Google Cloud Platform, Avere Systems, and Cycle Computing experts will share best practices for advancing solutions to big challenges faced by enterprises with growing compute and storage needs. In this “best practices” webinar, you’ll hear how these companies are working to improve results that drive businesses forward through scalability, performance, and ease of management. The slides were from a webinar presented January 24, 2017. The audience learned: - How enterprises are using Google Cloud Platform to gain compute and storage capacity on-demand - Best practices for efficient use of cloud compute and storage resources - Overcoming the need for file systems within a hybrid cloud environment - Understand how to eliminate latency between cloud and data center architectures - Learn how to best manage simulation, analytics, and big data workloads in dynamic environments - Look at market dynamics drawing companies to new storage models over the next several years Presenters communicated a foundation to build infrastructure to support ongoing demand growth.

1. WEBINAR
Solving Enterprise Business Challenges
Through Scale-Out Storage & Big Compute
Michael Basilyan, Product Manager, Google Cloud Platform
Scott Jeschonek, Director of Cloud Products, Avere Systems
Rob Futrick, CTO, Cycle Computing

2. Housekeeping
• Slides
• Questions
• Recording
• Attachments

3. Presenters
Michael Basilyan
Product Manager
Scott Jeschonek
Director of Cloud Products
Rob Futrick
CTO

4. Introduction to Google Cloud Platform
Focusing on Compute Engine & Storage
Michael Basilyan
basilyan@google.com
Product Manager, GCE

5. Agenda
• Google Cloud Overview
• Compute Engine VMs:
• GCE VM Instances & Managed Infrastructure
• Storage:
• Block Storage
• Cloud Storage

6. What is Google Cloud
Platform?

7. 7
Google Cloud Platform Services
VIRTUAL NETWORK
LOAD BALANCING
CDN
DNS
INTERCONNECT
Management Compute Storage Networking Data
Machine
Learning
STACKDRIVER
IDENTITY AND
ACCESS
MANAGEMENT
CLOUD ML
SPEECH API
VISION API
TRANSLATE API
NATURAL
LANGUAGE API

8. 8
Google Cloud Platform Services
VIRTUAL NETWORK
LOAD BALANCING
CDN
DNS
INTERCONNECT
Management Compute Storage Networking Data
Machine
Learning
STACKDRIVER
IDENTITY AND
ACCESS
MANAGEMENT
CLOUD ML
SPEECH API
VISION API
TRANSLATE API
NATURAL
LANGUAGE API

9. GCE: Compute & VM Features

10. VM Live Migration = No Downtime

11. Custom Machine Types
Average Savings: 19%
Create VMs shaped for your workloads instead of shaping your workloads to fit pre-defined VMs.

12. Preemptible VMs
Ideal for batch, grid, and fault-tolerant workloads
Save 80% off regular VM list prices: flat $0.01 per core hour
Flat pricing with no complex bidding or competition
Same performance (CPU, I/O, Net) as regular VMs
Example uses: Hadoop, Rendering/Transcoding, Genomics,
Monte Carlo Simulations, etc.

13. Managed Infrastructure - zero devops for IaaS
Create Groups of
Instances
- Define Instance Template
- Deploy Docker containers or
apps directly
- Automatically connect new
instances to load balancer
Autoheal
- Use app level healthcheck to
signal issue
- Get machine recreated or
restarted
Autoscale
- Add/Remove instances automatically
based on scaling policy (CPU
utilization, LB load, Custom Metrics)
- Scale pool of workers with task
queue
Update
- Deploy new version of your software
with rolling update while serving
traffic
- Do cannary, % rollout, control pace,
roll-back
- Recreate in place or surge instances

14. Ways we save you money
● Preemptible VMs
● Custom Machine Types
● Per-minute billing
● Sustained Use Discount
○ The more you use, the bigger
the discount. Automatically.
● Instance right-sizing
○ Instance recommendations
displayed on VM Instances
Page
○ Single Button Actuation

