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Akraino Edge Stack

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 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. 

 

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.

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.

 

OpenAirInterface End User Case Study – Running 5G on Akraino’s KNI Provider Access Edge Blueprint

By Akraino Edge Stack, Blog, Use Cases

Written by Ricardo Noriega, Project Team Lead, Akraino Kubernetes Native Infrastructure Blueprint Family, and Raymond Knopp,  Executive Committee member of the OpenAirInterface Alliance

Overview

Blueprints in the Akraino Kubernetes-Native Infrastructure (KNI) Blueprint Family leverage the best-practices and tools from the Kubernetes community to declaratively manage edge computing stacks at scale and with a consistent, uniform user experience from the infrastructure up to the services and from developer environments to production environments on bare metal or on public cloud.

One of the many use cases that the KNI blueprint family covers is the Provider Access Edge (PAE). The need for deploying mobile application on the edge is growing in latest times. Providing a platform that is capable of supporting deployment of mobile applications, using Kubernetes, and based on kubernetes tooling and declarative configuration from end to end is needed.

The OpenAirInterface project fosters a community of industrial as well as research contributors for software and hardware development for the core network (EPC) and access network and user equipment (EUTRAN) of 3GPP cellular networks. The OpenAirInterface alliance, has chosen the Akraino KNI PAE blueprint as the reference platform to develop, test and deploy its 4G and 5G open source mobile networks.

Key features on the Provider Access Edge blueprint

Telco / 5G network functions are among the more exigent Kubernetes workloads, but they are not unique: customers from high performance computing, high frequency trading, industrial control, et al. are asking for pretty much similar sets of capabilities.

This blueprint targets small footprint deployments able to host NFV (in particular vRAN) and MEC (e.g. AR/VR, machine learning, etc.) workloads. Its key features are:

  • Lightweight, self-managing clusters based on CoreOS and Kubernetes (OKD distro).
  • Support for VMs (via KubeVirt) and containers on a common infrastructure.
  • Application lifecycle management using the Operator Framework.
  • Support for multiple networks using Multus.
  • Support for high throughput interfaces using the SRIOV operator.
  • Support for real-time workloads.
  • Support for Stream Control Transmission Protocol (SCTP).

OpenAirInterface network deployment

The OpenAirInterface alliance has made a great effort on moving all the components that form a 4G/5G mobile network to the Kubernetes world. Building all the container images and writing the corresponding manifests to match a specific deployment model has been a tremendous work.

To support the 5G network in a production-like deployment, we configured the OpenShift based KNI PAE blueprint to segregate real-time and non-real-time compute workloads as well as management, control, and data plane traffic according to the following logical deployment architecture:

Conclusion

5G is designed to bring to the enterprise world as well as to the regular consumer, high throughput and low latency bandwidth that will enable the use cases of the future like IoT, autonomous cars, and many other applications deployed at the edge of the networks. The Akraino Kubernetes Native Infrastructure blueprint family allows to run these very demanding workloads on top, and OpenAirInterface has chosen us as the reference platform.

References

https://www.openairinterface.org/docs/workshop/8_Fall2019Workshop-Beijing/Talks/2019-12-05-DEFOSSEUX.pdf

Learn more about OpenAirInterface here. Learn more about Akraino here.

Akraino’s Debut at China Mobile’s “Innovation 2020 Cloud Technology Week”

By Akraino Edge Stack, Blog

Written by Tina Tsou, Co-Chair of the Akraino TSC, and Su Gu, Senior Researcher with China Mobile

On June 17, China Mobile Science and Technology Association held the “Innovation 2020 Cloud Technology Week” forum, which officially opened a series of cloud related activities  that included product launches, lectures, networking opportunities and more. For example, there was an announcement of “NEST+” plan geared for external industrial ecological cooperation. The event theme was innovation and celebrated the collaboration, openness, and win-win of technology companies both big and small. 

The event was attended and viewed by more than 734,000 people ranging from academicians, scholars, thought leaders and industry experts. There was a lot of buzz around the 5G development and collaboration in the  innovation, 6G frontier, artificial intelligence, Internet of Vehicles, Internet of Things, and other fields. 

