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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

Solving enterprise challenges through scale out storage & big compute final

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Page 1: Solving enterprise challenges through scale out storage & big compute final

WEBINAR

Solving Enterprise Business ChallengesThrough Scale-Out Storage & Big ComputeMichael Basilyan, Product Manager, Google Cloud PlatformScott Jeschonek, Director of Cloud Products, Avere SystemsRob Futrick, CTO, Cycle Computing

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Housekeeping

• Slides

• Questions

• Recording

• Attachments

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Presenters

Michael BasilyanProduct Manager

Scott JeschonekDirector of Cloud Products

Rob FutrickCTO

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Introduction to Google Cloud PlatformFocusing on Compute Engine & Storage

Michael Basilyan [email protected] Manager, GCE

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Agenda

• Google Cloud Overview

• Compute Engine VMs:

• GCE VM Instances & Managed Infrastructure

• Storage:

• Block Storage

• Cloud Storage

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What is Google Cloud Platform?

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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

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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

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GCE: Compute & VM Features

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VM Live Migration = No Downtime

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Custom Machine TypesAverage Savings: 19%

Create VMs shaped for your workloads instead of shaping your workloads to fit pre-defined VMs.

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Preemptible VMsIdeal for batch, grid, and fault-tolerant workloads

Save 80% off regular VM list prices: flat $0.01 per core hourFlat pricing with no complex bidding or competitionSame performance (CPU, I/O, Net) as regular VMs

Example uses: Hadoop, Rendering/Transcoding, Genomics, Monte Carlo Simulations, etc.

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Managed Infrastructure - zero devops for IaaSCreate 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

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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

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Block & Object Storage

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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)

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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)

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Block StorageReliable, high-performance block storage for virtual machine instances on GCE

Standard Persistent Disk SSD Persistent Disk Local SSD

Targetscenarios

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, Snapshots64 TB, Disk Size sets performance

(Attach larger VMS for max SSD performance)

Encryption3TB

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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.

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Regions and Zones

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–––– 20182018

Current regions and number of zones

Edge points of presence

Network

Committed regions for 2017 and number of zones

#

# https://peering.google.comhttps://cloud.google.com/compute/docs/regions-zones/regions-zones

Google Cloud Platform InfrastructureGoogle Cloud Platform is built on a datacenter network infrastructure that supports Google scale, performance, and availability

2

3

Singapore2

S Carolina

N Virginia

BelgiumLondon

Tokyo

TaiwanMumbai

Sydney

OregonIowa

Frankfurt

São Paulo

Finland

3

3

33

3

3

2

43

3

3

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Cloud HPC: Data Access ChallengesScott Jeschonek, Director of Cloud Products

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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

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“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)

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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

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Hybrid HPC

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Grids On-Demand

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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

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Using a Data Access Layer

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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

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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

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Lessons Learned from 10 YearsHow Cloud Changes Big ComputeRob Futrick, CTO

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The BroadInstitute

Need: 270,000 hours of computing

Why: Machine learning to map relationships among cancer datasets

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© 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

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© 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

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© 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

Page 37: Solving enterprise challenges through scale out storage & big compute final

© 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 / LDAPAudit/Compliance data

Usage data (User, Group, App)

Job run-time by instance data

AppServer platform

Internal

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© 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

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© 2016 Copyright | All rights reserved

7 Lessons Learned

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© Copyright Cycle Computing LLC | All Rights Reserved

PAGE 40

#1 – Zero waiting in line for compute

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© 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

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© 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

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© Copyright Cycle Computing LLC | All Rights Reserved

PAGE 43

#4 – Cloud gets faster/cheaper over time

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© Copyright Cycle Computing LLC | All Rights Reserved

PAGE 44

#5 – Time & cost are the sole metrics that matter

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© Copyright Cycle Computing LLC | All Rights Reserved

PAGE 45

Everythingyou don’t think about!

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© 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

Computing720

AnalysisSCALABLE COMPUTING (in hours)

720

Computing Analysis Analysis

1456 hours /60.6 Days to Decision

7208

Computing

ANTICIPATED BENEFIT (in hours)

8

Page 47: Solving enterprise challenges through scale out storage & big compute final

© 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

Computing720

AnalysisSCALABLE COMPUTING (in hours)

Higher Quality Output, Iterative Analysis, Less Context Switching

Computing & AnalysisPOST ADOPTION: AGILE DESIGN PROCESS

8

Page 48: Solving enterprise challenges through scale out storage & big compute final

© 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

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© 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

Page 50: Solving enterprise challenges through scale out storage & big compute final

© 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 / LDAPAudit/Compliance data

Usage data (User, Group, App)

Job run-time by instance data

AppServer platform

Internal

Page 51: Solving enterprise challenges through scale out storage & big compute final

Questions& Answers

Page 52: Solving enterprise challenges through scale out storage & big compute final

Contact Information

Michael BasilyanProduct Manager

[email protected]

Scott JeschonekDirector of Cloud [email protected]

AvereSystems.com

Rob FutrickCTO

[email protected]