15. Block & Object Storage

16. Cloud
Storage
Cloud
Bigtable
Cloud
Datastore
Cloud
SQL
Good for:
Binary or object
data (BLOB)
Such as:
Media, analytics,
archive/backup
Good for:
Hierarchical,
mobile, web
Such as:
User profiles,
Game State
Good for:
Web
frameworks
Such as:
CMS,
eCommerce
Good for:
Heavy read +
write, events,
Such as:
AdTech,
Financial, IoT
Where do I store my data?
Big
Query
Good for:
Data
Warehouse
Such as:
Analytics,
Dashboards
Relational NoSQL Object Warehouse
Good for:
Local VM file
storage
Such as:
Application
data/binaries
Block
Persistent
Disk (GCE)

17. Cloud
Storage
Cloud
Bigtable
Cloud
Datastore
Cloud
SQL
Good for:
Binary or object
data (BLOB)
Such as:
Media, analytics,
archive/backup
Good for:
Hierarchical,
mobile, web
Such as:
User profiles,
Game State
Good for:
Web
frameworks
Such as:
CMS,
eCommerce
Good for:
Heavy read +
write, events,
Such as:
AdTech,
Financial, IoT
Where do I store my data?
Big
Query
Good for:
Data
Warehouse
Such as:
Analytics,
Dashboards
Relational NoSQL Object Warehouse
Good for:
Local VM file
storage
Such as:
Application
data/binaries
Block
Persistent
Disk (GCE)

18. Block Storage
Reliable, high-performance block storage for virtual machine instances on GCE
Standard Persistent
Disk
SSD Persistent Disk Local SSD
Target
scenarios
Large data processing
workloads and some enterprise
applications
Genomics processing, video
transcoding in GCE
High performance database
and enterprise applications
MySQL, SQL Server, Oracle
In-memory databases
High-performance scratch space
Features
Persistent storage
Cost sensitive ($.04 GB)
Persistent storage
Performance sensitive
($0.17GB)
Ephemeral storage
Highest-performance ($0.218
GB)
Encryption, Snapshots
64 TB, Disk Size sets performance
(Attach larger VMS for max SSD performance)
Encryption
3TB

19. Cloud Storage: Object/Blog store
● Google Cloud Storage is a scalable
object storage service suitable for
all kinds of unstructured data.
● Cloud Storage vs Perst. Disk:
○ Scales to exabytes.
○ Accessible from anywhere.
○ REST interface; higher latency
than locally attached block
storage (PD)
○ Write semantics include insert
and overwrite file only.
○ Offers versioning.
○ Cheaper!
● Lots of guidelines on picking
storage on our site.

20. Regions and Zones

21. –––– 2018
2018
Current regions and number of zones
Edge points of presence
Network
Committed regions for 2017 and number
of zones
#
# https://peering.google.com
https://cloud.google.com/compute/docs/regions-zones/regions-zones
Google Cloud Platform Infrastructure
Google Cloud Platform is built on a datacenter network infrastructure that supports Google scale,
performance, and availability
2
3
Singapore2
S Carolina
N Virginia
Belgium
London
Tokyo
Taiwan
Mumbai
Sydney
Oregon
Iowa
Frankfurt
São Paulo
Finland
3
3
3
3
3
3
2
4
3
3
3

22. Cloud HPC:
Data Access Challenges
Scott Jeschonek, Director of Cloud Products

23. HPC in the Cloud
• Bring 100s or 1000s of cores online, quickly and efficiently
• Networking within the Cloud Compute environment minimizes compute latency
• Creative use of preemptible / spot market VM instances allow large numbers of
worker nodes at reasonable cost

24. “Pure” Cloud HPC
• Entire grid in Compute
Cloud
• Data is located locally
•
Cloud Storage options
may be used
• 3rd party Data may be
incorporated (from their
cloud storage)

