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変通 [hen-tsoo] noun1. Resourcefulness – the quality of being able to cope with a difficult situation2. Adaptability – the ability to change (or be changed) to fit changed circumstances3. Agility – the power of moving quickly and easily; nimbleness
INFINITELY SCALABLE CLUSTERSGrid computing on public cloud
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WELCOME TO HENTSŪ
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AGENDA• Grid computing overview• Trusted tools moving into public cloud• Alternative cloud services
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SOME BACKGROUND
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TERMINOLOGY• Public Cloud (AWS, Azure,
Google)• Private Cloud (Your
datacentre)• High Performance Computing
(HPC)• Grid computing• Compute cluster• Mathworks MATLAB
• CPUs / Processors / Cores• RAM (processor storage)• Disk (physical storage)• IaaS (virtual hardware and
networking)• PaaS (software services)
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WHAT IS PUBLIC CLOUD?“A service provider makes resources, such as virtual machines, applications and storage, available to the general public.”• Utility model• No contracts• Shared hardware / multi tenant• Self managed
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WHAT IS GRID COMPUTING?Traditional resource limitations:• Data store performance • PC Processor / Memory / Storage• Network bandwidthThe researcher may wait a long time for results.
• Grid computing moves the computational work from the PC to a cluster of servers
• The cluster processes the data on behalf of the researcher and returns the results
• Processing time is reduced• Larger datasets can be tackled
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KEY CONCEPTSThe challenges The workflows
Number of tasks
Size
of
data
Big Data
High Throughput Computing
MapReduce
High Performance Computing
Ingest Process
Analyse
Visualise
Store
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CHOICE OF TOOLS AND PLATFORMS
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TRUSTED TOOLS & PUBLIC CLOUD
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HARDWARE INFLEXIBILITY• Buy 22 core processors at
2.2GHz or 6 core processors at 3.6GHz?
• Buy 8GB, 16GB or 32GB memory modules (RAM per core ratio)?
• Graphical Processing Units (GPUs)?
• How much local storage per server?
• What network devices between servers (32 or 48 port switches?)
• What size file server?
Monday Tuesday Wednesday Thursday Friday Saturday Sunday0
20
40
60
80
100
120
Date
Jobs
per
day
Grid usage varies depending on research priorities:
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PROFILING MATLAB RESOURCE USAGE• MATLAB uses one processor
core at a time (50% on a 2 vCPU machine). Use parallel computing toolkit for multicore PCs.
• MATLAB stores all data in RAM, very little I/O while processing
• I/O spike when writing out results
SysInternals Process ExplorerOctober 2016
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MATLAB GRID WITH PUBLIC CLOUD- Pay only for what you
use- Scale compute resource
up AND down- Minimal capital outlay
on hardware- Experiment with grid
computing platforms quickly, cheaply and with no commitment
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A DAY IN A PUBLIC CLOUD CLUSTER
Time 02:00:0004:10:0006:20:0008:30:0010:40:0012:50:0015:00:0017:10:0019:20:0021:30:0023:40:000
20
40
60
80
100
120
140
160
180
Workers Tasks in Queue
- Cluster consisting 32x 4 cores
- Max 128 worker nodes- Ramps up as jobs get
submitted- Tears down nodes when
jobs finished- Minimising costs when not
in use
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IDEAL CLUSTER SIZE?
8 16 32 64 96 128 160 192 2240
200
400
600
800
1000
1200
1400
Job Run time in seconds
Cores
Seco
nds
Ingest Process
Analyse
Visualise
Store
Optimise other parts of the workflow?
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RUNNING MATLAB CLUSTER ON IAASAWS vCPUs are hyper-threaded™
Each vCPU is a hyper thread of an Intel Xeon core for 2nd generation instance types(M4, M3, C4, C3, R3, HS1, G2, I2, and D2)https://aws.amazon.com/ec2/instance-types/
Azure does not overcommit memory or cores. vCPUs are physical cores.Azure does not use hyper-threading.https://aws.amazon.com/ec2/instance-types/
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GRID DEPLOYMENT OPTIONS1. Infrastructure as a Service (IaaS) DIY
Spin up a compute cluster on VMs for additional capacity and new workloads
2. BurstUse existing on premises compute cluster and burst on cloud as required
3. Software as a Service (SaaS)Software vendors and Managed Service Providers provide their own SaaS solutions. Pay for compute and application software per hour
4. Platform as a Service (PaaS)Cloud providers’ data analytics platform as a service:Google BigQuery & Datalab, Microsoft HDInsight, Amazon EMR
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CLOUD HOSTED DATA AND ANALYTICS AS A SERVICE
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GOOGLE BIG DATA REFERENCE ARCHITECTURE
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WHAT IS BIGQUERY?Hadoop based “service that enables interactive analysis of massively large datasets”• Distributed File System -
Stores data that’s larger than can fit on a single machine
• Map Reduce – Distributes processing across multiple systems
http://blogs.forrester.com/mike_gualtieri/13-06-07-what_is_hadoop October 2016
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GOOGLE BIGQUERY AND DATALAB DEMO
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DON’T FORGET SECURITYSecurity considerations:• Secure transfer and storage of data and code• Secure remote access to cloud hosted environment• Secure authentication
• Windows AD credentials• AWS IAM credentials• Google accounts• Microsoft accounts
• Auditing (who accessed what, who changed what)
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SUMMARY• Traditional grid and HPC tools can benefit from moving into cloud• Vast landscape of available tools• Off-the-shelf PaaS offerings• Integrations and ecosystems• Cheap and very quick to experiment
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NEXT EVENT: JANUARY 2017Intellectual Property (IP) security for Public Cloud ServicesSecuring mobile email and cloud based file storage
October 2016