CONTRARIAN TIME
BLURAY ARCHIVEBLURAY ARCHIVE
FLASH ARCHIVEFLASH ARCHIVE
SOURCE – URL: https://flic.kr/p/dA2Dc | AUTHOR: Rberteig | LICENSE: Creative Commons
SOURCE – URL: BluRayDiscBack.p ng | AUTHOR: Cdnomad| LICENSE: Public Domain
A NO-­SQL FIRM WITH IN-­MEMORY FOCUS
VOLATILITY | UNCERTAINTY | COMPLEXITY | AMBIGUITY

3. © Hitachi Data Systems Corporation 2015. All rights reserved.3
ITILITIL
WATERFALLWATERFALL
AGILEAGILE
AGILEAGILE
DEVOPSDEVOPS
ITERATIVE
…
ITERATIVE
…
ENGINEERING/DEVELOPMENTENGINEERING/DEVELOPMENT
IT & OPERATIONSIT & OPERATIONS
20012001
20092009
20112011
20192019

4. © Hitachi Data Systems Corporation 2015. All rights reserved.4
engineered systems!
!converged stacks!
chef!puppet
cfengine!capistrano
!scale-up primitives!
scale-out primitives!
!ganglia!paxos!
!ansible!cactai!
papertrail!sensu
!ant!jenkins
maven!gradel!stash
!nagios!icinga
nova!glance
QUALITY
OPERATIONS
!nebula!cinder!swift
!restful!python!ruby
!googleflow!
storm!cql!s3
!mongo!yarn
!cassandra
DEVELOPMENT
DEVOPS
CONTINUOUS DEPLOYMENTCONTINUOUS DEPLOYMENT
deployments per
month
deployments
per year
people deploy to
production
SOURCE: ETSY’s 2012 DevOps statistics reported at SXSW

5. © Hitachi Data Systems Corporation 2015. All rights reserved.5
SHARED NOTHING
EVENTUALLY CONSISTENT
DATA VARIANCE
SCALE-OUT, SCALE-UP DEFINEDSCALE-OUT, SCALE-UP DEFINED
SHARED EVERYTHING
CONSISTENT
PERFECT DATA
10s
of nodes
10s
of nodes
≥100s
of nodes
≥100s
of nodes

6. © Hitachi Data Systems Corporation 2015. All rights reserved.6
SHARED NOTHING
EVENTUALLY CONSISTENT
DATA VARIANCE
SCALE-OUT, SCALE-UP DEFINEDSCALE-OUT, SCALE-UP DEFINED
SHARED EVERYTHING
CONSISTENT
PERFECT DATA
10s
of nodes
10s
of nodes
≥100s
of nodes
≥100s
of nodes
NO VARIANCE WITH A PROBABILTY OF 1 FOR CONCLUSION
THINK: BANK ACCOUNT BALANCES, FINANCIAL TRADES,
COMPONENT FAILURES ON CRITICAL INFRASTRUCTURES,
OR ESSENTIALLY ANYTHING WITH RISK AND REGULATION
VARIANCE WITH A PROBABILTY LESS THAN 1 FOR CONCLUSION
THINK: SENTIMENT ANALYSIS, PREDICTIVE MAINTENANCE,
PROCESS CONTROLS OR ANYTHING WHERE DECISIONS CAN BE
MADE WITH SOME DEGREE OF RISK TOLERANCE

7. © Hitachi Data Systems Corporation 2015. All rights reserved.7
SCALE-OUT, SCALE-UP A CAGE MATCHSCALE-OUT, SCALE-UP A CAGE MATCH
WINNER?WINNER?

8. © Hitachi Data Systems Corporation 2015. All rights reserved.8
SCALE-OUT, SCALE-UP ON THE SAME…SCALE-OUT, SCALE-UP ON THE SAME…
EAM!EAM!

