Edge ppt

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Edge Computing
© 2019 Carnegie Mellon University
[DISTRIBUTION STATEMENT A] Approved for public release and unlimited distribution.
Software Engineering Institute
Carnegie Mellon University
Pittsburgh, PA 15213
SCSS 2019
Software and Cyber Solutions Symposium: Benefits and Risks of Cloud Computing
Edge Computing: Use Cases
and Challenges
Grace A. Lewis

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Edge Computing
Copyright 2019 Carnegie Mellon University. All Rights Reserved.
This material is based upon work funded and supported by the Department of Defense under Contract No. FA8702-15-D-
0002 with Carnegie Mellon University for the operation of the Software Engineering Institute, a federally funded research
and development center.
The view, opinions, and/or findings contained in this material are those of the author(s) and should not be construed as an
official Government position, policy, or decision, unless designated by other documentation.
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FURNISHED ON AN "AS-IS" BASIS. CARNEGIE MELLON UNIVERSITY MAKES NO WARRANTIES OF ANY KIND,
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FREEDOM FROM PATENT, TRADEMARK, OR COPYRIGHT INFRINGEMENT.
[DISTRIBUTION STATEMENT A] This material has been approved for public release and unlimited distribution. Please see
Copyright notice for non-US Government use and distribution.
This material was prepared for the exclusive use of SCSS 2019 and may not be used for any other purpose without the
written consent of permission@sei.cmu.edu.
DM19-0872

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Edge Computing
Edge Computing
Idea is to push applications, data and computing
power to the edge of the Internet, in close
proximity to mobile devices, sensors, and end
users
An early example is Akamai, with servers around
the world to distribute web site content from
locations close to the user (content delivery
networks, or CDNs)

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Edge Computing
Edge Computing: Drivers
Latency
• data processing close to where it originates avoids round-trip time to the
cloud
Bandwidth
• optimization of communication to and from the cloud
Privacy/security
• sensitive data stays local
Connectivity
• continued processing (in some cases) despite lack of connectivity to the
cloud
Local dependencies
• data processing close to points of interaction with end users and other
system components

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Edge Computing
Edge Computing: The Telco View
Opportunity for providing edge computing
devices in existing infrastructure
• e.g., micro data centers at the base of cellular
towers
Multiple organizations seeking standardization:
Multi-Access Edge Computing (MEC), Open
Edge Computing (OEC), OpenFog consortium,
etc.
Business model is still not clear: Who pays for
the service? Consumer? Content Provider?
Edge Computing according to the Open Edge Computing
Initiative [1]

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Edge Computing
Edge Computing: The Cloud Provider View
Goal is mainly to provide
• Content Delivery Network (CDN) services
• IoT data processing and aggregation for
data in transit to the cloud
Examples
• Azure IoT Edge — deploy business logic to
edge devices and monitor from the cloud
• Amazon
- AWS CloudFront — CDN Service, includes
Lambda@Edge
- AWS Greengrass — connected IoT devices
can run AWS Lambda functions and other
code on locally-collected data
Industrial IoT: IoT to Edge to Cloud [2]

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Edge Computing
Edge Computing: The “Appliance” View
Goal is to provide a “data center in a box”
to push cloud computing capabilities to
the edge
• Often combined with networking
capabilities such as edge gateways
and smart routers
Many players in this space, such as
Amazon, Cisco, Dell EMC, HPE, etc.
Disconnected Operations
AWS Snowball Edge — large-scale data
transfer service with an embedded
computing platform (based on AWS
Greengrass plus Lambda functions)
Snowball Edge Device [3]

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Edge Computing
Opportunities for DoD and Government
Edge Computing via “appliances” can
provide computation and data to support
a wide variety of missions
• Military
• Humanitarian
• Public safety
• Public service

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Edge Computing
Computation and Data in
Disconnected Environments
Providing computation-intensive capabilities
and data at the edge when there is no access
to the cloud
• Speech recognition
• Face recognition
• Speech translation
• Image recognition
• Image processing
• Air/water quality analysis
Tactical Cloudlets [4][5][6]

