Using an open-source model to spur AI adoption
The Linux Foundation’s (LF) Deep Learning Foundation has set itself the ambitious goal of providing companies with all the necessary artificial intelligence (AI) software they will need.
Eyal Felstaine“Everything AI, we want you to take from open source,” says Eyal Felstaine, a member of the LF Deep Learning governing board and also the CTO of Amdocs. “We intend to have the entire [software] stack.”
The Deep Learning Foundation is attracting telecom, large-scale data centre operators and other players. Orange, Ciena, Red Hat, Chinese ride-sharing firm, Didi, and Intel are the latest companies to join the initiative.
The Deep Learning Foundation’s first project is Acumos, a platform for developers to build, share and deploy AI applications. Two further projects have since been added: Angel and Elastic Deep Learning.
Goal
The 'democratisation of data' is what has motivated the founding of the deep-learning initiative, says Felstaine.
A company using a commercial AI platform must put its data in a single repository. “You are then stuck [in that environment],” says Felstaine. Furthermore, fusing data from multiple sources exacerbates the issue in that the various datasets must be uploaded to the one platform.
Using an open-source approach will result in AI software that companies can download for free. “You can run it at your own place and you are not locked into any one vendor,” says Felstaine.
Everything AI, we want you to take from open source
Deep learning, machine learning and AI
Deep learning is associated with artificial neural networks which is one way to perform machine learning. And just as deep learning is a subset of machine learning, machine learning is a subset of AI, albeit the predominant way AI is undertaken today.
“Forty years ago if you had computer chess, the program’s developers had to know how to play chess,” says Felstaine. “That is AI but it is not machine learning.”
With machine learning, a developer need not know the rules of chess. “The software developer just needs to get the machine to see enough games of chess such that the machine will know how to play,” says Felstaine.
A neural network is composed of interconnected processing units or neurons. Like AI, it is a decades-old computer science concept. But an issue has been the efficient execution of a neural network when shared across processors due to input-output constraints. Now, with the advent of the internet content providers and cloud, not only can huge datasets be used to train neural networks but the ‘hyper-connectivity’ between the servers’ virtual machines or containers means large-scale neural networks can be used.
Containers offer a more efficient way to run many elements on a server. “The numbers of virtual machines on a CPU is maybe 12 if you are lucky; with containers, it is several hundred,” says Felstaine. Another benefit of developing an application using containers is that it can be ported across different platforms.
“This [cloud clustering] is a quantitative jump in the enabling technology for traditional neural networks because you can now have thousands and even tens of thousands of nodes [neurons] that are interconnected,” says Felstaine. Running the same algorithms on much larger neural networks has only become possible in the last five years, he says.
Felstaine cites as an example the analysis of X-ray images. Typically, X-rays are examined by a specialist. For AI, the images are sent to a firm for parsing where the images are assessed and given a ‘label’. Millions of X-ray images can be labelled before being fed to a machine-learning application such as Tensorflow or H2O. Tensorflow, for example, is open-source software that is readily accessible.
The resulting trained software, referred to as a predictor, is then capable of analysing an X-ray picture and give a prognosis based on what it has learnt from the dataset of X-rays and labels created by experts. “This is pure machine learning because the person who defined Tensorflow doesn’t know anything about human anatomy,” says Felstaine. Using the software creates a model. “It’s an empty hollow brain that needs to be taught.”
Moreover, the X-ray data could be part of a superset of data from several providers such as life habits from a fitness watch, the results of a blood test, and heart data to create a more complex model. And this is where an open-source framework that avoids vendor lock-in has an advantage.
Acumos
Acumos started as a collaboration between AT&T and the Indian IT firm, Tech Mahindra, and was contributed to the LF Deep Learning Foundation.
Felstaine describes Acumos as a way to combine, or federate, different AI tools that will enable users to fuse data from various sources "and make one whole out of it”.
There is already an alpha release of Acumos and the goal, like other open-source projects, is to issue two new software releases a year.