9. © Hitachi Data Systems Corporation 2015. All rights reserved.9
PUTTING IT ALL TOGETHERPUTTING IT ALL TOGETHER
scale-upscale-out
① Acquire packet data to discern indicators of packet loss and persist
F87AB:01001!
F87AA:01000!
F87A9:00111!
F87A8:00110!
F87A7:00100!
HITACHI
MARS
HITACHI
STREAM
② Lookup customer name, ID, etc. from HANA in real-timeAPIAPI
③ Spin up Hadoop to compute sentiment on Twitter data by customer
API
④ Combine all data to try and correlate packet behaviors with sentiment
APIAPI
API

10. © Hitachi Data Systems Corporation 2015. All rights reserved.10
PUTTING IT ALL TOGETHERPUTTING IT ALL TOGETHER
① Acquire packet data to discern indicators of packet loss and persist
F87AB:01001!
F87AA:01000!
F87A9:00111!
F87A8:00110!
F87A7:00100!
HITACHI
MARS
HITACHI
STREAM
② Lookup customer name, ID, etc. from HANA in real-timeAPIAPI
③ Spin up Hadoop to compute sentiment on Twitter data by customer
API
④ Combine all data to try and correlate packet behaviors with sentimen
APIAPI
API
LATENCY LEADS TO POOR QUALITYLATENCY LEADS TO POOR QUALITY
scale-upscale-out

11. © Hitachi Data Systems Corporation 2015. All rights reserved.11
EDGE CORE
AGUMENT WITH EDGE SCALE-INAGUMENT WITH EDGE SCALE-IN
① Spin up packet acquisition & WAN-ACC engines on the edge
② Load the relevant models and queriesAPIAPI
③ Decommission packet acquisition in the core
API APIAPI
API
scale-in (to the) edge
F87AB:01001!
F87AA:01000!
F87A9:00111!
F87A8:00110!
F87A7:00100!
HITACHI
MARS
HITACHI
STREAM
HITACHI
STREAM
F87AB:01001!
F87AA:01000!
F87A9:00111!
F87A8:00110!
F87A7:00100!
HITACHI
MARS
scale-upscale-out

12. © Hitachi Data Systems Corporation 2015. All rights reserved.12
FABRICFABRIC NETWORKNETWORK
IN-MEMORY COMPUTE PRESSURES NETWORKSIN-MEMORY COMPUTE PRESSURES NETWORKS
IMPLICATION: NEW (STORAGE) ARCHITECTURES ARE REQUIRED
GigaBYTES/sec
[UNIT: 8 bits]
GigaBYTES/sec
[UNIT: 8 bits]
GigaBITS/sec
[UNIT: 1 bit]
GigaBITS/sec
[UNIT: 1 bit]
THROUGHPUTTHROUGHPUT
0.1’s MICROseconds
[ORDER: 0.0000001s]
0.1’s MICROseconds
[ORDER: 0.0000001s]
10’s MICROseconds
[ORDER: 0.00001s]
10’s MICROseconds
[ORDER: 0.00001s]
LATENCYLATENCY
FABRICS (e.g. PCIe, IB) LEAD SERIAL NETWORK RATES

13. © Hitachi Data Systems Corporation 2015. All rights reserved.13
(IN 2020 ACCORDING TO CISCO)
INTERNET OF THINGSINTERNET OF THINGS

14. © Hitachi Data Systems Corporation 2015. All rights reserved.14
OROR
OROR
OROR
OROR
IS IN-MEMORY COMPUTE WAN COMPATIBLE?IS IN-MEMORY COMPUTE WAN COMPATIBLE?
10Gbps
leased line
10Gbps
leased line

15. © Hitachi Data Systems Corporation 2015. All rights reserved.15
Transition to Multi-­Modal Architectures
TRANSITION
DATABASE
APPLICATION SERVER
WEB PRESENTATION
APPLICATION(S)/
SOURCE(S)
WAREHOUSE/MARTS
CUBES
IN MEMORY
ANALYSIS
…
THING/
SOURCE
ANALYSIS
DATA LAKES/OBJECT CLOUDS
ANALYSIS ANALYSIS ANALYSIS
LOW LATENCYDEEP CAPACITY
Applications and sources at
real-time and near real-time
focus that are latency
intolerant
Capacity optimized
persistenceand data
lakes with embedded
analytics
PERSISTENT STORE
SCRATCH
PROCESSING
HPC DWH 3 TIER

