Why we cannot live without a Telco Cloud, and how does one build one?

There are a more mobile phone connections (~7.9 billion) than the number of humans (~7.7 billion) colonising this planet.

Let me explain.

Clearly, not every person in the world has a mobile device. Here we’re talking about mobile connections that come from people with multiple devices (dual SIMs, tablets) and other integrated devices like cars, and other smart vehicles, and of course the myriad IOT devices. I don’t have to go too far — my electric 2 wheeler has a mobile connection that it uses to cheerfully download the updated firmware version and the software patches every now and then.

While the global population is growing at 1.08% annually, the mobile phone connections are growing at 2.0%. We will very soon be outnumbered by the number of mobile subscriptions, all happily chatting, tweeting and in general sending data over the network. Some of it would need low latency and low jitter, while some may be more tolerant to the delays and jitter.

What’s the big deal with mobile connections growing?

Well, historically most people have used their mobile phones to talk; to catch up on all the gossip on your neighbours and relatives.

Not anymore.

Now, it’s primarily being used to watch video.

And lots of it; both cached and live.

And it will only grow.

Video traffic in mobile networks is forecast to grow by around 34 percent annually up to 2024 to account for nearly three-quarters of mobile data traffic, from approximately 60 percent currently.

Why is the mobile video traffic growing?

The growth is driven by the increase of embedded video in many online applications, growth of video-on-demand (VoD) streaming services in terms of both subscribers and viewing time per subscriber, multiple video sharing platforms, the evolution towards higher screen resolutions on smart devices. All of these factors have been influenced by the increasing penetration of video-capable smart devices.

India had (still has?) the highest average data usage per smartphone at around 9.8 GB per month by the end of 2018.

And the Internet traffic’s not even hit the peak yet.

It will hit the roof once 5G comes in. Will reach dizzying stratospheric heights when mobile content in the Indian vernacular languages comes of age.

India is home to around 19,500 languages or dialects. Every state has its own primary language and which often is alarmingly different from the state bordering it. There is a popular Hindi saying:

Kos-kos par badle paani, chaar kos par baani

The languages spoken in India change every few kilometres, as does the taste of the water.

Currently, most of the mobile content is in few popular Indian languages.

However, thats changing.

How is the Internet traffic related to the number of languages in India?

According to a Sharechat report, 2018 was the year when for the first time internet users in great numbers accessed social media in their regional languages and participated actively in contributing to user generated content in native languages.

A KPMG India and a Google report claims that the Indian language internet users are expected to grow at a CAGR of 18% vs English users at a CAGR of 3%.

This explains a flurry of investments in vernacular content startups in India.

When all these users come online, we are looking at a prodigious growth in the Internet traffic. More specifically, in the user generated traffic, which primarily would be video — again, video that is live or could be cached.

In short, we’re looking at massive quantities of data being shipped at high speeds over the Internet.

And for this to happen, the telco networks need to change.

From the rigid hardware based network to a more agile, elastic, virtualized, cloud based network. The most seismic changes will happen in the service provider network closest to the customer — the edge network. In the Jurassic age, this would have meant more dedicated hardware at the telco edge. However, given the furious rate of innovation, locking into rigid hardware platforms may not be very prudent since the networks will need to support a range of new devices, service types, and use cases. 5G with its enhanced mobile data experience will unleash innovation that’s not possible for most ordinary mortals to imagine today. The networks however need to be ready for that onslaught. They need to be designed to accommodate the agility and the flexibility that is not needed today.

And how do the networks get that agility and flexibility?

I agree with Wally for one.

The telcos will get that flexibility by virtualizing their network functions, and by, uhem, moving it all to the cloud.

Let me explain this.

Every node, every element in a network exists for a reason. It’s there to serve a function (routing, firewall, intrusion detection, etc). All this while we had dedicated, proprietary hardware that was optimized and purpose built to serve that one network function. These physical appliances had to be manually lugged and installed in the networks. I had written about this earlier here and here.

Now, replace this proprietary hardware with a pure software solution that runs on an off-the-shelf x86 based server grade hardware. One could run this software on a bare metal server or inside a virtual machine running on the hypervisor.

Viola, you just “virtualized” the “network function”!

This is your VNF.

So much for the fancy acronym.

The networks get that flexibility and agility by replacing the physical appliances with a telco cloud running the VNFs. By bringing the VNFs closer to their customer’s end devices. By distributing the processing, and management of data to micro datacenters at the periphery of the network, closer to the customer end devices. Think of it as content caching 2.0.

