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Efficient Mixed-Platform Clouds
Phillip B. Gibbons, Intel Labs
Michael Kaminsky, Michael Kozuch, Padmanabhan Pillai (Intel Labs)
Gregory Ganger, David Andersen, Garth Gibson (Carnegie Mellon)
NSF Workshop on
Sustainable Energy Efficient Data Management
May 2, 2011
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Cloud Computing & Homogeneity
• In near future, significant fraction of all data
analysis and data storage will occur in the cloud
• Traditional data center goal: Homogeneity
+ Reduce administration costs: maintenance, diagnosis, repair
+ Ease of load balancing
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Ideal: single Server Architecture tailored to the workload
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Homogeneity: Challenges
• No single workload: Mix of customer workloads
– Computation-heavy apps (powerful CPUs, little I/O BW)
– Random I/O apps (I/O latency bound)
– Streaming apps (I/O BW bound, little memory)
– Memory-bound apps
– Apps exploiting hardware assists such as GPUs
• Common denominator Server Architecture falls short
– E.g., Two orders of magnitude loss in energy efficiency
(see example on next slide)
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FAWN: Fast Array of Wimpy Nodes
• For key-value stores, FAWN provides 120X more
queries per Joule than traditional server
• FAWN great for some workloads, terrible for others
Homogeneity
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New Goal: Specialization
•Specialization is fundamental to efficiency
– No single platform best for all application types
– e.g., huge efficiency gains in FAWN
– Called division of labor in sociology (see also, bees)
•Cloud computing must embrace specialization
– and consequent heterogeneity and change-over-time
Specialization is fundamental to
sustainable energy-efficient data management
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Efficient Mixed-Platform Clouds
Cloud in 2020 will need…
• Infrastructure purposely composed of many
platform types, some general-purpose and some
specialized to particularly important application types
• Infrastructure embraces heterogeneity by design
• Nimble incorporation of new technologies
is enabled by explicitly aiming for heterogeneity
– E.g., solid state RAM and accelerators
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Efficient Mixed-Platform Cloud
Research Agenda
• Develop specializations motivated by important
application types
• Algorithms/frameworks for exploiting specializations
• Making applications able to work on varied platforms
– And automatically mapping them to best platform, accounting
for where the data is
• Explore disruptive impact of new technologies
– integration into systems, exploitation by applications
• Data management in mixed-platform cloud
Our progress to date on specializations: See FAWN [SOSP’09],
Hi-Spade [Sigmod’10,Sigmod’11], PCM-DB [CIDR’11] projects
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Coming Soon:
Intel Science and Technology Center
on Cloud Computing (ISTC-CC)
• Pending approvals, legal
agreements, etc
• $2.5M / year for 3-5 years
• Homed at Carnegie Mellon
• 4 Intel researchers
Research Agenda
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Back Up Slides
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Defining Cloud Computing…
• Easy to get mired in defining cloud computing
– we really want to avoid doing so (again )
• NIST ended up with a 2-page definition
– here’s their 15th version, for reference:
• Is it …
– Amazon Web Services (EC2, S3, etc.) ?
– Google Apps + Chrome ?
– Private clouds based on VMware/Eucalyptus/etc ?
– Hadoop / MapReduce ?
– NoSQL DBs (Cassandra, etc.) ?
• All are examples of broad collection of trends
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Cloud in 2020?
•Huge range of uses, exploiting …
– shared, managed resources
– needs to be massive scale, efficient, automated, trustworthy
– availability of interesting data
– needs to support BIG DATA, sensor data, mining of both
– convenient on-demand access from anywhere
– needs to be elastic, easy-to-use, location-independent
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