16. © Hitachi Data Systems Corporation 2015. All rights reserved.16
Transition to Multi-­Modal Architectures
TRANSITION
DATABASE
APPLICATION SERVER
WEB PRESENTATION
APPLICATION(S)/
SOURCE(S)
WAREHOUSE/MARTS
CUBES
IN MEMORY
ANALYSIS
…
THING/
SOURCE
ANALYSIS
DATA LAKES/OBJECT CLOUDS
ANALYSIS ANALYSIS ANALYSIS
LOW LATENCYDEEP CAPACITY
Applications and sources at
real-time and near real-time
focus that are latency
intolerant
Capacity optimized
persistenceand data
lakes with embedded
analytics
PERSISTENT STORE
SCRATCH
PROCESSING
HPC DWH 3 TIER
PROCESSING:
MEMORY:
STORAGE:
I/O & SENSORS:
4000 MIPS (64bit)
0.5 GB
8 GB
1 GigE | ACCELEROMETER | …
ECU

17. © Hitachi Data Systems Corporation 2015. All rights reserved.17
Transition to Multi-­Modal Architectures
TRANSITION
DATABASE
APPLICATION SERVER
WEB PRESENTATION
APPLICATION(S)/
SOURCE(S)
WAREHOUSE/MARTS
CUBES
IN MEMORY
ANALYSIS
…
THING/
SOURCE
ANALYSIS
DATA LAKES/OBJECT CLOUDS
ANALYSIS ANALYSIS ANALYSIS
LOW LATENCYDEEP CAPACITY
Applications and sources at
real-time and near real-time
focus that are latency
intolerant
Capacity optimized
persistenceand data
lakes with embedded
analytics
PERSISTENT STORE
SCRATCH
PROCESSING
HPC DWH 3 TIER
COMPACT U’s
FLASH TRENDING
KVM/DOCKER
EVO RAIL
storage (memory) control
scale-in (to the edge):
stream,
filtering/winnowing,
control systems, small
edge applications

18. © Hitachi Data Systems Corporation 2015. All rights reserved.18
Transition to Multi-­Modal Architectures
TRANSITION
DATABASE
APPLICATION SERVER
WEB PRESENTATION
APPLICATION(S)/
SOURCE(S)
WAREHOUSE/MARTS
CUBES
IN MEMORY
ANALYSIS
…
THING/
SOURCE
ANALYSIS
DATA LAKES/OBJECT CLOUDS
ANALYSIS ANALYSIS ANALYSIS
LOW LATENCYDEEP CAPACITY
Applications and sources at
real-time and near real-time
focus that are latency
intolerant
Capacity optimized
persistenceand data
lakes with embedded
analytics
PERSISTENT STORE
SCRATCH
PROCESSING
HPC DWH 3 TIER
scale-out compu-store
scale-out obj-store
scale-up:
object-dns
sap-hana
graph
kvs
MEMORIESMEMORIES
storage
control

19. © Hitachi Data Systems Corporation 2015. All rights reserved.19
Transition to Multi-­Modal Architectures
TRANSITION
IN MEMORY
ANALYSIS
…
THING/
SOURCE
ANALYSIS
DATA LAKES/OBJECT CLOUDS
ANALYSIS ANALYSIS ANALYSIS
LOW LATENCYDEEP CAPACITY
Applications and sources at
real-time and near real-time
focus that are latency
intolerant
Capacity optimized
persistenceand data
lakes with embedded
analytics
PERSISTENT STORE
SCRATCH
PROCESSING
HPC
scale-out hyper-converged
scale-out obj-store
scale-upobject-dnsMEMORIESMEMORIES

20. © Hitachi Data Systems Corporation 2015. All rights reserved.20
CREDITS for images/materials
1. Hadoop Coffee Image
‒ Author: http://www.flickr.com/photos/yukop/
‒ License: http://creativecommons.org/licenses/by-­sa/2.0/
2. Waterfall photo
‒ Author: https://www.flickr.com/photos/thart2009/
‒ License: https://creativecommons.org/licenses/by/2.0/
‒ URL: https://flic.kr/p/9FqqQF
3. Agile development
‒ Author: https://www.flickr.com/photos/improveit/
‒ License: https://creativecommons.org/licenses/by/2.0/
‒ URL: https://flic.kr/p/Nt7cB
4. BluRay Icon
‒ Author: Jordan (http://jrdng.deviantart.com/?rnrd=8324)
‒ License: http://commons.wikimedia.org/wiki/Main_Page