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Where the Edges Meet: Public Cloud Edge Interface

By Akraino, Akraino Edge Stack, Blog

Written by Oleg Berzin, Ph.D., a member of the Akraino Technical Steering Committee and Senior Director Technology Innovation at Equinix

Introduction

Why 5G

5G will provide significantly higher throughput than existing 4G networks. Currently, 4G LTE is limited to around 150 Mbps. LTE Advanced increases the data rate to 300 Mbps and LTE Advanced Pro to 600Mbps-1 Gbps. The 5G downlink speeds can be up to 20 Gbps. 5G can use multiple spectrum options, including low band (sub 1 GHz), mid-band (1-6 GHz) and mmWave (28, 39 GHz). The mmWave spectrum has the largest available contiguous bandwidth capacity (~1000 MHz) and promises dramatic increases in user data rates. 5G enables advanced air interface formats and transmission scheduling procedures that decrease access latency in the Radio Access Network by a factor of 10 compared to 4G LTE.

The Slicing Must Go On

Among advanced properties of the 5G architecture, Network Slicing enables the use of 5G network and services for a wide variety of use cases on the same infrastructure. Network Slicing (NS) refers to the ability to provision a common physical system to provide resources necessary for delivering service functionality under specific performance (e.g. latency, throughput, capacity, reliability) and functional (e.g. security, applications/services) constraints.

Network Slicing is particularly relevant to the subject matter of the Public Cloud Edge Interface (PCEI) Blueprint. As shown in the figure below, there is a reasonable expectation that applications enabled by the 5G performance characteristics will need access to diverse resources. This includes conventional traffic flows, such as access from mobile devices to the core clouds (public and/or private) as well as the general access to the Internet, edge traffic flows, such as low latency/high speed access to edge compute workloads placed in close physical proximity to the User Plane Functions (UPF), as well as the hybrid traffic flows that require a combination of the above for distributed applications (e.g. online gaming, AI at the edge, etc). One point that is very important is that the network slices provisioned in the mobile network must extend beyond the N6/SGi interface of the UPF all the way to the workloads running on the edge computing hardware and on the Public/Private Cloud infrastructure. In other words, “The Slicing Must Go On” in order to ensure continuity of intended performance for the applications.


The Mobile Edge

The technological capabilities defined by the standards organizations (e.g. 3GPP, IETF) are the necessary conditions for the development of 5G. However, the standards and protocols are not sufficient on their own. The realization of the promises of 5G depends directly on the availability of the supporting physical infrastructure as well as the ability to instantiate services in the right places within the infrastructure.

Latency can be used as a very good example to illustrate this point. One of the most intriguing possibilities with 5G is the ability to deliver very low end to end latency. A common example is the 5ms round-trip device to application latency target. If we look closely at this latency budget, it is not hard to see that to achieve this goal a new physical aggregation infrastructure is needed. This is because the 5ms budget includes all radio/mobile core, transport and processing delays on the path between the application running on User Equipment (UE) and the application running on the compute/server side. Given that at least 2ms will be required for the “air interface”, the remaining 3ms is all that’s left for the radio/packet core processing, network transport and the compute/application processing budget. The figure below illustrates an example of the end-to-end latency budget in a 5G network.

The Edge-in and Cloud-out Effect

Public Cloud Service Providers and 3rd-Party Edge Compute (EC) Providers are deploying Edge instances to better serve their end-users and applications, A multitude of these applications require close inter-working with the Mobile Edge deployments to provide predictable latency, throughput, reliability, and other requirements.

The need to interface and exchange information through open APIs will allow competitive offerings for Consumers, Enterprises, and Vertical Industry end-user segments. These APIs are not limited to providing basic connectivity services but will include the ability to deliver predictable data rates, predictable latency, reliability, service insertion, security, AI and RAN analytics, network slicing, and more.

These capabilities are needed to support a multitude of emerging applications such as AR/VR, Industrial IoT, autonomous vehicles, drones, Industry 4.0 initiatives, Smart Cities, Smart Ports. Other APIs will include exposure to edge orchestration and management, Edge monitoring (KPIs), and more. These open APIs will be the foundation for service and instrumentation capabilities when integrating with public cloud development environments.

Public Cloud Edge Interface (PCEI)

Overview

The purpose of Public Cloud Edge Interface (PCEI) Blueprint family is to specify a set of open APIs for enabling Multi-Domain Inter-working across functional domains that provide Edge capabilities/applications and require close cooperation between the Mobile Edge, the Public Cloud Core and Edge, the 3rd-Party Edge functions as well as the underlying infrastructure such as Data Centers, Compute hardware and Networks. The Compute hardware is optimized and power efficient for Edge such as the Arm64 architecture.

