Surveys the advancements in data center technologies that are currently taking place. From commute, networking and storage virtualization, to X86 vs. Arm64 and the force of moving from scale-up based architectures to scale-out ones. By Liran Zvibel, CTO of Weka.IO

1. T H E F U T U R E O F
T H E D A T A C E N T E R
L I R A N Z V I B E L ,
L I R A N @ W E K A . I O

2. X 5 0 G R O W T H
R A T E !
D A T A I S E XP L O D I N G

3. D A T A G R O W T H
• Amount of data collected and stored is ever
increasing
• New services are created based on using that
data, and old enterprises are forced to provide
new applications for the same cost
• IoT, logs, sensors, media files and other
accumulated data are regularly processed and
reprocessed
• Some data is “cold” but still accessed (FB
images)
• Regulatory archiving data

5. G I A N T S I N N O V A T E , R E S T
F O L L O W
• Internet giants like Google, Facebook, Amazon handle
massive amount of data, and must solve scalability
problem ahead of anyone else, using cheaper,
commodity components.
• Enterprise companies are forced to keep up the pace,
and with the help of the traditional vendors try keep up.
• IBM, Oracle, Microsoft, SAP, Dell and the like lost their
ability to shape the future of the data centers.

6. S C A L E - U P V S S C A L E -
O U T
• Scale-up based solutions are bound, and will max-out. Becomes too expensive.
• Everything must scale, hence must be built from many small components that
together solve the big problem
• SW infrastructure is required to create systems that are scale-out. Once this is
done, adding features is at linear deployment cost, not possible at scale-up
• Large RDBMS (“Oracle”) vs. Big Data solutions (Hadoop, Cassandra, etc)
Scale up:
Scale out:

7. C O M P U T E
V I R T U A L I Z A T I O N
• Being able to run several OS instances on the
same computer
• Allowed much condensed infrastructure (compute
aggregation), resource agility and failover ability.
• Programmable resource management makes it a
Cloud
• Enterprises will morph their data centers to a
private cloud, then move to a hybrid cloud model

8. X 8 6 A N D A R M 6 4
C O M M O N S L O T
• ARM64 very efficient for data movement
(I/O), natively support virtualization
• Applied Micro, AMD and others will come
up with ARM based servers
• Facebook’s Open Compute Project has
designs that use X86 (“Intel”s) and
Arm64 CPUs concurrently
• New designs leverage hundreds of cores
in a server. Xeon Phi or bigger ARMs

9. R A C K D I S A G G R E G A T I O N

10. N E T W O R K
• 100Gb Eth/PCIe last year by
Intel (and other)
• 100x speedup in about 10
years, unprecedented
progress related to other
components!
• Physical and logical locality
(or placement) is dead
• Ethernet won once again

11. N E T W O R K I N G
S C A L E O U T
• Cisco just announced the 576 ports 40Gb Nexus
9516 switch, so not all scale-out yet.
• south-north vs east-west traffic
• Can be replaced with 50, much cheaper switches,
that also act as ToR
• Cheaper ROI, Pay as you go, better performance
• Clouds won’t use these devices

12. S D N : S W D E F I N E D N E T W O R K I N G
• HW devices only provide data flow,
management and control done in application.
• Most large network equipment providers move
towards supporting these open standards.
• End users are free to shape traffic, and
provide higher-level services that were once
impractical

13. S T O R A G E
• Scale out, virtualized
• Flash NAND 3D, price drops
• New mediums to archive and
handle cold storage
• Cloud object storage changing
economics

14. L I R A N @ W E K A . I O
@ L I R A N Z V I B E L
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