As an important part of this ecosystem, LF Edge was represented at this event through project leaders and members. LF Edge’s Tina Tsou, Co-Chair of the Akraino TSC and Enterprise Architect at Arm, was on-hand to host a workshop with China Mobile Technology (USA) about the trend of edge computing and LF Edge’s Akraino Edge Stack project.

Other member speakers include Dr Oleg Berzin, Senior Director at Equinix, Jason Shepherd, LF Edge Governing Board member, Project EVE leader and Vice President at Zededa and Tapio Tallgren, a member of the LF Edge Technical Advisory Council and Lead SW Architect at Nokia, to introduce their contributes to Akraino and share their views on MEC.

Joe Ward, CEO at EdgeVideo, Arif Khan,CEO at ParserLabs and China Mobile’s Su Gu, Senior Researcher, and Dr Jian Li also shared their views on Telco MEC and how to interwork with public clouds on MEC.

The “Innovation 2020 Cloud Technology Week” was a great success.  To learn more about Akraino Edge Stack, click here

 

Private LTE/5G ICN Akraino Blueprint

By Akraino Edge Stack, Blog

Written by Amar Kapadia, member of the Akraino Edge Stack community, co-founder at Aarna Networks, Inc. and an ONAP specialist. This blog originally ran on the Aarna Networks blog. You can find more content like this here

As one of the main contributors, I’m thrilled to state that the Private LTE/5G Integrated Cloud Native NFV/App Stack blueprint in LF Edge’s Akraino Edge Stack project got approved last month.

Given the opening up of unlicensed/licensed private spectrum all around the world (e.g. CBRS in the US), Private LTE/5G promises to be very exciting market. Six end users (Airbus, Globe, Orange, Tata Communications, T-Mobile, and Verizon), a number of vendors (such as us), and individuals are collaborating on this blueprint demo which will be created in a completely open manner and will contain, to the degree possible, open source components.

The key components of this blueprint are:

Private LTE/5G ICN Blueprint Software Stack

  • NFVI hardware: Standard server, switch, storage components

  • NFVI software: Kubernetes with OVN (SDN), Virtlet (to run VMs), Multus (for multiple CNI), Istio, and SD-EWAN (to connect an app across clouds). A main component in the NFVI software will also be an open source 5G UPF CNF.

  • Orchestrator: ONAP with AF integration, OpenNESS

  • Workloads (CNFs): Facebook Magma for vEPC, TIP OCN and Polaris for 5GC

  • Workloads (CNAs): We are starting with the applications in the original ICN blueprint, viz. 360° video, EdgeX Foundry, video AI/ML. However, we might change things around to collaborate with other Akraino blueprints such as the 5G MEC blueprint that is working on cloud gaming, HD video, and live broadcasting.

We will first start with Private LTE over CBRS but then quickly move over to Private 5G and edge computing.

As an open source effort, we could always use more help, Please join us if this is interesting!

LF Edge Member Spotlight: Arm

By Akraino Edge Stack, Blog, Member Spotlight

The LF Edge community comprises 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 Tina Tsou, Enterprise Architect at Arm and Co-Chair of the Technical Steering Committee for the Akraino Edge Stack, to discuss the importance of open source software, collaborating with industry leaders in edge computing, security, how they contribute to the Akraino Edge Stack project and the impact of being a part of the LF Edge ecosystem.

Can you tell us a little about your organization?

I work for Arm, a semiconductor IP company, as Enterprise Architect in the Infrastructure line of business. 

What are your interests in open source projects?

The open source approach allows me to get requirements from customers, understand the solutions from partners, so I can get prepared well for internal design and products, having open source in mind.

What are your thoughts on LF Edge and what sort of impact do you think LF Edge has on the edge, networking, and IoT industries?

I joined the Akraino Edge Stack per a customer’s ask and then the project was merged under the LF Edge umbrella. LF Edge is the de facto standard for the mainstream product and deployment of edge computing including edge servers, network edge, and device gateway.

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

The top benefits include having direct connection with customers that keep me updated on the latest trends of edge computing and collaboration among end to end ecosystems and supply chain as well as validation in CI/CD environments.

What are your top contributions as a community member?