The high-level relationships between the functional domains are shown in the figure below:

The Data Center Facility (DCF) Domain. The DCF Domain includes Data Center physical facilities that provide the physical location and the power/space infrastructure for other domains and their respective functions.

The Interconnection of Core and Edge (ICE) Domain. The ICE Domain includes the physical and logical interconnection and networking capabilities that provide connectivity between other domains and their respective functions.

The Mobile Network Operator (MNO) Domain. The MNO Domain contains all Access and Core Network Functions necessary for signaling and user plane capabilities to allow for mobile device connectivity.

The Public Cloud Core (PCC) Domain. The PCC Domain includes all IaaS/PaaS functions that are provided by the Public Clouds to their customers.

The Public Cloud Edge (PCE) Domain. The PCE Domain includes the PCC Domain functions that are instantiated in the DCF Domain locations that are positioned closer (in terms of geographical proximity) to the functions of the MNO Domain.

The 3rd party Edge (3PE) Domain. The 3PE domain is in principle similar to the PCE Domain, with a distinction that the 3PE functions may be provided by 3rd parties (with respect to the MNOs and Public Clouds) as instances of Edge Computing resources/applications.

Architecture

The PCEI Reference Architecture and the Interface Reference Points (IRP) are shown in the figure below. For the full description of the PCEI Reference Architecture please refer to the PCEI Architecture Document.

Use Cases

The PCEI working group identified the following use cases and capabilities for Blueprint development:

  1. Traffic Steering/UPF Distribution/Shunting capability — distributing User Plane Functions in the appropriate Data Center Facilities on qualified compute hardware for routing the traffic to desired applications and network/processing functions/applications.
  2. Local Break-Out (LBO) – Examples: video traffic offload, low latency services, roaming optimization.
  3. Location Services — location of a specific UE, or identification of UEs within a geographical area, facilitation of server-side application workload distribution based on UE and infrastructure resource location.
  4. QoS acceleration/extension – provide low latency, high throughput for Edge applications. Example: provide continuity for QoS provisioned for subscribers in the MNO domain, across the interconnection/networking domain for end-to-end QoS functionality.
  5. Network Slicing provisioning and management – providing continuity for network slices instantiated in the MNO domain, across the Public Cloud Core/Edge as well as the 3Rd-Party Edge domains, offering dedicated resources specifically tailored for application and functional needs (e.g. security) needs.
  6. Mobile Hybrid/Multi-Cloud Access – provide multi-MNO, multi-Cloud, multi-MEC access for mobile devices (including IoT) and Edge services/applications
  7. Enterprise Wireless WAN access – provide high-speed Fixed Wireless Access to enterprises with the ability to interconnect to Public Cloud and 3rd-Party Edge Functions, including the Network Functions such as SD-WAN.
  8. Distributed Online/Cloud Gaming.
  9. Authentication – provided as service enablement (e.g., two-factor authentication) used by most OTT service providers 
  10. Security – provided as service enablement (e.g., firewall service insertion)

The initial focus of the PCEI Blueprint development will be on the following use cases:

  • User Plane Function Distribution
  • Local Break-Out of Mobile Traffic
  • Location Services

User Plane Function Distribution and Local Break-Out

The UPF Distribution use case distinguishes between two scenarios:

  • UPF Interconnection. The UPF/SPGW-U is located in the MNO network and needs to be interconnected on the N6/SGi interface to 3PE and/or PCE/PCC.
  • UPF Placement. The MNO wants to instantiate a UPF/SPGW-U in a location that is different from their network (e.g. Customer Premises, 3rd Party Data Center)

UPF Interconnection Scenario

UPF Placement Scenario

UPF Placement, Interconnection and Local Break-Out Examples

Location Services (LS)

This use case targets obtaining geographic location of a specific UE provided by the 4G/5G network, identification of UEs within a geographical area as well as facilitation of server-side application workload distribution based on UE and infrastructure resource location.

 

Acknowledgements

Project Technical Lead: Oleg Berzin

Committers: Suzy GuTina Tsou Wei Chen, Changming Bai, Alibaba; Jian Li, Kandan Kathirvel, Dan Druta, Gao Chen, Deepak Kataria, David Plunkett, Cindy Xing

Contributors: Arif , Jane Shen, Jeff Brower, Suresh Krishnan, Kaloom, Frank Wang, Ampere

LF Edge Demos at Open Networking & Edge Summit

By Blog, EdgeX Foundry, Event, Fledge, LF Edge, Open Horizon, Project EVE, Secure Device Onboard

Open Networking & Edge Summit, which takes place virtually on September 28-30, is co-sponsored by LF Edge, the Linux Foundation and LF Networking. With thousands expected to attend, ONES will be the epicenter of edge, networking, cloud and IoT. If you aren’t registered yet – it takes two minutes to register for US$50 – click here.

