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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 onSustainable 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

Ideal: single Server Architecture tailored to the workload

CPU

MemDisk

CPU CPU

CPU CPU

MemDisk

CPU CPU

CPU CPU

MemDisk

CPU CPU

CPU CPU

MemDisk

CPU CPU

CPU CPU

MemDisk

CPU CPU

CPU

…

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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

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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…

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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