I serve as LF Edge TAC member, Akraino TSC co-chair, contributed Integrated Edge Cloud (IEC) platform (Arm enabler) used by multiple blueprints like the 5G MEC System, the AI Edge, etc.  Sample blueprints that I’ve contributed to include:

  1. The purpose of Public Cloud Edge Interface (PCEI) Blueprint family is to specify a set of open APIs for enabling interworking between multiple functional entities or Domains that provide services needed for implementation of Edge capabilities/applications that require close integration between the Mobile Edge, the Public Cloud Core and Edge as well as the 3rd-Party provided Edge functions. In Release 3 we choose to expose the set of location APIs as defined by the MTSI MEC to showcase the PCEI capability.
  2. KubeEdge Edge Service Blueprint showcases end-to-end solution for edge services with KubeEdge centered edge stack. The first release will focus on the ML inference offloading use case. It supports multi-arch.
  3. The Radio Edge Cloud Blueprint is a member of the Telco Appliance blueprint family which is designed to provide a fully integration tested appliance tuned to meet the requirements of the RAN Intelligent Controller (RIC). Changes since Release 2 (2019-11-18), include New Features like Arm64 Support! – REC Release 3 supports Arm based CPUs for the first time. Support includes Ampere Hawk and Falcon servers.
  4. IEC (Integrated Edge Cloud) is a platform that enables new functionalities and business models on the network edge.
  5. The IEC type 2 mainly focuses on the high-performance computing system of medium and/or large deployment at the data center.
  6. IEC Type 3 mainly focuses on Android Application running on edge Arm Cloud architecture with GPU/ vGPU Management.

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

LF Edge has a pragmatic approach for Practice, Project, Production in a closed loop, which is at production quality and deployable.

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

Start by questioning how you expect LF Edge will be used in your business and then read the end user stories. You will find the right project and perspective that you care about and then take baby steps, for example, try an Akraino blueprint you are interested in, and report any issue you find. The community always has your back. Have fun!

Members of the Akraino TSC at the March 2020 F2F

To learn more about Akraino Edge Stack, click here. To find out more about our members or how to join LF Edge, click here. Additionally, if you have questions or comments, visit the  LF Edge Slack Channel and share your thoughts in the #community or #akraino-tsc channels.

LF Edge Expands Ecosystem with Open Horizon, adds Seven New Members and Reaches Critical Deployment Milestones

By Akraino Edge Stack, Announcement, Baetyl, EdgeX Foundry, Fledge, Home Edge, LF Edge, Open Horizon, Project EVE, State of the Edge

  • Open Horizon, an application and metadata delivery platform, is now part of LF Edge as a Stage 1 (At-Large) Project.
  • New members bring R&D expertise in Telco, Enterprise and Cloud Edge Infrastructure.
  • EdgeX Foundry hits 4.3 million downloads and Akraino R2 delivers 14 validated deployment-ready blueprints.
  • Fledge shares a race car use case optimizing car and driver operations using Google Cloud, Machine Learning and state-of-the-art digital twins and simulators.

SAN FRANCISCO – April 30, 2020 –  LF Edge, an umbrella organization under The Linux Foundation that aims to establish an open, interoperable framework for edge computing independent of hardware, silicon, cloud, or operating system, today announced continued project momentum with the addition a new project and several technical milestones for EdgeX Foundry, Akraino Edge Stack and Fledge. Additionally, the project welcomes seven new members including CloudBrink, Federated Wireless, Industrial Technology Research Institute (ITRI), Kaloom, Ori Industries, Tensor Networks and VoerEir to its ecosystem.

Open Horizon, an existing project contributed by IBM, is a platform for managing the service software lifecycle of containerized workloads and related machine learning assets. It enables autonomous management of applications deployed to distributed webscale fleets of edge computing nodes and devices without requiring on-premise administrators.

Edge computing brings computation and data storage closer to where data is created by people, places, and things. Open Horizon simplifies the job of getting the right applications and machine learning onto the right compute devices, and keeps those applications running and updated. It also enables the autonomous management of more than 10,000 edge devices simultaneously – that’s 20 times as many endpoints as in traditional solutions.

“We are thrilled to welcome Open Horizon and new members to the LF Edge ecosystem,” said Arpit Joshipura, general manager, Networking, Edge & IoT, the Linux Foundation. “These additions complement our deployment ready LF Edge open source projects and our growing global ecosystem.”