Several LF Edge members will be at the conference leading discussions about trends, presenting use cases and sharing best practices. For a list of LF Edge focuses sessions, click here and add them to your schedule. LF Edge will also host a pavilion – in partnership with our sister organization LF Networking – that will showcase demos, including the debut of two new ones that feature a collaboration between Project EVE and Fledge and Open Horizon and Secure Device Onboarding. Check out the sneak peek of the demos below:

Managing Industrial IoT Data Using LF Edge (Fledge, EVE)

Presented by Flir, Dianomic, OSIsoft, ZEDEDA and making its debut at ONES, this demo showcases the strength of Project EVE and Fledge. The demo Fledge will show how the two open source projects work together to securely manage, connect, aggregate, process, buffer and forward any sensor, machine or PLC’s data to existing OT systems and any cloud. Specifically, it will show a FLIR IR Camera video and data feeds being managed as described.

 

Real-Time Sensor Fusion for Loss Detection (EdgeX Foundry):

Presented by LF Edge members HP, Intel and IOTech, this demo showcases the strength of the Open Retail Initiative and EdgeX Foundry. Learn how different sensor devices can use LF Edge’s EdgeX Foundry open-middleware framework to optimize retail operations and detect loss at checkout. The sensor fusion is implemented using a modular approach, combining point-of-sale , computer vision, RFID and scale data into a POC for loss prevention.

This demo was featured at the National Retail Federation Show in January. More details about the demo can be found in HP’s blog and  Intel blog.

               

Low-touch automated onboarding and application delivery with Open Horizon and Secure Device Onboard

Presented by IBM and Intel, this demo features two of the newest projects accepted into the LF Edge ecosystem – Secure Device Onboard was announced in July while Open Horizon was announced in April.

An OEM or ODM can generate a voucher with SDO utilities that is tied to a specific device. Upon purchase, they can send the voucher to the purchaser. With LF Edge’s Open Horizon Secure Device Onboard integration, an administrator can load the voucher into Open Horizon and pre-register the device. Once the device is powered on and connected to the network, it will automatically authenticate, download and install the Open Horizon agent, and begin negotiation to receive and run relevant workloads.

For more information about ONES, visit the main website: https://events.linuxfoundation.org/open-networking-edge-summit-north-america/. 

Exploration and Practices of Edge Computing: Cloud Managing Containerized Devices

By Blog, EdgeX Foundry, Industry Article, Trend

Written by Gavin Lu, LF Edge member, EdgeX Foundry China Project Lead and R&D Director in the VMware Office of the CTO

As an industry leader with vast experience and knowledge, Gavin has been writing a series of articles focused on edge computing. These articles are posted on his personal blog and are posted here with his permission. To read more content from Gavin, visit his website.

Introduction

The previous article introduced the cloud management virtualization device solution. This article will describe the Nebula project, a unified management of containerized devices and edge applications and data analysis cloud services.

Nebula Architecture

Project Nebula is designed based on the following key ideas:

  • Agnostic to device CPU architecture, supporting both x86 and ARM;
  • Agnostic to edge application frameworks, supporting EdgeX Foundry and other frameworks that can be packaged and run;
  • Agnostic to data analytics services, supporting on-premise and cloud deployment;
  • Support small to large scale deployment;
  • Support end-to-end multi-tenant operation model from device to cloud.
EdgeX Foundry Architecture

Nebula supports EdgeX Foundry framework, and we already published a live test bed at https://18.189.42.126/. Those who are interested in Nebula could contact yixingj@vmware.com to register for a trial, installation and user guides with detailed information.

Nebula Demo

Installation

Nebula is designed in containerized micro-service architecture, and is installed by default in OVA format. Similar to Pallas architecture introduced in the previous article, although Nebula package is encapsulated in OVA, it does not depend on any specific virtualization infrastructure or cloud platform to be installed. Technically, it could completely be converted to other formats, or install on any cloud platform that supports OVA format.

The basic resource requirement of Nebula is:

  • CPU: 2 virtual CPU cores
  • Memory: 8GB
  • Storage: 150GB

Its installation process is similar to other normal OVA, and users can log in as the administrator after completion.

Nebula Service Console

Vendor Portal

After the installation is complete, users can log in to the vendor portal as an administrator according to the prompt address in VM console as above and perform user management.

Nebula Management Portal

In Nebula, edge application services are defined as following: A Service can contain multiple Versions, and a Version contains multiple Service Components.

Edge Service Hierarchy

For each service created, it is necessary to determine parameters and resource requirement such as version, CPU platform, memory, storage, network, etc., to facilitate verification in full life cycle management.

Vendors can upload a set of EdgeX Foundry applications packaged in container images, and define categories, dependencies between containers, resource parameters, startup order, and parameters of connected data analysis cloud services.