How will such tools benefit telecom operators? Felstaine says AT&T is already using AI to save costs by helping field engineers maintain its cell towers. The field engineer uses a drone to inspect the operator’s cell towers, and employing AI to analyse the drone’s images, it guides the field engineer as to what maintenance, if any, is needed.
One North American operator has said it has over 30 AI projects including one that is guiding the operator as to how to upgrade a part of its network to minimise the project's duration and the disruption.
One goal for Acumos is to benefit the Open Networking Automation Platform (ONAP) that oversees Network Functions Virtualisation (NFV)-based networks. ONAP is an open-source project that is managed by the Linux Foundation Networking Fund.
NFV is being adopted by operators to help them lunch and scale services more efficiently and deliver operational and capital expenditure savings. But the operation and management of NFV across a complex telecom network is a challenge to achieving such benefits, says Felstaine.
ONAP already has a Data Collection, Analytics, and Events (DCAE) subsystem which collects data regarding the network’s status. Adding Acumos to ONAP promises a way for machine learning to understand the network’s workings and provide guidance when faults occur, such as the freezing of a virtual machine running a networking function.
With such a fault, the AI could guide the network operations engineer, pointing out that humans take this action next and that the action has an 85 percent success rate. It then gives the staff member the option to proceed or not. Ultimately, AI will control the networking actions and humans will be cut out of the loop. “AI as part of ONAP? That is in the future,” says Felstaine.
The two new framework projects - Angel and Elastic Deep Learning - have been contributed to the Foundation from the Chinese internet content providers, Tencent and Baidu, respectively.
Both projects address scale and how to do clustering. “They are not AI, more ways to distribute and scale neural networks,” says Felstaine.
The Deep Learning Foundation was launched in March by the firms Amdocs, AT&T, B.Yond, Baidu, Huawei, Nokia, Tech Mahindra, Tencent, Univa, and ZTE.
OPNFV's releases reflect the evolving needs of the telcos
Heather KirkseyThe open source group, part of the Linux Foundation, specialises in the system integration of network functions virtualisation (NFV) technology.
The OPNFV issued Fraser, its latest platform release, earlier this year while its next release, Gambia, is expected soon.
Moreover, the telcos continual need for new features and capabilities means the OPNFV’s work is not slowing down.
“I don’t see us entering maintenance-mode anytime soon,” says Heather Kirksey, vice president, community and ecosystem development, The Linux Foundation and executive director, OPNFV.
Meeting a need
The OPNFV was established in 2014 to address an industry shortfall.
“When we started, there was a premise that there were a lot of pieces for NFV but getting them to work together was incredibly difficult,” says Kirksey.
Open-source initiatives such as OpenStack, used to control computing, storage, and networking resources in the data centre, and the OpenDaylight software-defined networking (SDN) controller, lacked elements needed for NFV. “No one was integrating and doing automated testing for NFV use cases,” says Kirksey.
I don’t see us entering maintenance-mode anytime soon
OPNFV set itself the task of identifying what was missing from such open-source projects to aid their deployment. This involved working with the open-source communities to add NFV features, testing software stacks, and feeding the results back to the groups.
The nature of the OPNFV’s work explains why it is different from other, single-task, open-source initiatives that develop an SDN controller or NFV management and orchestration, for example. “The code that the OPNFV generates tends to be for tools and installation - glue code,” says Kirksey.
OPNFV has gained considerable expertise in NFV since its founding. It uses advanced software practices and has hardware spread across several labs. “We have a large diversity of hardware we can deploy to,” says Kirksey.
One of the OPNFV’s advanced software practices is continuous integration/ continuous delivery (CI/CD). Continuous integration refers to how code is added to a software-build while it is still being developed unlike the traditional approach of waiting for a complete software release before starting the integration and testing work. For this to be effective, however, requires automated code testing.
Continuous delivery, meanwhile, builds on continuous integration by automating a release’s update and even its deployment.
“Using our CI/CD system, we will build various scenarios on a daily, two-daily or weekly basis and write a series of tests against them,” says Kirksey, adding that the OPNFV has a large pool of automated tests, and works with code bases from various open-source projects.
Kirksey cites two examples to illustrate how the OPNFV works with the open-source projects.
