Network Services Abstractions for Edge Computing · Network Services Abstractions for Edge...

Network Services Abstractions for

Edge ComputingCOST ACROSS 2017

[email protected]

September 3, 2017

Next-gen applications and services leveraging advanced networking technologies

in smart and connected communities

Outreach

In partnership withwith withwith

Out InPeople use the Internet Devices use the Internet

Move data to computing Move computing to the data

Exploit massive datacenters & networks Exploit locality

Validated datasets Perishable data streams

Abundant inter-city backbone bandwidth Abundant intra-city access bandwidth

Bandwidth is the key measurement Latency is the key measurement

Wait for the response Predictable, deterministic response time

Computers model and monitor real world Computers are integral parts of real world

Glenn’s 2017 In-and-Out List

North America (ARIN)

Europe (RIPE)

Latin America (LACNIC)

Asia Pacific (APNIC)

Africa (AFRINIC)

“Backbone” (highly connected

networks)

Date: July 11 2015

Credit: Barrett Lyon / The Opte Project

Visualization of the routing paths of the Internet.

The End of End-to-End

Natural Edges

Changes in:

- Power availability

- Compute / storage / networking capability

- Aggregation / sharing possibilities

- Mobility

- Competition

- Regulation boundaries

- System federation boundaries

- Services boundaries (for composite services / NFV)

Examples of Edges

- Personal edge (e.g., smartphone)

- Home edge (e.g., Google Home)

- Vehicle edge (e.g., Android Auto)

- Farm edge

- Neighborhood edge (e.g., Brambleton)

- Business edge (company IT department)

- City edge (e.g., Digital Town Square)

- State / regional edge

- Trading partner edge (e.g., EU, NAFTA, Mercosur)

Why City Edges?

Economics: Price for inter-city communication

declining less rapidly than computing and

storage prices.

Break-even is in the range of 5-50Mbps =

relocation of one server John Chung-I and Marvin A. SIRBU

Carnegie Mellon University

Pricing Multicast Communications:

A Cost-Based Approach

Why City Edges?

Delay: response time limited by:

- Speed of light

- Economics of peering point locations

- Store-and-forward delays

- Queuing delays (congestion)

Locality:

- Data / AI / CPS / is only meaningful locally

- Data location requirements

- Resilience in times of natural disasters

- Supports local digital economy

What Belongs at a City Edge?

Economics:

- High I/O to compute ratio (e.g., IoT)

Delay:

- Requires real-time (or near real-time) response

Locality:

- Requirements of General Data Protection Regulation (GDPR)

- Resilience requirements (survivability)

- Sustainability (e.g., CO2 management)

Credit: iScoop.eu based on Cisco data

City Edge Computing

The Future of Cloud Computing and the Internet

Drivers:

Explosion of M2M and IoT drives traffic and scale

City-based clouds now have sufficient scale

M2M and IoT data usually has locality and is perishable

Cyberphysical systems (CPS) (e.g., microgrid coordination) need low latency

Desire for civic resilience and digital self-sufficiency

Sample Applications:

Interactive and streaming VR/AR (experiential education)

Home health monitoring and intervention\

AI and personal assistants (e.g., cost-reduced robots)

City-wide autonomous vehicle scheduling

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Global City Teams

Challenge (GCTC)

Lexington, KY: July 19

Las Vegas, NV: July 19

HomeEdge

Learner-directedEducation

Safety ormedicalrobot

Real-time health monitoring


Non-stop intersection management

Incident

WirelessHead-end

Crowd-sourced incident management

Smart streetlight

FiberHead-end

CommunityEdge Cloud

ProgrammableNetwork

To provider’s own distantcloud


Advanced networking technologiesenable smart communities

GR 3/11/2017

HomeEdge






Incident

WirelessHead-end


Smart streetlight

FiberHead-end

CommunityEdge Cloud

ProgrammableNetwork




Sensors

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HomeEdge






Incident

WirelessHead-end


Smart streetlight

FiberHead-end

CommunityEdge Cloud

ProgrammableNetwork




Sensors

Feed real-time big-data and AI analytics

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HomeEdge






Incident

WirelessHead-end


Smart streetlight

FiberHead-end

CommunityEdge Cloud

ProgrammableNetwork




Sensors

Feed real-time big-data and AI analytics

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Example Edge Abstractions (Virtualization Affordances)