After the release, users can see and deploy these edge services.

Device Registration

Before users actually deploy EdgeX Foundry applications, they must first register the device they would use into their Nebula accounts.

Users need to download Nebula agent program nebulacli.tar by themselves and run it on the device to complete the registration. This registration step could be manual, or it can be automated in batch operations for OEM.

./install.sh init -u user-acccount -p user-account-password -n user-device-name

User Portal

After completing the device registration, users can install and manage EdgeX Foundry or other edge applications released in advance on Nebula service by vendors. Users can find proper applications in the catalog.

After selection, users can further specify parameter settings of the deployment in the drag-and-drop wizard, which maps to parameter values defined by the vendor before.

After all parameters are set, the actual deployment can be carried out, either in batch or multiple times to multiple devices. After deploying EdgeX Foundry applications, users can monitor device resources and application run time status in real time.

Nebula provides complete Restful API documentation, with which users can automate operations to deploy EdgeX Foundry applications in a large scale.

Next

From the second article to this article, I introduced the basic method of building and managing virtualized devices and containerized devices from the cloud. But I did not answer the question of how to deal with single-point device failure. Compared with the traditionally inflexible and inefficient full redundancy or external NAS solution, the next article will introduce device clusters on hyper-convergence architecture.

Pushing AI to the Edge (Part One): Key Considerations for AI at the Edge

By Blog, LF Edge, Project EVE, State of the Edge, Trend

Q&A with Jason Shepherd, LF Edge Governing Board member and VP of Ecosystem at ZEDEDA

This content originally ran on the ZEDEDA Medium Blog – visit their website for more content like this.

This two-part blog provides more insights into what’s becoming a hot topic in the AI market — the edge. To discuss more on this budding space, we sat down with our Vice President of ecosystem development, Jason Shepherd, to get his thoughts on the potential for AI at the edge, key considerations for broad adoption, examples of edge AI in practice and some trends for the future.


Chart defining the categories within the edge, as defined by LF Edge

Image courtesy of LF Edge

LF Edge Member Spotlight: HPE

By Akraino, Akraino Edge Stack, Blog, Member Spotlight

The LF Edge community is represents a diverse set of member companies and people that represent the IoT, Enterprise, Cloud and Telco Edge. The Member Spotlight blog series highlights these members and how they are contributing to and leveraging open source edge solutions. Today, we sat down with Rohit Arora, Enterprise Architect at Hewlett Packard Enterprise (HPE) to discuss the importance of open source, leading Multi Access Edge Computing (MEC) initiatives, participating in the Technical Advisory Committee (TAC) and collaborating with the LF Edge ecosystem.

Can you tell us a little about your organization?

HPE is a global, edge-to-cloud Platform-as-a-Service company. HPE solutions connect, protect, analyze, and act on data and applications wherever they live, from edge to cloud, so insights can be turned into outcomes at the speed required to thrive in today’s complex world.

Why is your organization adopting an open source approach?

We at HPE believe in innovation and open source encourages innovation by bringing communities together to build common platform. HPE has been involved in various open source projects.

Why did you join LF Edge and what sort of impact do you think LF Edge has on the edge, networking, and IoT industries?

We joined LF edge because it aligns with HPE’s direction of edge to cloud. Edge computing is creating a major transformation in most industries and we believe initiatives driven by LF edge are critical for this digital transformation

What do you see as the top benefits of being part of the LF Edge community?

There are many benefits of being part of LF edge but we believe the biggest is to be part of a community which is driving the innovation for the next gen networks at the edge.

What sort of contributions has your team made to the community, ecosystem through LF Edge participation?

HPE has contributions on the LF Edge Governing Board and TAC, HPE has also made some contributions to the infrastructure requirements for LF Edge. HPE is also actively involved in LF edge projects such as Akraino and process adoption.

What do you think sets LF Edge apart from other industry alliances?

There are two main reasons LF Edge is different from other industry alliances

  1. A wide set of different community members: There is a wide variety of community members in LF edge from telco services providers, NEPs to chip manufacturers. This provides different viewpoints and provides the right level of expertise that is needed.
  2. Projects execution: The community really believes in executing and we have seen some projects coming from idea to development and then testing at a very fast pace.

How will  LF Edge help your business?

HPE is leading infrastructure provider and have wide variety of solutions for the edge. We are also leading MEC (Multi Access Edge Computing) initiatives with some major telcos. By being part of LFEdge we get access to latest innovations and resources in edge computing. This can help us build our solution to fit industry needs.

What advice would you give to someone considering joining LF Edge?

There are so many projects LF Edge is driving, the best place to start would be to pick a project which aligns with your company’s directions and see how you can drive innovation with your contributions for the project. There are many resources available and all the community members are very helpful to provide any info you need.