The edge cloud will be the first point of contact and a lot of processing will happen there. The telco giants are pushing what’s known as edge computing: where VNFs run on a telco cloud closer to the end user, thereby cutting the distance to a computer making a given decision. These VNFs, distributed across different parts of a network, run at the “edges” of the network.

Because the VNFs run on virtual machines, one could potentially run several such virtual functions on a single hypervisor. Not only does this save on hardware costs, space and power, it also simplifies the process of wiring together different network functions, as it’s all done virtually within a single device/server. The service function chaining got a lot simpler!

While we can run multiple VNFs on a single server, we can also split the VNFs across different servers to gain additional capacity during demanding periods. The VNFs can scale up, and scale down, dynamically, as the demand ebbs and flows.

This just wasn’t possible in the old world where physical network functions (fancy word for network appliances) were used. The telco operators would usually over-provision the network to optimize around the peak demand.

In the new paradigm we could use artificial intelligence and deep learning algorithms to predict the network demand and spin the VNFs in advance to meet the network demand in advance.

How can machine learning help?

Virtual Network Functions (VNFs) are easy to deploy, update, monitor, and manage. It’s after all just a special workload running on a VM. It takes less than a few seconds to spin a new instance of a VM. 

The number of VNF instances, similar to generic computing resources in cloud, can be easily scaled based on load. Auto-scaling (of resources without human intervention) has been investigated in academia and industry. Prior studies on auto-scaling use measured network traffic load to dynamically react to traffic changes.

There are several papers that explore using a Machine Learning (ML) based approach to perform auto-scaling of VNFs in response to dynamic traffic changes. The ML classifiers learn from past VNF scaling decisions and seasonal/spatial behavior of network traffic load to generate scaling decisions ahead of time. This leads to improved QoS and significant cost savings for the Telco operators.

In a 2017 Heavy Reading survey, most respondents said that AI/ML would become a critical part of their network operations by 2020. AI/ML and Big data technologies would play a pivotal role in making real time decisions when managing virtualized 5G networks. I had briefly written about it here.

Is Telco Cloud the same as a data center?

Oh, the two are different.

Performance is the key in Telco Cloud. The workloads running on the Telco Cloud are extremely sensitive to delay, packet loss and latency. A lot of hard work goes into ensuring that the packet reaches the VNF as soon as it hits the server’s NIC. You dont want the packet to slowly inch upwards through the host’s (its almost always Linux) OS before it finally reaches the VM hosting the VNF.

In a datacenter running regular enterprise workloads, a few milli seconds of delay may still be acceptable. However, on a telco cloud, running a VNF, such a delay can be catastrophic.

Linux, and its networking component is optimized for general purpose computing. This means that achieving high performance networking inside the Linux kernel is not easy, and requires some bit, ok quite a bit, of customizations and hacks to get it past the 50K packets per second limit thats often incorrectly cited as an upper limit for the Linux kernel performance. Routing packets through the kernel may work for the regular data center workloads.

However, the VNFs need something better.

Because the Linux kernel is slow, we need to completely bypass the kernel.

One could start with SR-IOV.

Very simply, with SR-IOV, a VM hosting the VNF has direct access to subset of PCI resources on a physical NIC. With an SR-IOV compliant driver, the VNF can directly DMA (Direct Memory Access) the outgoing packets to the NIC hardware to achieve higher throughput and lower latency. DMA operation from the device to the VM memory does not compromise the safety of underlying hardware. Intel IO Virtualization Technology (vt-d) supports DMA and interrupt remapping and that restricts the NIC hardware to subset of physical memory allocated for a particular VM. No hypervisor interaction is needed except for interrupt processing.

However, there is a problem with SR-IOV.

Since the packet coming from the VNF goes out of the NIC unmodified, the telco operators would need some other HW switch, or some other entity to slap on the VxLAN or the other tunneling headers on top of the data packet so that it can reach the right remote VM. You need a local VTEP that all these packets hit when they come out of the NIC.

Having a VTEP outside complicates the design. The operators would like to push the VTEP into the compute, and have a plain IP fabric that only does IP routing. There was ways to solve this problem as well, but SR-IOV has limitations on potential migration of the VM hosting the VNF from one physical server to another. This is a big problem. If the VM gets locked down, then we lose on the flexibility and the agility that we had spoken of before.

Can something else be used?


There’s a bunch of kernel bypass techniques, and I’ll only look at a few.