When OPNFV first worked with OpenStack, the open-source cloud platform took far too long - about 10 seconds - to detect a faulty virtual machine used to implement a network function running on a server. “We had a team within OPNFV, led by NEC and NTT Docomo, to analyse what it would take to be able to detect faults much more quickly,” says Kirksey.
The result required changes to 11 different open-source projects, while the OPNFV created test software to validate that the resulting telecom-grade fault-detection worked.
Another example cited by Kirksey was to enable IPv6 support that required changes to OpenStack, OpenDaylight and FD.io, the fast data plane open source initiative.
The reason cloud-native is getting a lot of excitement is that it is much more lightweight with its containers versus virtual machines
OPNFV Fraser
In May, the OPNFV issued its sixth platform release dubbed Fraser that progresses its technology on several fronts.
Fraser offers enhanced support for cloud-native technology that use microservices and containers, an alternative to virtual machine-based network functions.
The OPNFV is working with the Cloud Native Computing Foundation (CNCF), another open-source organisation overseen by the Linux Foundation.
CNCF is undertaking several projects addressing the building blocks needed for cloud-native applications. The most well-known being Kubernetes, used to automate the deployment, scaling and management of containerised applications.
“The reason cloud-native is getting a lot of excitement is that it is much more lightweight with its containers versus virtual machines,” says Kirksey. “It means more density of what you can put on your [server] box and that means capex benefits.”
Meanwhile, for applications such as edge computing, where smaller devices will be deployed at the network edge, lightweight containers and Kubernetes are attractive, says Kirksey.
Another benefit of containers is faster communications. “Because you don’t have to go between virtual machines, communications between containers is faster,” she says. “If you are talking about network functions, things like throughput starts to become important.”
The OPNFV is working with cloud-native technology in the same way it started working with OpenStack. It is incorporating the technology within its frameworks and undertaking proof-of-concept work for the CNCF, identifying shortfalls and developing test software.
OPNFV has incorporated Kubernetes in all its installers and is adopting other CNCF work such as the Prometheus project used for monitoring.
“There is a lot of networking work happening in CNCF right now,” says Kirksey. “There are even a couple of projects on how to optimise cloud-native for NFV that we are also involved in.”
OPNFV’s Fraser also enhances carrier-grade features. Infrastructure maintenance work can now be performed without interrupting virtual network functions.
Also expanded are the metrics that can be extracted from the underlying hardware, while the OPNFV’s Calipso project has added modules for service assurance as well as support for Kubernetes.
Fraser has also improved the support for testing and can allocate hardware dynamically across its various labs. “Basically we are doing more testing across different hardware and have got that automated as well,” says Kirksey.
Linux Foundation Networking Fund
In January, the Linux Foundation combined the OPNFV with five other open-source telecom projects it is overseeing to create the Linux Foundation Networking Fund (LNF).
The other five LNF projects are the Open Network Automation Platform (ONAP), OpenDaylight, FD.io, the PNDA big data analytics project, and the SNAS streaming network analytics system.
Edge is becoming a bigger and more important use-case for a lot of the operators
“We wanted to break down the silos across the different projects,” says Kirksey. There was also overlap with members sitting on several projects’ boards. “Some of the folks were spending all their time in board meetings,” says Kirksey.
Service provider Orange is using the OPNFV Fraser functional testing framework as it adopts ONAP. Orange used the functional testing to create its first test container for ONAP in one day. Orange also achieved a tenfold reduction in memory demands, going from a 1-gigabyte test virtual machine to a 100-megabyte container. And the operator has used the OPNFV’s CI/CD toolchain for the ONAP work.
By integrating the CI/CD toolchain across projects, OPNFV says it is much easier to incorporate new code on a regular basis and provide valuable feedback to the open source projects.
The next code release, Gambia, could be issued as early as November.
Gambia will offer more support for cloud-native technologies. There is also a need for more work around Layer 2 and Layer 3 networking as well as edge computing work involving OpenStack and Kubernetes.
“Edge is becoming a bigger and more important use-case for a lot of the operators,” says Kirksey.