To find out more about LF Edge members or how to join, click here.

Additionally, if you have questions or comments, visit the  LF Edge Slack to share your thoughts and engage with community members. 

 

Finalists for the 2020 Edge Woman of the Year Award!

By Blog, State of the Edge

Written by Candice Digby, Partner and Events Manager at Vapor IO, a LF Edge member and active community member in the State of the Edge Project

Last year’s Edge Woman of the Year winner Farah Papaioannou is ready to pass the torch.

“I was honored to have been chosen as Edge Woman of the Year 2019 and to be recognized alongside many inspiring and innovative women across the industry,” said Farah Papaioannou, Co-Founder and President of Edgeworx, Inc. “I am thrilled to pay that recognition forward and participate in announcing this year’s Edge Woman of the Year 2020 finalist categories; together we have a lot to accomplish.”

(left to right) Matt Trifiro, Farah Papaioannou, Gavin Whitechurch

With more nominations in the 2nd annual competition, it was difficult for State of the Edge and Edge Computing World to select only ten top finalists. The Edge Woman of the Year 2020 nominees represent industry leaders in roles that are impacting the direction of their organization’s strategy, technology or communications around edge computing, edge software, edge infrastructure or edge systems.

The Edge Woman of the Year Award represents a long-term industry commitment to highlight the growing importance of the contributions and accomplishments made by women in edge computing.  The award is presented at the annual Edge Computing World event which gathers the whole edge computing ecosystem, from network to cloud and application to infrastructure end-users and developers while also sharing edge best practices.

The annual Edge Woman of the Year Award is presented to outstanding female and non-binary professionals in edge computing for outstanding performance in their roles elevating Edge. The 2020 award committee selected the following 10 finalists for their excellent work in the named categories:

  • Leadership in Edge Startups
    • Kathy Do, VP, Finance and Operations at MemVerge
  • Leadership in Edge Open Source Contributions
    • Malini Bhandaru, Open Source Lead for IoT & Edge at VMware
  • Leadership in Edge at a Large Organization
    • Jenn Didoni, Head of Cloud Portfolio at Vodafone Group Business
  • Leadership in Edge Security
    • Ramya Ravichandar, VP of Product Management at FogHorn
  • Leadership in Edge Innovation and Research
    • Kathleen Kallot, Director, AI Ecosystem, arm
  • Leadership in Edge Industry and Technology
    • Fay Arjomandi, Founder and CEO, mimik technology, Inc.
  • Leadership in Edge Best Practices
    • Nurit Sprecher, Head of Management & Virtualization Standards, Nokia
  • Leadership in Edge Infrastructure
    • Meredith Schuler, Financial & Strategic Operations Manager, SBA Edge
  • Overall Edge Industry Leadership
    • Nancy Shemwell, Chief Operating Officer, Trilogy Networks, Inc.
  • Leadership in Executing Edge Strategy
    • Angie McMillin, Vice President and General Manager, IT Systems, Vertiv

The “Top Ten Women in Edge” finalists are selected from nominations and submissions submitted by experts in edge from around the world. The final winner will be chosen by a panel of industry judges. The winner of the Edge Woman of the Year 2020 will be announced during this year’s Edge Computing World, being held virtually October 12-15, 2020.

For more information on the Women in Edge Award visit: https://www.lfedge.org/2020/08/25/state-of-the-edge-and-edge-computing-world-announce-finalists-for-the-2020-edge-woman-of-the-year-award/

 

Akraino’s AI Edge-School/Education Video Security Monitoring Blueprint

By Akraino, Akraino Edge Stack, Blog, Use Cases

Written by Hechun Zhang, Staff Systems Engineer, Baidu; Akraino TSC member, and PTL of the AI Edge Blueprint; and Tina Tsou, Enterprise Architect, Arm and Akraino TSC Co-Chair

In order to support end-to-end edge solutions from the Akraino community, Akraino uses blueprint concepts to address specific Edge use cases. A Blueprint is a declarative configuration of the entire stack i.e., edge platform that can support edge workloads and edge APIs. In order to address specific use cases, a reference architecture is developed by the community.

The School/Education Video Security Monitoring Blueprint belongs to the AI Edge Blueprint family. It focuses on establishing an open source MEC platform that combined with AI capacities at the Edge. In this blueprint, latest technologies and frameworks like micro-service framework, Kata container, 5G accelerating, and open API have been integrated to build a industry-leading edge cloud architecture that could provide comprehensive computing acceleration support at the edge. And with this MEC platform, Baidu has expanded AI implementation across products and services to improve safety and engagement in places such as factories, industrial parks, catering services, and classrooms that rely on AI-assisted surveillance.