“LF Edge is bringing together some of the most significant open source efforts in the industry, said Todd Moore, IBM VP Open Technology, “We are excited to contribute the Open Horizon project as this will expand the work with the other projects and companies to create shared approaches, open standards, and common interfaces and APIs.”

Open Horizon joins LF Edge’s other projects including: Akraino Edge Stack, Baetyl,  EdgeX Foundry, Fledge, Home Edge, Project EVE and State of the Edge. These projects support emerging edge applications across areas such as non-traditional video and connected things that require lower latency, and  faster processing and mobility. By forming a software stack that brings the best of cloud, enterprise and telecom, LF Edge helps to unify a fragmented edge market around a common, open vision for the future of the industry.

Since its launch last year, LF Edge projects have met significant milestones including:

  • EdgeX Foundry has hit 4.3 million docker downloads.
  • Akraino Edge Stack (Release 2) has 14 specific Blueprints that have all tested and validated on hardware labs and can be deployed immediately in various industries including Connected Vehicle, AR/VR, Integrated Cloud Native NFV, Network Cloud and Tungsten Fabric and SDN-Enabled Broadband Access.
  • Fledge shares a race car use case optimizing car and driver operations using Google Cloud, Machine Learning and state-of-the-art digital twins and simulators.
  • State of the Edge merged under LF Edge earlier this month and will continue to pave the path as the industry’s first open research program on edge computing. Under the umbrella, State of the Edge will continue its assets including State of the Edge Reports, Open Glossary of Edge Computing and the Edge Computing Landscape.

Support from the Expanding LF Edge Ecosystem

Federated Wireless:

“LF Edge has become a critical point of collaboration for network and enterprise edge innovators in this new cloud-driven IT landscape,” said Kurt Schaubach, CTO, Federated Wireless. “We joined the LF Edge to apply our connectivity and spectrum expertise to helping define the State of the Edge, and are energized by the opportunity to contribute to the establishment of next generation edge compute for the myriad of low latency applications that will soon be part of private 5G networks.”

Industrial Technology Research Institute (ITRI):

“ITRI is one of the world’s leading technology R&D institutions aiming to innovate a better future for society. Founded in 1973, ITRI has played a vital role in transforming Taiwan’s industries from labor-intensive into innovation-driven. We focus on the fields of Smart Living, Quality Health, and Sustainable Environment. Over the years, we also added a focus on 5G, AI, and Edge Computing related research and development. We joined LF Edge to leverage its leadership in these areas and to collaborate with the more than 75 member companies on projects like Akraino Edge Stack.”

Kaloom:

“Kaloom is pleased to join LF Edge to collaborate with the community on developing open, cloud-native networking, management and orchestration for edge deployments” said Suresh Krishnan, chief technology officer, Kaloom.  “We are working on an unified edge solution in order to optimize the use of resources while meeting the exacting performance, space and energy efficiency needs that are posed by edge deployments. We look forward to contributing our expertise in this space and to collaborating with the other members in LF Edge in accelerating the adoption of open source software, hardware and standards that speed up innovation and reduce TCO.”

Ori Industries:

“At Ori, we are fundamentally changing how software interacts with the distributed hardware on mobile operator networks.” said Mahdi Yahya, Founder and CEO, Ori Industries. “We also know that developers can’t provision, deploy and run applications seamlessly on telco infrastructure. We’re looking forward to working closely with the LF Edge community and the wider open-source ecosystem this year, as we turn our attention to developers and opening up access to the distributed, telco edge.”

Tensor Networks:

“Tensor Networks believes in and supports open source. Having an arena free from the risks of IP Infringement to collaborate and develop value which can be accessible to more people and organizations is essential to our efforts. Tensor runs its organization, and develops products on top of Linux.  The visions of LF Edge, where networks and latency are part of open software based service composition and delivery, align with our vision of open, fast, smart, secure, connected, and customer driven opportunities across all industry boundaries.” – Bill Walker, Chief Technology Officer.

