A Hybrid Cloud Architecture for a Social Science Research Compu9ng Data Center
June 30, 2014 @ DCPerf 2014
Len Wisniewski
Director, Research Technology Services Ins9tute for Quan9ta9ve Social Science, Harvard University
Joint work with Steve Abramson and Bill Horka
30 June 2014
Social Science Research Problems
• Examples • Sta9s9cal analysis (EdX) • Social network analysis (TwiTer) • Text analysis (PDF scraping) • Geographic analysis (WorldMap) • Qualita9ve analysis (survey data)
Collect Data
Store Data
Clean Data
AnalyzeData
Archive & Share Data
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Social Science Research requirements
• Easy to use • Availability of GUIs for familiar applica9ons
• Scalable analysis • Scalable data • Scalable computa9on
• Secure storage • Confiden9al data • Harvard Level 3, 4, 5 data (see security.harvard.edu)
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IQSS Research Technology Services
Research Technology Consul9ng
Infrastructure Research & Development
Infrastructure Opera9ons
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RCE in detail
• RCE = Research Compu9ng Environment • Used to run common sta9s9cal applica9ons
– R, GAUSS, Mathema9ca, MATLAB, Octave, SAS, S-‐PLUS, Stata
• RCE has three types of nodes – Login nodes
• User logs in via NX (similar to VNC) and gets a desktop session • User can launch an applica9on directly from the desktop
– Compute-‐on-‐demand nodes • User has special “RCE Powered Applica9ons” menu to launch applica9ons on machines with large memory resources (up to 250 GB)
– Batch nodes • Used typically for non-‐interac9ve, long-‐running, scalable jobs • Most jobs use R
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RCE architecture and configura9on
Resource Manager
User Secure login
via NX
Interac9ve nodes
Allocate and
manage
resources
Local
Remote
Key Applica+ons R GAUSS Mathema9ca MATLAB Octave SAS S-‐PLUS Stata (SE and MP)
Batch nodes
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RCE batch resource usage
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Batch nodes and R
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The case for RCE and the cloud
• To advance research compu9ng technologies, we need to focus less on commodity services
• External vendors manage large commodity clusters more efficiently than any in-‐house opera9on
• Embarassingly parallel queries, the bulk of social science data analysis, are ideal research to benefit from the cloud resources
• The RCE’s structure allows a gradual transi9on and hybrid infrastructure
• Clouds will expand the range of hardware and plahorm support for all researchers
30 June 2014
The hybrid model: RCE and the cloud
Remote …
Resource Manager
User Secure login
via NX
Interac9ve
Allocate and
manage
resources
Local
Key Applica+ons R GAUSS Mathema9ca MATLAB Octave SAS (COD only) S-‐PLUS Stata (SE and MP)
R GAUSS Mathema9ca MATLAB Octave SAS S-‐PLUS Stata (SE and MP) R Octave
Batch nodes
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Cloud advantages
• Elas9city – Avoids over-‐ and under-‐resourcing – Eliminates need to pay for resources not in use – Accesses much larger set of resources when needed
• Increased research compu9ng focus – Offloads hardware maintenance to the “experts” – Focuses local staff on working with researchers to develop the next genera9on
of social science compu9ng tools
• Customized user environments – Sets up each cloud OS image with only the somware needed
• More direct accoun9ng of usage – Reduces divisional upfront commitment – Charges project for specific 9me / resources used
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Moving exclusively to the cloud
Local
Remote …
Resource Manager
User Secure login
via NX
Interac9ve
Allocate and
manage
resources
Challenges 1. Managing
number of nodes in cluster
2. Securing communica9on between local and remote resources
3. Syncing local and remote data
4. Managing cost for high-‐memory nodes
5. License management and connec9on issues for interac9ve apps
Batch nodes
1
2
3
4
5
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Implementa9on
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Future work
• Simula9on • Expanding to other clouds • Distributed file systems • Securely isola9ng jobs • Hierarchical databases