Value Proposition

  • Establish an open-source edge infrastructure on which each member company can develop its own AI applications, e.g. video security monitoring.
  • Contribute use cases which help customers adopt video security monitoring, AI city, 5G V2X, and Industrial Internet applications.
  • Collaborate with members who can work together to figure out the next big thing for the industry.

Use cases

Improved Student-Teacher Engagement

 

Using deep learning model training for video data from classrooms, school management can evaluate class engagement and analyze individual student concentration levels to improve real-time teaching situations.

Enhanced Factory Safety and Protection

Real-time monitoring helps detecting factory workers who might forget security gadgets, such as helmets, safety gloves, and so on, to prevent hazardous accidents in the workplace. Companies can monitor safety in a comprehensive and timely way, and used findings as a reference for strengthening safety management.

Reinforced Hygiene and Safety in Catering

Through monitoring staff behavior in the kitchen, such as smoking breaks and cell phone use, this solution ensures the safety and hygiene of the food production process.

Advanced Fire Detection and Prevention

Linked and networked smoke detectors in densely populated places, such as industrial parks and community properties, can help quickly detect and alert authorities to fire hazards and accidents.

Network Architecture

OTE-Stack is an edge computing platform for 5G and AI. By virtualization it can shield heterogeneous characteristics and gives a unified access of cloud edge, mobile edge and private edge. For AI it provides low-latency, high-reliability and cost-optimal computing support at the edge through the cluster management and intelligent scheduling of multi-tier clusters. And at the same time OTE-Stack makes device-edge-cloud collaborative computing possible.

Baidu implemented video security monitoring blueprints on the Arm infrastructure, including cloud-edge servers, hardware accelerators, and custom CPUs designed for world-class performance. Arm and Baidu are members of the Akraino project and use edge cloud reference stack of networking platforms and cloud-edge servers built on Arm Neoverse. The Arm Neoverse architecture supports a vast ecosystem of cloud-native applications and combines AI Edge blueprint for an open source mobile edge computing (MEC) platform optimized for sectors such as safety, security, and surveillance.

“Open source has now become one of the most important culture and strategies respected by global IT and Internet industries. As one of the world’s top Internet companies, Baidu has always maintained an enthusiastic attitude in open source, actively contributing the cutting edge products and technologies to the Linux foundation. Looking towards the future, Baidu will continue to adhere to the core strategy of open source and cooperate with partners to build a more open and improved ecosystem.” — Ning Liu, Director of AI Cloud Group, Baidu

In the 5G era, OTE-Stack has obvious advantages in the field of edge computing:

  • Large scale and hierarchical cluster management
  • Support third cluster
  • Lightweight cluster controller
  • Cluster autonomy
  • Automatic disaster recovery
  • Global scheduling
  • Support multi-runtimes
  • Kubernetes native support

For more information about this Akraino Blueprint, click here.  For general information about Akraino Blueprints, click here.

Breaking Down the Edge Continuum

By Blog, State of the Edge, Trend, Use Cases

Written by Kurt Rinehart, Director of Data Science at Section. This blog originally ran on the Section website. For more content like this, please click here.

There are many definitions of “the edge” out there. Sometimes it can seem as if everyone has their own version.

LF Edge, an umbrella organization that brings together industry leaders to build “an open source framework for the edge,” has a number of edge projects under its remit, each of which seeks to unify the industry around coalescing principles and thereby accelerate open source edge computing developments. Part of its remit is to define what the edge is, an invaluable resource for the edge community to coalesce around.

Latest LF Edge White Paper: Sharpening the Edge

In 2018, State of the Edge (which recently became an official project of LF Edge) put out its inaugural report, defining the edge using four criteria:

  • “The edge is a location not a thing;
  • There are lots of edges, but the edge we care about today is the edge of the last mile network;
  • This edge has two sides: an infrastructure edge and a device edge;
  • Compute will exist on both sides, working in coordination with the centralized cloud.”

Since that inaugural report, much has evolved within the edge ecosystem. The latest white paper from LF Edge, Sharpening the Edge: Overview of the LF Edge Taxonomy and Framework, expands on these definitions and moves on from simply defining two sides (the infrastructure and the device edge) to use the concept of an edge continuum.

The Edge Continuum

The concept of the edge continuum describes the distribution of resources and software stacks between centralized data centers and deployed nodes in the field as “a path, on both the service provider and user sides of the last mile network.”

In almost the same breath, LF Edge also describes edge computing as essentially “distributed cloud computing, comprising multiple application components interconnected by a network.”

We typically think of “the edge” or “the edges” in terms of the physical devices or infrastructure where application elements run. However, the idea of a path between the centralized cloud (also referred to as “the cloud edge” or “Internet edge”) and the device edge instead allows for the conceptualization of multiple steps along the way.