VoerEir:

“In our extensive work with industry leaders for NFVI/VIM test and benchmarking,  a need to standardize infrastructure KPIs in Edge computing has gradually become more important,” said Arif  Khan, Co-Founder of VoerEir AB. “This need has made it essential for us to join LF Edge and to initiate the new Feature Project “Kontour” under the Akraino umbrella. We are excited to collaborate with various industry leaders to define, standardize  and measure Edge KPIs.”

About The Linux Foundation

Founded in 2000, the Linux Foundation is supported by more than 1,000 members and is the world’s leading home for collaboration on open source software, open standards, open data, and open hardware. Linux Foundation’s projects are critical to the world’s infrastructure including Linux, Kubernetes, Node.js, and more.  The Linux Foundation’s methodology focuses on leveraging best practices and addressing the needs of contributors, users and solution providers to create sustainable models for open collaboration. For more information, please visit us at linuxfoundation.org.

The Linux Foundation has registered trademarks and uses trademarks. For a list of trademarks of The Linux Foundation, please see our trademark usage page: https://www.linuxfoundation.org/trademark-usage. Linux is a registered trademark of Linus Torvalds.

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Building an End-to-End NFV & Applications Stack Powered by Kubernetes and ONAP

By Akraino Edge Stack, Blog

Written by Sagar Nangare, an Akraino community member and technology blogger who writes about data center technologies that are driving digital transformation. He is also an Assistant Manager of Marketing at Calsoft.

Introduction

In this article, we’ll look at how open source projects like Kubernetes, ONAP and many others can be stacked to build a Network Function Virtualization (NFV) framework that is identified as an Akraino Integrated Cloud Network (ICN) blueprint.  We’ll outline the step-by-step inclusion of open source projects for various purposes (security, monitoring, orchestration, etc.). Readers will leave with an understanding of the role each open source project will play in the stack.

Glossary

Here are some key terms that I use throughout this article:

NFV: Network Function Virtualization

ONAP: Open Network Automation Framework

CNF: cloud-native network functions

VNF: Virtual Network Functions

Akraino ICN Blueprint: Akraino Integrated Cloud Network or Akraino Edge Stack is a Linux Foundations project for edge computing infrastructure. It is one of the internal projects or blueprints dedicated to the cloud native NFV stack.

CNI: container-native interface

What is NFV (Network Function Virtualization)?

NFV is a concept of transforming hardware-based network functions into software-based applications. It simply decouples network functions from proprietary hardware without impacting performance.

Why do we need NFV?

  • Management and orchestration of thousands of network resources from central location
  • Enable network programmability
  • Allow dynamic scaling of network resources
  • High level automation in the network
  • Monitoring of resources and network connectivity
  • Ability to integrate new services
  • Optimization of network performance

The main reasons for enterprises and service to adopt NFV include: a vast scale of network resources containing different types of network equipment from different vendors, reducing the CAPEX and OPEX for network infrastructure, delivering services in an agile manner and enabling scalability and elasticity of network infrastructure to support rapid technology innovation.

Open Source for NFV

Kubernetes and ONAP are the key open source projects for this NFV stack. Using Kubernetes at the edge is something that leading networking solution companies are evaluating to provide dynamic capabilities managed from a central cloud. If you aren’t familiar with ONAP, it is a platform for real-time, policy-driven orchestration and automation of physical and virtual network functions.

We’ve already seen how Kubernetes is used in NFV as a backbone for cloud-native evolvement. This push for Kubernetes to dive into the NFV stack opens up possibilities for other open-source projects to make up the NFV backbone of enterprise and telecom networks. And further, Kubernetes will enable more automation and dynamic orchestration of application and infrastructure resources across the NFV-powered network.

Additionally, since various edges or hubs are involved in the typical 5G and enterprise network backed by NFV architecture, open-source projects like ONAP and Akraino blueprints are coming up with specific modules for critical orchestration and monitoring tasks.

Now that we’ve acknowledged the major role of Kubernetes and ONAP, let’s focus on the exact needs for a complete end-to-end NFV stack that will innovate a software-driven network. Let’s also look at how we can address those needs by combining different open source projects to power a 5G network and enterprise WAN.

Why Do We Need an End-to-End NFV Stack?

Currently, the IT infrastructure of enterprises and communication service providers is transitioning from centrally managed to geographically distributed. 5G presents the prominent use case for edge nodes or hubs as the initial level data center to communicate with IoT devices and host application services. And enterprises are deploying SD-WAN with edge-like capabilities.