Intel DPDK (Data Plane Development Kit) has been used in some solutions to bypass the kernel, and then there are new emerging initiatives such as FD.io (Fast Data Input Output) based on VPP (Vector Packet Processing). More will likely emerge in the future.

DPDK and FD.io move networking into Linux user space to address both speed and technology plug-in requirements. Since these are built in the Linux user space, there are no changes in the Linux kernel. This eliminates the extra effort required to convince the Linux kernel community about the usefulness of the patches and their adoption can be accelerated.

DPDK bypasses the Linux kernel and manages the NIC and CPU assignment directly. It uses up some CPU cores for the network processing. It has threads that handle the receiving and processing of packets from the assigned receive queues. They do this in a tight loop, and anything interrupting these threads can cause packets to be dropped. That is why these threads must run on dedicated CPU cores; that is, no other threads — including the various Linux kernel tasks — in the system should run on this core.

Telcos consider this as a “waste” of their CPU cores. The cores that could have run the VNFs have now been hijacked by the DPDK to process packets from the NICs. Its also questioned if we can get a throughput of 100Gbps and beyond with DPDK and other kernel bypass techniques. It might be asinine to dedicate 30 CPU cores in a 32 core server for packet processing, leaving only 2 cores for the VNF.

Looks almost impossible to get 100Gbps+ thats needed for NFV.

Fortunately, no — things are a lot better.

Enter SmartNICs — the brainer cousin of the regular NICs, or rather NICs on steroids. These days there is a lot more brains in the modern NIC – or at least some of them – than we might realize. They take the offloading capabilities to a whole new level. The NIC vendors are packing in a lot of processors in their NIC ASICs to beef up their intelligence. Mellanox’s ConnectX-5 adapter card, which is widely deployed by hyperscalers has six different processors built into it that were designed by Mellanox.

Ok, so these are not CPUs in the normal sense, the ones you and I understand. These are purpose built to allow the NIC to, for instance, look at fields in the incoming packets, look at the IP headers and the Layer 2 headers and look at the outer encapsulated tunnel packets for VXLAN and from that do flow identification and then finally take actions.

This is history repeating itself.

Many many years ago, when dinosaurs still ruled the Earth, Cisco would use a MIPS processor to forward packets in software. And then the asteroid hit the Earth, and Cisco realized that to make the packet routing and forwarding more efficient and for it to scale, they needed custom ASICs, and they started making chips to forward packets.

This is exactly what is now happening in the Telco Cloud space. Open vSwitch was pure software that steered the data between individual virtual machines and routed it, but the performance and scalability was bad that companies started questioning on why some of the processing couldn’t be offloaded to the hardware. Perhaps, down to the NIC if you will. And thats what the latest and the greatest smartNICs do. You can download the OVS rules onto the NIC cards so that you completely bypass the Linux kernel and do all that heavy lifting in hardware.

DPDK and SmartNICs are very interesting and warrants a separate post, which i will do in some time.

So, what is the conclusion?

Aha, i meandered. I often do when I’m very excited.

The Internet traffic is exploding. It’s nowhere near the saturation point, and will increase manifold with 5G and other technologies coming in.

The Telco network can only scale if its virtualized, a’la the telco cloud. Pure hardware based old-style network, especially at the edges, will fail miserably. It will not be able to keep pace with the rapid changes that 5G will bring in. Pure hardware will still rule in the network core. Not at the edge. The edge cloud is where most of the innovation (AI/ML, kernel bypass in software, smartNICs, newer offloading capabilities, etc) will take place.

Telco cloud is possible. We have all the building blocks, today. We have the technology to virtualize, to ship packets at 100-200 Gbps to (and from) the VMs running the VNFs. Imagine the throughput that a rack full of commodity x86 servers, where each does 200Gbps, will get you.

I am very excited about the technology trendlines and the fact that what we’re working on in Nuage Networks is completely inline with where the networking industry is headed.

I am throughly enjoying this joyride. How about you?

SDN with Big Data Analytics for an Intelligent Network

Software, cloud computing and IOT are rapidly transforming networks in a way, and at a rate, never seen before. With software-as-a-service (SaaS) models, enterprises are moving more and more of their critical applications and data to public and hybrid clouds. Enterprise traffic, that never left the corporate network, is now shifting to the Internet, reaching out to different data centers across the globe. Streaming Video (Netflix, Youtube, Hulu, Amazon) accounts for an absurdly high percentage of traffic in the Internet and content providers have built out vast content distribution networks (CDNs) that overlay the Internet backbone. Higher resolutions (HD and UHD) will increase the traffic further, and by some accounts, will be over 80% of the total network traffic by 2020. More and more businesses are being created that reach their customers exclusively over the Internet (Spotify, Amazon, Safari, Zomato, etc). Real-time voice and video communications are moving to cloud-based delivery and network operators are challenged to deliver these services without impacting user quality of experience. And if this was’nt enough, with the advances being made in IOT, we have more devices than ever, lively communicating and chatting in real time over the Internet.