The latest white paper concentrates on two main edge categories within the edge continuum: the Service Provider Edge and the User Edge (each of which is broken down into further subcategories).

edge continuum diagram
Image source: LF Edge

The Service Provider Edge and the User Edge

LF Edge positions devices at one extreme of the edge continuum and the cloud at the other.

Next along the line of the continuum after the cloud, also described as “the first main edge tier”, is the Service Provider (SP) Edge. Similarly to the public cloud, the infrastructure that runs at the SP Edge (compute, storage and networking) is usually consumed as a service. In addition to the public cloud, there are also cellular-based solutions at the SP Edge, which are typically more secure and private than the public cloud, as a result of the differences between the Internet and cellular systems. The SP Edge leverages substantial investments by Communications Service Providers (CSPs) into the network edge, including hundreds of thousands of servers at Points of Presence (PoPs). Infrastructure at this edge tier is largely more standardized than compute at the User Edge.

The second top-level edge tier is the User Edge, which is on the other side of the last mile network. It represents a wider mix of resources in comparison to the SP Edge, and “as a general rule, the closer the edge compute resources get to the physical world, the more constrained and specialized they become.” In comparison to the SP Edge and the cloud where resources are owned by these entities and shared across multiple users, resources at the User Edge tend to be customer-owned and operated.

Moving from the Cloud to the Edge

What do we mean when we talk about moving from the cloud to the edge? Each of the stages along the edge continuum take you progressively closer to the end user. You have high latency and more compute in the centralized cloud versus low latency and less compute as you get closer to the User Edge. When we talk about moving from the cloud to the edge, it means we want to leverage the whole stack and not solely focus on the centralized cloud.

Let’s look at the most obvious use case: content delivery networks (CDNs). In the 1990s, Akamai created content delivery networks to allow localized websites to serve a global audience. A website based in New York could leverage Akamai’s distributed network of proxy servers and data centers around the world to be able to store their static assets globally, including HTML, CSS, JavaScript, video, and images. By caching these in Akamai’s distributed global points of presence (PoP), the website’s end users worldwide were guaranteed high availability and consistent performance.

These days, CDNs are considered to be only one layer in a highly complex Internet ecosystem. Content owners such as media companies and e-commerce vendors continue to pay CDN operators to deliver their content to end users. In turn, a CDN pays ISPs, carriers, and network operators for hosting its servers in their data centers. That’s the Service Provider Edge we’re talking about.

An edge compute platform is still a geographically distributed network, but instead of simply providing proxy servers and data centers, an edge compute platform also offers compute. How do we define this? Compute can be defined as many things, but essentially, it boils down to the ability to run workloads wherever you need to run them. Compute still gives you high availability and performance, but it also allows for the capability to run packaged and custom workloads positioned relatively spatially to users.

An edge compute platform leverages all available compute between the cloud provider and the end user, together with DevOps practices, to deliver traditional CDN and custom workloads.

Applying Lessons from the Cloud to the Edge

We can take the lessons we’ve learned in the cloud and apply them to the edge. These include:

  • Flexibility – At Section, we describe this as wanting to be able to run “any workload, anywhere”, including packaged and customized workloads;
  • Taking a multi-provider approach to deployments – This offers the opportunity to create a higher layer of abstraction. Infrastructure as Code (IaC) is the process of managing and provisioning computer data centers through machine-readable definition files as opposed to physical hardware configuration or interactive configuration tools. At Section, we have 6-7 different providers, from cloud providers to boutique providers to bare metal providers.
  • Applying DevOps practices – In order to provide the capabilities that the cloud has at the infrastructure edge, we need to enable developers to get insight and to run things at the edge at speed, just as they did in the cloud. This is DevOps. It’s important to be able to apply DevOps practices here since, “if you build it, you own it”. You want to make things open, customizable, and API-driven with integrations, so that developers can leverage and build on top of them.
  • Leveraging containerized workloads – Deploying containers at the edge involves multiple challenges, particularly around connectivity, distribution and synchronization, but it can be done, and in doing, allows you to leverage this architecture to deploy your own logic, not just pre-packaged ones. Containerization also offers:
    • Security
    • Standardization
    • Isolation; and
    • A lightweight footprint.
  • Insights and Visibility – We need to give developers deep, robust insight into what’s happening at the edge, just as we do in the cloud. The three pillars of observability are logs, metrics and tracing. An ELK stack can provide this, giving developers the invaluable ability to understand what is happening when things inevitably go wrong.

