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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
Housekeeping
• Slides
• Questions
• Recording
• Attachments
Presenters
Michael Basilyan
Product Manager
Scott Jeschonek
Director of Cloud Products
Rob Futrick
CTO
Introduction to Google Cloud Platform
Focusing on Compute Engine & Storage
Michael Basilyan
basilyan@google.com
Product Manager, GCE
Agenda
• Google Cloud Overview
• Compute Engine VMs:
• GCE VM Instances & Managed Infrastructure
• Storage:
• Block Storage
• Cloud Storage
What is Google Cloud
Platform?
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
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
GCE: Compute & VM Features
VM Live Migration = No Downtime
Custom Machine Types
Average Savings: 19%
Create VMs shaped for your workloads instead of shaping your workloads to fit pre-defined VMs.
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.
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
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
Block & Object Storage
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)
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)
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
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.
Regions and Zones
–––– 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
Cloud HPC:
Data Access Challenges
Scott Jeschonek, Director of Cloud Products
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
“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)
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
Hybrid HPC
Grids On-Demand
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
Using a Data Access Layer
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
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
Lessons Learned from 10 Years
How Cloud Changes Big Compute
Rob Futrick, CTO
33
The Broad
Institute
Need: 270,000 hours of
computing
Why: Machine learning to
map relationships among
cancer datasets
© 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
© 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
© 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
© 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
© 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
© 2016 Copyright | All rights reserved
7 Lessons Learned
© Copyright Cycle Computing LLC | All Rights Reserved
PAGE 40
#1 – Zero waiting in line for compute
© 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
© 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
© Copyright Cycle Computing LLC | All Rights Reserved
PAGE 43
#4 – Cloud gets faster/cheaper over time
© Copyright Cycle Computing LLC | All Rights Reserved
PAGE 44
#5 – Time & cost are the sole metrics that matter
© Copyright Cycle Computing LLC | All Rights Reserved
PAGE 45
Everything
you don’t
think about!
© 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
© 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
© 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
© 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
© 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
Questions
& Answers
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

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Solving enterprise challenges through scale out storage & big compute final

  • 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.
  • 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
  • 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
  • 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