25. Hybrid HPC
Existing HPC clusters:
Capital investment
- Possibly sunk cost already
Logical investment:
- Hardware Tuned
- Storage optimized
- Network optimized
- Daily ops dependent on status
quo
Cloud HPC Clusters:
Transient investment:
- Can build on demand
infrastructure
Expand on-prem:
- Use orchestration and grid
management to extend jobs into
cloud
- Schedule jobs based on
performance / cost requirements

26. Hybrid HPC

27. Grids On-Demand

28. Latency “Kills”
• Access to Data is the main challenge for HPC
• Amplified in the cloud:
- Data has to be located on or near the worker nodes
- Data may be in your datacenter
- Copy it all to the cloud?
- Costs for workers grows if data has to be copied to local disks
- Pipelines may require multiple writes (of results)
- Writes to local storage increases consistency risks
- Writes back to on-prem storage introduces significant latency

29. Using a Data Access Layer

30. Advantages of Data Access Layer
Keep your data on prem! – Data in cloud is only there while the compute
nodes work the jobs.
- Reduce the security objections, simplify the move to cloud
Increase cloud compute performance – using file system caching, most of
the data will be in RAM, close to the nodes
- Avoids ingest latencies and slashes transit latency after first read
Scale out – Using solution that facilitates 10s of 1000s of core file system
connections

31. Hybrid Cloud / Hybrid HPC Using Avere Technology
Customer Needs Avere Delivers
Low-latency file access Edge-Core Architecture
Scalable Performance
and Availability
Scale-out Clustering
NFS & SMB interfaces FlashCloud File System
for Object
Single pool of storage Global Namespace
High Security AES-256 Encryption,
KMIP
Flexibility Physical and virtual
products

32. Lessons Learned from 10 Years
How Cloud Changes Big Compute
Rob Futrick, CTO

33. 33
The Broad
Institute
Need: 270,000 hours of
computing
Why: Machine learning to
map relationships among
cancer datasets

34. © Copyright Cycle Computing LLC | All Rights Reserved
PAGE 34
Internal cluster queue too
long
Up & running in 1 hour,
scaled and completed
project in 2 weeks
30 years of Computing in 6
hours!
Submit jobs, orchestrate ML
application
Encrypt, route data to Cloud,
return results
51,200 cores
To run R ML
framework
Secure Cluster
Cell Line
Data, RNA,
DNA
Scaling Machine Learning @ The Broad Institute

35. © Copyright Cycle Computing LLC | All Rights Reserved
PAGE 35
Manufacturing
& Electronics
Pharma &
Biotech
Financial &
Insurance
Media &
Entertainment
Oil & Gas
65% of G2000 are limited by access to Big
Compute

36. © Copyright Cycle Computing LLC | All Rights Reserved
PAGE 36
The Challenges: Cloud & HPC Big Compute
User
Inputs
Existing Workflows
Data Dependencies Instance types
Applications
Scalability
Budget Controls
AuthorizationSecurity Stack
IT LOB
Inputs
Job scripts & data
Cloud accounts
Storage / Data sources
OS variations
AD / LDAP Authorization

37. © Copyright Cycle Computing LLC | All Rights Reserved
PAGE 37
The Solution: CycleCloud for Cloud HPC & Big Compute
User
Inputs
Existing Workflows
Data Dependencies Instance types
Applications
Scalability
Budget Controls
AuthorizationSecurity Stack
IT LOB
Inputs
Job scripts & data
Cloud accounts
Storage / Data sources
OS variations
AD / LDAP
Audit/Compliance data
Usage data (User, Group, App)
Job run-time by instance data
AppServer platform
Internal