Edge Computing Use Cases in the Wild

There are many examples of use cases already operating at the Edge. A few of the many interesting ones out there include:

  • Facebook Live – When you see a live stream in your feed and click on it, you are requesting the manifest. If the manifest isn’t already on your local PoP, the request travels to the data center to get the manifest, and then fetches the media files in 1 second clips. ML algorithms operate on the 1 second clips to optimize them in real time to deliver the best, fastest experience for users.
  • Cloudflare Workers – These are Service Worker API implementations for the Cloudflare platform. They deploy a server-side approach to running JavaSCript workloads on Cloudflare’s global network.
  • Chick-fil-A – A surprising one. Chick-fil-A has been pushing into the device edge over the last couple of years. Each of their 20,000 stores has a Kubernetes cluster that runs there. The goal: “low latency, Internet-independent applications that can reliably run our business”, in addition to high availability for these applications, a platform that enables rapid innovation, and the ability to horizontally scale.

We’re Not Throwing Away the Cloud

One last thing to make clear: we’re not talking about throwing away the cloud. The cloud is going nowhere. We will be working alongside it, using it. What we’re talking about is moving the boundary of our applications out of the cloud closer to the end user, into the compute that is available there. And, as we’ve seen, we don’t need to throw away the lessons we’ve learned in the cloud; we can still use the tools that we’re used to, plus gain all the advantages that the edge continuum has to offer.

You can download the LF Edge taxonomy white paper here. You can also watch the LF Edge Taxonomy Webinar, which shares insight from the white paper, on our Youtube Channel. Click here to watch it now.  

MicroMEC now available with the Akraino R3 Release!

By Akraino, Akraino Edge Stack, Blog

Written by Tapio Tallgren, Technical Leader at Nokia Mobile Networks, Community Sub-Committee Chair of Akraino TSC,Ferenc Szekely, Program Manager, SUSE, Committer of Micro MEC blueprint of Akraino TSC and Tina Tsou, Enterprise Architect, Arm, Akraino TSC Co-Chair

The MicroMEC platform started life as a platform to run applications at the very edge of the network, like in a light pole. We joined the LF Edge’s Akraino project from the very beginning.

To find out what the use cases would be first, we participated in the IoThon hackathon in 2019 where we built a miniature city with sensors, cameras and small servers — also known as Raspberry Pis. Our plan was that we will provide APIs to enable developers to access the sensors, cameras, or other independent hardware devices attached to our small servers, ie. the MicroMEC nodes. It was clear that we wanted to deploy all the APIs as well as the apps in containers. We needed a tool like Kubernetes to help us build and manage the MicroMEC cluster. As we targeted “small” devices, with max 4GB of RAM -at that time- and low power consumption we looked into alternatives to k8s. That is how we picked k3s. 

By the autumn of 2019 we had our lab running Raspberry Pi 3B+ and 4B nodes with k3s. We had a successful hackathon – Junction 2019 – in Finland where the teams presented solutions utilizing the MicroMEC cluster. We also added OpenFaaS Cloud (OFC) into the mix and a developer UI to the platform. This allowed developers to write serverless applications for the MicroMEC cluster and deploy them with ease. They could concentrate on their core business: developing apps while MicroMEC with OFC took away the burden of cluster management, deployment etc.

Right after Junction, we were at the Akraino 5G MEC Hackathon in the USA. For this event MicroMEC had to become more “MEC”. This implied the implementation of MEC-11 interfaces and the UI to manage those apps that our MEC-11 implementation made discoverable for customers near the MicroMEC cluster. The MEC cluster runs on Arm architecture based hardware.

With all this activity, we missed the first two Akraino releases, but now we are very happy to join the Akraino R3 release! For this, we had to figure out what is the easiest way to install the stack on the device with a MMC card. The easiest way is to not install anything on the fragile card, but boot the stack from a network server. Eventually we made all MicroMEC nodes to boot from a network server using PXE and the storage of each node was attached via iscsi. This requires a fast enough LAN, but thankfully cheap gigabit switches are widely available these days. 

Learn more about Akraino here.

 

The Over the Edge Podcast

By Blog, State of the Edge

If you ask 100 people to define edge, you might get 112 different answers, but we do know this much: Edge computing represents a long-term transformation of the Internet that could take decades to fully materialize.

Over The Edge is a podcast about edge computing and those in the industry who are creating the future of the internet. On the show we talk to corporate leaders, open-source experts, technologists, journalists, analysts, and the community at large, to discuss technological innovations, trends, practical applications, business models, and the occasional far-flung theory. Over the Edge is brought to you by the sponsorship of Catchpoint, NetFoundry, Ori Industries, Packet, Seagate, Vapor IO, and Zenlayer.

Listen to the podcast here: OverTheEdgePodcast.com

Check out some of the LF Edge member interviews:

July 29 – Matt Trifiro, VaporIO

July 29 – Galeal Zino, Netfoundry

July 29 – Jacob Smith, Packet

August 5 – Joe Zhu, Zenlayer

August 19 – Malini Bhandaru, VMware

August 26 – Jason Shepherd, ZEDEDA