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Edge Computing
Data Pre-Processing, Filtering, and Pre-Fetching (Cloud
to Edge)
Using edge devices to
• pre-process,
• pre-fetch, or
• filter unnecessary data from streams
intended for mobile devices
Goal: Mobile devices receive only the
data that they need, when they need it
• reduced bandwidth
• reduced latency
• reduced cognitive load

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Edge Computing
Data Pre-Processing and Caching (Edge to Cloud)
Using edge devices to
• pre-process, or
• cache
data heading for enterprise repositories

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Edge Computing
Field Operations
People that spend time away from their main
offices or labs, such as researchers, medics,
and sales personnel, can leverage portable
surrogates to support their computation and
data needs
PowerSense: Image Processing for Dengue Detection [7]
Leverages
microfluidic
paper-based
analytical
devices (μPADs)

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Edge Computing
Resource-Challenged Environments
Less-privileged regions characterized by limited Internet access, limited
electricity and network access, and potentially low levels of literacy can
leverage surrogates to obtain information to support their communities
AgroTempus Features [8]
• Surrogates in villages
download and cache
data from mobile hub
• Surrogates upload field-
collected data to the
mobile hub which
eventually syncs with the
cloud

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Edge Computing
Challenges
Hardware (especially in the context of Edge AI)
Privacy
Security
Data and computation allocation to edge devices
(especially at runtime)
Resource discovery
Google Edge TPU BETA [9]
The Joneses (2009)

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Edge Computing
Summary
Edge Computing is about pushing applications, data
and computing power to the edge of the Internet, in
close proximity to mobile devices, sensors, and end
users
Edge Computing via “appliances” can provide
computation and data to support a wide variety of
missions
I challenge you to think about use cases for Edge
computing beyond IoT
• Military
• Humanitarian
• Public safety
• Public service

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Edge Computing
References
[1] Open Edge Computing Initiative. http://openedgecomputing.org (2019)
[2] Open Automation Software. IIoT Edge Computing vs. Cloud Computing. https://openautomationsoftware.com/blog/iiot-
edge-computing-vs-cloud-computing/ (2019)
[3] Amazon. AWS Snow Family. https://aws.amazon.com/snow/?c=17&pt=6 (2019)
[4] Echeverría, Sebastián, Grace A. Lewis, James Root, and Ben Bradshaw. "Cyber-foraging for improving survivability of
mobile systems." In MILCOM 2015-2015 IEEE Military Communications Conference, pp. 1421-1426. IEEE, 2015.
[5] Echeverría, Sebastián, Dan Klinedinst, Keegan Williams, and Grace A. Lewis. "Establishing trusted identities in
disconnected edge environments." In 2016 IEEE/ACM Symposium on Edge Computing (SEC), pp. 51-63. IEEE, 2016.
[6] Lewis, Grace A., Sebastián Echeverría, Dan Klinedinst, and Keegan Williams. "Secure VM migration in tactical
cloudlets." In MILCOM 2017-2017 IEEE Military Communications Conference (MILCOM), pp. 388-393. IEEE, 2017.
[7] Matthews, Jerrid, et al. "PowerSense: power aware dengue diagnosis on mobile phones." Proceedings of the First
ACM Workshop on Mobile Systems, Applications, and Services for Healthcare. ACM, 2011.
[8] Brion, Reuel. Demonstrator for a Cyber-Foraging System to Support Agricultural Knowledge Exchange in Resource-
challenged Environments. Masters Thesis. VU University Amsterdam. 2015.
[9] Google. Edge TPU BETA. https://cloud.google.com/edge-tpu/ (2019)

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Edge Computing
Contact Information
Grace A. Lewis
Tactical and AI-Enabled Systems (TAS) Initiative
Software Solutions Division (SSD)
Software Engineering Institute
4500 Fifth Avenue
Pittsburgh, PA 15213-2612
USA
Phone: +1 412-268-5851
Email: glewis@sei.cmu.edu
WWW: http://www.sei.cmu.edu/staff/glewis

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