38. © Copyright Cycle Computing LLC | All Rights Reserved
PAGE 38
Who is Cycle Computing?
• Leader in Cloud Big Compute/HPC
• Pioneering Cloud Management Software for 10 years
• 370M compute-hours managed
• Compute hour growth: 7x every 2 years
• CycleCloud Value Proposition
• Simple Managed Access to Big Compute
• Accelerating Innovation for the Enterprise
=> Faster time to result, with cost control
• Our customers
• Fortune 500, startups, and public sector
• Life sciences & pharma, financial services,
manufacturing, insurance, electronics

39. © 2016 Copyright | All rights reserved
7 Lessons Learned

40. © Copyright Cycle Computing LLC | All Rights Reserved
PAGE 40
#1 – Zero waiting in line for compute

41. © Copyright Cycle Computing LLC | All Rights Reserved
PAGE 41
#2 – Ask questions of any scale
Ask the right question,
regardless of scale
Think about the problem first
Then the system

42. © Copyright Cycle Computing LLC | All Rights Reserved
PAGE 42
#3 – Users with unique requirements are OK
Trivial to support different use cases
Different GPU, RAM, SSD, OS needs can be created easily
Move workloads that don’t fit internally to Cloud

43. © Copyright Cycle Computing LLC | All Rights Reserved
PAGE 43
#4 – Cloud gets faster/cheaper over time

44. © Copyright Cycle Computing LLC | All Rights Reserved
PAGE 44
#5 – Time & cost are the sole metrics that matter

45. © Copyright Cycle Computing LLC | All Rights Reserved
PAGE 45
Everything
you don’t
think about!

46. © Copyright Cycle Computing LLC | All Rights Reserved
PAGE 46
#6 – Accelerating answers, accelerates people
720 (hours) 720 720
Computing Analysis
2880 hours /
120 Days to Decision
Computing
720
Analysis
SCALABLE COMPUTING (in hours)
720
Computing Analysis Analysis
1456 hours /
60.6 Days to Decision
7208
Computing
ANTICIPATED BENEFIT (in hours)
8

47. © Copyright Cycle Computing LLC | All Rights Reserved
PAGE 47
#6 – Accelerating answers, accelerates people
720 (hours) 720 720
Computing Analysis
2880 hours /
120 Days to Decision
Computing
720
Analysis
SCALABLE COMPUTING (in hours)
Higher Quality Output, Iterative Analysis,
Less Context Switching
Computing & Analysis
POST ADOPTION: AGILE DESIGN PROCESS
8

48. © Copyright Cycle Computing LLC | All Rights Reserved
PAGE 48
#7 – Every smart person gets their own workspace
Old: Shared internal cluster
• Competition for resources
• Waiting in line for compute
• Zero sum game between users
New: Cluster Per Researcher
• Remove bottlenecks
• Cost controls to manage $
• No waiting = 2x faster users
User
User User UserUser User UserUserUser
User
User User

49. © Copyright Cycle Computing LLC | All Rights Reserved
PAGE 49
Lessons Learned Summary
1. Zero Queue Wait for
computing
2. Any scale, Any time
3. Users with unique
requirements are ok
4. Performance goes up over
time, same cost
5. Time and Cost are the sole
metrics
6. Faster iterations
7. Every researcher gets their
own workspace
49

50. © Copyright Cycle Computing LLC | All Rights Reserved
PAGE 50
The Solution: CycleCloud for Cloud HPC & Big Compute
User
Inputs
Existing Workflows
Data Dependencies Instance types
Applications
Scalability
Budget Controls
AuthorizationSecurity Stack
IT LOB
Inputs
Job scripts & data
Cloud accounts
Storage / Data sources
OS variations
AD / LDAP
Audit/Compliance data
Usage data (User, Group, App)
Job run-time by instance data
AppServer platform
Internal

51. Questions
& Answers

52. Contact Information
Michael Basilyan
Product Manager
basilyan@google.com
cloud.google.com
Scott Jeschonek
Director of Cloud Products
scottj@averesystems.com
AvereSystems.com
Rob Futrick
CTO
rfutrick@cyclecomputing.com
CycleComputing.com