OPNFV is also continuing to enhance its test suites for the various projects. “We want to ensure we can support the service providers real-world deployment needs,” concludes Kirksey.
How ONAP is blurring network boundaries
Telecom operators will soon be able to expand their networks by running virtualised network functions in the public cloud. This follows work by Amdocs to port the open-source Open Network Automation Platform (ONAP) onto Microsoft’s Azure cloud service.
Source: Amdocs, Linux Foundation
According to Craig Sinasac, network product business unit manager at Amdocs, several telecom operators are planning to run telecom applications on the Azure platform, and the software and services company is already working with one service provider to prepare the first trial of the technology.
Deploying ONAP in the public cloud blurs the normal understanding of what comprises an operator’s network. The development also offers the prospect of web-scale players delivering telecom services using ONAP.
ONAP
ONAP is an open-source network management and orchestration platform, overseen by the Linux Foundation. It was formed in 2017 with the merger of two open-source orchestration and management platforms: AT&T’s ECOMP platform, and Open-Orchestrator (Open-O), a network functions virtualisation platform initiative backed by companies such as China Mobile, China Telecom, Ericsson, Huawei and ZTE.
The ONAP framework’s aim is the become the telecom industry’s de-facto orchestration and management platform.
Craig SinasacAmdocs originally worked with AT&T to develop ECOMP as part of the operator’s Domain 2.0 initiative.
“Amdocs has hundreds of people working on ONAP and is the leading vendor in terms of added lines of code to the open-source project,” says Sinasac.
Amdocs has make several changes to the ONAP code to port it onto the Azure platform.
The company is using Kubernetes, the open-source orchestration system used to deploy, scale and manage container-based applications. Containers, used with micro-services, offer several advantages compared to running networks functions on virtual machines.
Amdocs is also changing ONAP components to make use of TOSCA cloud generic descriptor files that are employed with the virtual network functions. The descriptor files are an important element to enable virtual network functions from different vendors to work on ONAP, simplifying the operator effort needed for their integration.
“There are also changes to the multiVIM component of ONAP, to enable Azure cloud control,” says Sinasac. MultiVIM is designed to decouple ONAP from the underlying cloud infrastructure.
Further work is needed so that ONAP can manage a multi-cloud environment. One task is to enable closed-loop control by completing work already underway to the ONAP Data Collection, Analytics, and Events (DCAE) component to run in containers. The DCAE is a component of ONAP that is of interest to several operators that recently joined ONAP.
Amdocs is making its changes available as open-source code.
Business opportunities
For Microsoft, porting ONAP onto Azure promises new operator customers. Microsoft is also keen for vendors like Amdocs to use Azure for their own development work.
Telecom operators could use the Azure platform in several ways. An operator running ONAP on its own cloud-based network could use the platform to spin up additional network functions on the Azure platform. This could be to expand network capacity, restore the network in case of a fault, or to host location-sensitive network functions where the operator has no presence.
A telco could also use Azure’s data centres to expand into regions where it has no presence.
Amdocs says cloud players could offer telecom and over-the-top services using ONAP. “As long as they have connectivity to their customers,” says Sinasac.
Ciena picks ONAP’s policy code to enhance Blue Planet
Operators want to use automation to help tackle the growing complexity and cost of operating their networks.
Kevin Wade“Policy plays a key role in this goal by enabling the creation and administration of rules that automatically modify the network’s behaviour,” says Kevin Wade, senior director of solutions, Ciena’s Blue Planet.
Incorporating ONAP code to enhance Blue Planet’s policy engine also advances Ciena’s own vision of the adaptive network.
Automation platforms
ONAP and Ciena’s Blue Planet are examples of network automation platforms.
ONAP is an open software initiative created by merging a large portion of AT&T’s original Enhanced Control, Orchestration, Management and Policy (ECOMP) software developed to power its own software-defined network and the OPEN-Orchestrator (OPEN-O) project, set up by several companies including China Mobile, China Telecom and Huawei.
ONAP’s goal is to become the default automation platform for service providers as they move to a software-driven network using such technologies as network functions virtualisation (NFV) and software-defined networking (SDN).