In such architectures, workloads types like microservices, virtual network functions (VNFs) and cloud-native network functions (CNFs) are distributed. All those workload types are orchestrated by different solutions like Kubernetes and commercial solutions provided by VMware, Red Hat and Rancher.

In Figure 1, we can see that it’s difficult to manage all the workloads with different orchestrators, for several reasons. Security policy enforcement and monitoring of distributed nodes differs from centralized ones. You’ll need to manage all clouds and applications deployed on them and get insights about the performance of resources deployed on different edge sites. Additionally, this transition will cost the enterprises and telecom network providers.

Last year at ONS Europe (Open Networking Summit), Srinivasa Adeppalli (Intel) and Ravi Chunduru (Verizon) discussed using Kubernetes with ONAP4K8S, a sub-project in ONAP. This stack would manage all the applications and network functions spread across multiple Kubernetes clusters hosted on different edge nodes or data center infrastructures. They showed how a single pane of glass can orchestrate different edge sites, multiple clouds and network traffic.

Let’s See How it Works

In the presentation, Srinivas and Ravi showed an SD-WAN enterprise edge with microservices deployed in clusters and VNFs and CNFs present in an NFV stack. A request for data access generates the traffic that steers from pods to the internet through different VNFs, CNFs and external routers. Some of the microservices can be user-facing, with low latency needs. In this case, you need a multi-traffic orchestrator that controls the traffic flow and prioritizes the user-facing applications to deliver optimal performance.

Figure 3 shows the stack with the traffic orchestrator. In this scenario, Istio service mesh framework couples the microservices deployed at different edge sites. IPAM Manager ensures that each site is assigned a unique IP subnet and avoids overlapping addresses to Microservices/functions.

As we have seen, microservices, along with VNFs and CNFs, span multiple edge sites or clouds. So, we need a multi-zone manager (Figure 4).

Edge sites may face targeted attacks by external threats that can compromise network communication channels, microservices-based apps, software, and SSD/HDD. In Figure 5, we’ll use a new orchestrator called Multi-security orchestrator that uses CA Service, key distribution and attestation. You can integrate Istio, Vault project and Keycloak project with the Multi-security orchestrator to protect sensitive data as well as manage identity and access. In fact, you can run Istio from the central multi-cluster security orchestrator.

Further, a Multi-Cluster Security Orchestrator lets you monitor all the applications, VNFs and CNFs to check the health status and performance of multi-cluster app visibility and monitoring modules. Figure 6 shows how you can integrate PrometheusJaeger and Fluentd  on each edge site for data monitoring and collect the data logs for analysis at a central location.

Now we have a complete NFV stack, called an Akraino ICN (Integrated Cloud Network) blueprint.

The major frameworks used in the end-to-end NFV stack include ONAP4K8sOVN4K8sNFV, Kubernetes and Akraino SD-EWAN.

You can use ONAP4K8s as a Multi-Cloud/Cluster orchestrator to perform the following tasks:

  • Single-click applications deployment
  • Auto-configuration of service meshes
  • Auto-configuration of SD-WAN to facilitate connectivity among micro-services in multiple clusters
  • Parent CA and Child CA cert/private key enrollment for each edge/zone

You can use Kubernetes to orchestrate microservices-based applications and NFV-specific components like Multus, OVN4K8SNFV and SRIOVNIC as well as application components including Istio and Prometheus. OVN4K8sNFV works as a CNI (container-native interface) that supports multiple types of workloads, such as apps, CNFs, VNFs, etc.

Finally, Akraino SD-EWAN provides overlay connectivity among the Kubernetes clusters.

Conclusion

In this article, I’ve highlighted several open source projects that can be useful at various NFV tasks. Most of these projects are mature and widely integrated into different domains on a different scale. Each of the open source project addresses various features in NFV stack. You need to determine key capabilities from open source projects for each NFV features. You can get more information about implementation details and ongoing development from Akraino ICN and learn more about the NFV stack.

For more information about Akraino Blueprints, click here: https://wiki.akraino.org/. Or, join the conversation on the LF Edge Slack Channel. #akraino-blueprints #akraino-help #akraino-tsc