Four of the Possible Edge Abstrations (Virtualization Affordances):

Secure Connections to IoT devices

Information compression (discard/summarize older / less useful information)

Network provides low latency as required by each application

Services abstraction

Security Virtualization for IoT Devices


IoT Devices may have limited power, encryption capability security

Possible virtualizations for security:

- Very low-rate devices built with one-time pads on-board

- May have one-time responses on-board for challenge message responses

- Created with MAC addresses that have cryptographic properties

- Network routes packets based only on MAC source address (ignores

destination address and layer 3 addresses)

- Transmit/receive windows vary in time

- If connected wirelessly and stationary:

- Radio waveform multi-path signature matches verified signature

- If connected wirelessly and mobile:

- Signal strength to multiple receiving stations reasonable for reported

GPS location

Virtualization for Information Compression


During periods of network overload / delays:

Older information may be summarized or discarded

because IoT information is often localized and perishable.

Low Latency Virtualizations as Required for IoT Devices


Network priorities and timeslots may be assigned based on the specific latency

requirements of the type of IoT device and its current applications

Wirelessly: Frequency or coding assignments may also vary

Less demanding devices/applications/services may be given contention service

More demanding devices/applications/services may be given reservations or

time slots with less contention possibility

Locality and Repeating Service Properties help predict future behavioral needs

Low Latency Virtualizations


Network priorities and timeslots may be assigned based on the specific latency

requirements of the type of IoT device and its current applications

Services Orchestration Layer

Resources Orchestration / Slicing Layer

Resources Layer

Micro-scheduling / Hypervisor Layer

Composite Services Layer

A better abstraction


Instead of considering network flows …..

Consider network-delivered “services” as the main abstraction (related to NFV)

Each “service” may be composed of related compute, storage, software, and

network components.

Each service has its own billing and response requirements.

Services can provide virtualization idealizations (such as security, compression, …)

The Larry Peterson Services Abstraction (key issues)


Services may be composed of other services (microservices).

Resources (containers, VMs, network links, virtualizations) are services.

Services have an implicit security wrapper around them.

Services may either have a single tenant (client) or multiple tenants (clients).

The dependent services of a composite service are part of the composite service

manifest.

Ricart adds:

Services could be versioned (similar to Docker) for stability.

Multiple versions of a service could be active at once.

Once there is no tenant for a service, it may be lazily deallocated.

Services can be added by federation (put service wrapper on external services).

Services could be (multiply) signed.

CORD (OpenCloud) = Open-source services abstraction


Created by Larry Peterson, ON.LAB, and others with broad industry support

Open-source

Evolving rapidly

Being used for mobile NFV (M-Cord) by AT&T

Current Status


Considering adapting CORD to both academic research and production support

for prototype city edges and for wireless NFV under PAWR (workshop October

15-16, Snowbird, Utah, USA).

Low-Latency

ApplicationsFor smart and connected

communities

Streaming virtual and augmented reality

Interactive 4K video

Personal assistants think faster than you do

Intra-beat cardiac monitoring

Streaming intelligence to inexpensive robots

Microgrid millisecond coordination

City-wide optimized autonomous vehicles

Hospital-quality in-home health monitor

Real-time public safety information fusion

Dynamically optimized emergency response

Interactive & collaborative 3D model design

3D telerehabilitation

“Natural” Teleconferencing

Network Services Abstractions for Edge Computing · Network Services Abstractions for Edge...

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