Blue Planet is Ciena’s own open automation platform for SDN and NFV-based networks. The platform can be used to manage Ciena’s own platforms and has open interfaces to manage software-defined networks and third-party equipment.
Ciena gained the Blue Planet platform with the acquisition of Cyan in 2015. Since then Ciena has added two main elements.
One is the Manage, Control and Plan (MCP) component that oversees Ciena's own telecom equipment. Ciena’s Liquid Spectrum that adds intelligence to its optical layer is part of MCP.
The second platform component added is analytics software to collect and process telemetry data to detect trends and patterns in the network to enable optimisation.
“We have 20-plus [Blue Planet] customers primarily on the orchestration side,” says Wade. These include Windstream, Centurylink and Dark Fibre Africa of South Africa. Out of these 20 or so customers, one fifth do not use Ciena’s equipment in their networks. One such operator is Orange, another Blue Planet user Ciena has named.
A further five service providers are trialing an upgraded version of MCP, says Wade, while two operators are using Blue Planet’s analytics software.
In a closed-loop automation process, the policy subsystem guides the orchestration or the SDN controller, or both, to take actions
Policy
Ciena has been a member of the ONAP open source initiative for one year. By integrating ONAP’s policy components into Blue Planet, the platform will support more advanced closed-loop network automation use cases, enabling smarter adaptation.
“In a closed-loop automation process, the policy subsystem guides the orchestration or the SDN controller, or both, to take actions,” says Wade. Such actions include scaling capacity, restoring the network following failure, and automatic placement of a virtual network function to meet changing service requirements.
In return for using the code, Ciena will contribute bug fixes back to the open source venture and will continue the development of the policy engine.
The enhanced policy subsystem’s functionalities will be incorporated over several Blue Planet releases, with the first release being made available later this year. “Support for the ONAP virtual network function descriptors and packaging specifications are available now,” says Wade.
The adaptive network
Software control and automation, in which policy plays an important role, is one key component of Ciena's envisaged adaptive network.
A second component is network analytics and intelligence. Here, real-time data collected from the network is fed to intelligent systems to uncover the required network actions.
The final element needed for an adaptive network is a programmable infrastructure. This enables network tuning in response to changing demands.
What operators want, says Wade, is automation, guided by analytics and intent-based policies, to scale, configure, and optimise the network based on a continual reading to detect changing demands.
Will white boxes predominate in telecom networks?
Will future operator networks be built using software, servers and white boxes or will traditional systems vendors with years of network integration and differentiation expertise continue to be needed?
AT&T’s announcement that it will deploy 60,000 white boxes as part of its rollout of 5G in the U.S. is a clear move to break away from the operator pack.
The service provider has long championed network transformation, moving from proprietary hardware and software to a software-controlled network based on virtual network functions running on servers and software-defined networking (SDN) for the control switches and routers.
Glenn WellbrockNow, AT&T is going a stage further by embracing open hardware platforms - white boxes - to replace traditional telecom hardware used for data-path tasks that are beyond the capabilities of software on servers.
For the 5G deployment, AT&T will, over several years, replace traditional routers at cell and tower sites with white boxes, built using open standards and merchant silicon.
“White box represents a radical realignment of the traditional service provider model,” says Andre Fuetsch, chief technology officer and president, AT&T Labs. “We’re no longer constrained by the capabilities of proprietary silicon and feature roadmaps of traditional vendors.”
But other operators have reservations about white boxes. “We are all for open source and open [platforms],” says Glenn Wellbrock, director, optical transport network - architecture, design and planning at Verizon. “But it can’t just be open, it has to be open and standardised.”
Wellbrock also highlights the challenge of managing networks built using white boxes from multiple vendors. Who will be responsible for their integration or if a fault occurs? These are concerns SK Telecom has expressed regarding the virtualisation of the radio access network (RAN), as reported by Light Reading.
“These are the things we need to resolve in order to make this valuable to the industry,” says Wellbrock. “And if we don’t, why are we spending so much time and effort on this?”
Gilles Garcia, communications business lead director at programmable device company, Xilinx, says the systems vendors and operators he talks to still seek functionalities that today’s white boxes cannot deliver. “That’s because there are no off-the-shelf chips doing it all,” says Garcia.
We’re no longer constrained by the capabilities of proprietary silicon and feature roadmaps of traditional vendors
White boxes
AT&T defines a white box as an open hardware platform that is not made by an original equipment manufacturer (OEM).
A white box is a sparse design, built using commercial off-the-shelf hardware and merchant silicon, typically a fast router or switch chip, on which runs an operating system. The platform usually takes the form of a pizza box which can be stacked for scaling, while application programming interfaces (APIs) are used for software to control and manage the platform.
As AT&T’s Fuetsch explains, white boxes deliver several advantages. By using open hardware specifications for white boxes, they can be made by a wider community of manufacturers, shortening hardware design cycles. And using open-source software to run on such platforms ensures rapid software upgrades.
Disaggregation can also be part of an open hardware design. Here, different elements are combined to build the system. The elements may come from a single vendor such that the platform allows the operator to mix and match the functions needed. But the full potential of disaggregation comes from an open system that can be built using elements from different vendors. This promises cost reductions but requires integration, and operators do not want the responsibility and cost of both integrating the elements to build an open system and integrating the many systems from various vendors.
Meanwhile, in AT&T’s case, it plans to orchestrate its white boxes using the Open Networking Automation Platform (ONAP) - the ‘operating system’ for its entire network made up of millions of lines of code.
ONAP is an open software initiative, managed by The Linux Foundation, that was created by merging a large portion of AT&T’s original ECOMP software developed to power its software-defined network and the OPEN-Orchestrator (OPEN-O) project, set up by several companies including China Mobile and China Telecom.
AT&T has also launched several initiatives to spur white-box adoption. One is an open operating system for white boxes, known as the dedicated network operator system (dNOS). This too will be passed to The Linux Foundation.
The operator is also a key driver of the open-based reconfigurable optical add/ drop multiplexer multi-source agreement, the OpenROADM MSA. Recently, the operator announced it will roll out OpenROADM hardware across its network. AT&T has also unveiled the Akraino open source project, again under the auspices of the Linux Foundation, to develop edge computing-based infrastructure.
At the recent OFC show, AT&T said it would limit its white box deployments in 2018 as issues are still to be resolved but that come 2019, white boxes will form its main platform deployments.
Xilinx highlights how certain data intensive tasks - in-line security, performed on a per-flow basis, routing exceptions, telemetry data, and deep packet inspection - are beyond the capabilities of white boxes. “White boxes will have their place in the network but there will be a requirement, somewhere else in the network for something else, to do what the white boxes are missing,” says Garcia.
Transport has been so bare-bones for so long, there isn’t room to get that kind of cost reduction
AT&T also said at OFC that it expects considerable capital expenditure cost savings - as much as a halving - using white boxes and talked about adopting in future reverse auctioning each quarter to buy its equipment.
Niall Robinson, vice president, global business development at ADVA Optical Networking, questions where such cost savings will come from: “Transport has been so bare-bones for so long, there isn’t room to get that kind of cost reduction. He also says that there are markets that already use reverse auctioning but typically it is for items such as components. “For a carrier the size of AT&T to be talking about that, that is a big shift,” says Robinson.
Layer optimisation
Verizon’s Wellbrock first aired reservations about open hardware at Lightwave’s Open Optical Conference last November.
In his talk, Wellbrock detailed the complexity of Verizon’s wide area network (WAN) that encompasses several network layers. At layer-0 are the optical line systems - terminal and transmission equipment - onto which the various layers are added: layer-1 Optical Transport Network (OTN), layer-2 Ethernet and layer-2.5 Multiprotocol Label Switching (MPLS). According to Verizon, the WAN takes years to design and a decade to fully exploit the fibre.
“You get a significant saving - total cost of ownership - from combining the layers,” says Wellbrock. “By collapsing those functions into one platform, there is a very real saving.” But there is a tradeoff: encapsulating the various layers’ functions into one box makes it more complex.
“The way to get round that complexity is going to a Cisco, a Ciena, or a Fujitsu and saying: ‘Please help us with this problem’,” says Wellbrock. “We will buy all these individual piece-parts from you but you have got to help us build this very complex, dynamic network and make it work for a decade.”
Next-generation metro
Verizon has over 4,000 nodes in its network, each one deploying at least one ROADM - a Coriant 7100 packet optical transport system or a Fujitsu Flashwave 9500. Certain nodes employ more than one ROADM; once one is filled, a second is added.
“Verizon was the first to take advantage of ROADMs and we have grown that network to a very large scale,” says Wellbrock.
The operator is now upgrading the nodes using more sophiticated ROADMs, as part of its next-generation metro. Now each node will need only one ROADM that can be scaled. In 2017, Verizon started to ramp and upgraded several hundred ROADM nodes and this year it says it will hit its stride before completing the upgrades in 2019.
“We need a lot of automation and software control to hide the complexity of what we have built,” says Wellbrock. This is part of Verizon’s own network transformation project. Instead of engineers and operational groups in charge of particular network layers and overseeing pockets of the network - each pocket being a ‘domain’, Verizon is moving to a system where all the networks layers, including ROADMs, are managed and orchestrated using a single system.
The resulting software-defined network comprises a ‘domain controller’ that handles the lower layers within a domain and an automation system that co-ordinates between domains.
“Going forward, all of the network will be dynamic and in order to take advantage of that, we have to have analytics and automation,” says Wellbrock.
In this new world, there are lots of right answers and you have to figure what the best one is
Open design is an important element here, he says, but the bigger return comes from analytics and automation of the layers and from the equipment.
This is why Wellbrock questions what white boxes will bring: “What are we getting that is brand new? What are we doing that we can’t do today?”
He points out that the building blocks for ROADMs - the wavelength-selective switches and multicast switches - originate from the same sub-system vendors, such that the cost points are the same whether a white box or a system vendor’s platform is used. And using white boxes does nothing to make the growing network complexity go away, he says.
“Mixing your suppliers may avoid vendor lock-in,” says Wellbrock. “But what we are saying is vendor lock-in is not as serious as managing the complexity of these intelligent networks.”
Wellbrock admits that network transformation with its use of analytics and orchestration poses new challenges. “I loved the old world - it was physics and therefore there was a wrong and a right answer; hardware, physics and fibre and you can work towards the right answer,” he says. “In this new world, there are lots of right answers and you have to figure what the best one is.”
Evolution
If white boxes can’t perform all the data-intensive tasks, then they will have to be performed elsewhere. This could take the form of accelerator cards for servers using devices such as Xilinx’s FPGAs.
Adding such functionality to the white box, however, is not straightforward. “This is the dichotomy the white box designers are struggling to address,” says Garcia. A white box is light and simple so adding extra functionality requires customisation of its operating system to run these application. And this runs counter to the white box concept, he says.
We will see more and more functionalities that were not planned for the white box that customers will realise are mandatory to have
But this is just what he is seeing from traditional systems vendors developing designs that are bringing differentiation to their platforms to counter the white-box trend.
One recent example that fits this description is Ciena’s two-rack-unit 8180 coherent network platform. The 8180 has a 6.4-terabit packet fabric, supports 100-gigabit and 400-gigabit client-side interfaces and can be used solely as a switch or, more typically, as a transport platform with client-side and coherent line-side interfaces.
The 8180 is not a white box but has a suite of open APIs and has a higher specification than the Voyager and Cassini white-box platforms developed by the Telecom Infra Project.
“We are going through a set of white-box evolutions,” says Garcia. “We will see more and more functionalities that were not planned for the white box that customers will realise are mandatory to have.”
Whether FPGAs will find their way into white boxes, Garcia will not say. What he will say is that Xilinx is engaged with some of these players to have a good view as to what is required and by when.
It appears inevitable that white boxes will become more capable, to handle more and more of the data-plane tasks, and as a response to the competition from traditional system vendors with their more sophisticated designs.
AT&T’s white-box vision is clear. What is less certain is whether the rest of the operator pack will move to close the gap.