Security becomes a prime concern as more business critical applications migrate to the cloud. The number of DDOS attacks are only increasing and IOT devices can be compromised by hackers to launch some very lively and innovative attacks. A large scale cyber attack in 2016 used a botnet consisting of a multitude of IoT devices such as printers, camera, web cams, residential network gateways, and even baby monitors causing a major outage that brought down a big chunk of the Internet.

All this traffic goes over service provider networks that were built and designed using devices, protocols and management software from the Jurassic age. The spectacular growth and variability of traffic that is experienced today was not anticipated when these networks were built. There is a dire need to cope with changing traffic patterns and to optimize the use of available network resources at all levels (IP, MPLS and Optical) — we’ll talk about the multi-layer SDN controller that optimizes the IP-Optical layers some other time.

Given these challenges, its imperative that service providers work towards gaining realtime visibility into the network behavior and extract actionable insights needed to react immediately to network anomalies, changing traffic patterns and security threats and alarms.

And this is where big data analytics, like a knight in the shining armour, comes in.

Given the data rates that we are dealing with, and the rate at which traffic volumes and speeds are growing, deep packet inspection at line rate gets ruled out in most parts of the network. There is only so much that one can do with hardware’s brute force approach. Additionally, with most traffic being encrypted, DPI offers limited — no, zero — insight into whats happening in the network.

What can help at the scale that networks run today is streaming telemetry combined with big data analytics. Instead of constantly polling the devices in the network and then reacting to what is learnt, the new age mantra is for these devices to periodically push the relevant statistics to the data collectors, which can analyse this data and act based on that. One can argue that streaming network telemetry may not even require an IETF standard in order to be useful. A standard format like JSON could be used, and it’s up to the collector to parse and interpret the incoming barrage of data. This allows network operators to quickly write dev-ops tools that they can use to closely monitor their network and services. This opens up room for hyper innovation where new-age startups can quickly come up with products that can smartly mine the data from the network and draw rich insights into whats happening that can help the service providers in running their networks smarter and hotter.

Big data analytics entails ingesting, processing and storing exabytes worth of network data over a period of time that can be analysed later for actionable insights. With advances made in streaming analytics, this analysis can also happen in real time as the data comes piping hot from the network. New age scalable stream processors make it possible to fuse data streams to answer more sophisticated queries about the network in real-time.

By correlating data from sources beyond traditional routing and networking equipment (IX router-server views, DNS and CDN logs, firewall logs, billing and call detail records) it is possible for the analytics engine to identify patterns or behaviors that can not be identified by merely sifting through the device logs (collected traditionally using SNMP, syslogs, netflow, sflow, IPFIX, etc). The ability to correlate telemetry data from the network with applications such as Netflix or Youtube or SaaS applications such as an iOS upgrade can provide insights that can never be found with traditional traffic engineering approaches.

I claim that we now have the smarts to avoid the famous iOS7 meltdown that happened when iOS7 was released. Let’s see how:

The analytics engine feeding the controller can identify and correlate iOS updates to a new spike — an anomaly — in the network utilization inside an enterprise. The SDN controller can install more specific flows that will steer all iOS update traffic on a different path in the network. This way the controller can automatically adjust the enterprise customer flows to either (i) provide an improved iOS update experience OR (ii) prevent other enterprise traffic to get affected with the iOS update tsunami.  Advanced IP controllers (and those are being demo’ed to several service providers currently) can steer such traffic across multiple ASes as well.

We recently demo’ed a hierarchical SDN controller to a very big customer in Europe. The SDN controller was used to set up inter-domain IP/MPLS services and it used telemetry feeds to determine the realtime link utilization of the inter-domain links. We used that information to place the inter-domain IP services across multiple ASes — the new services were placed on the least utilized inter-domain link at that instance. The services could be moved around as the link utilization changed. This is very different from how its done today, where the BW utilization is reserved and services are placed based on the hard reservations. IMO, the concept of hard reservations will get obsoleted very soon. Why assume that a VPLS service on a link will take up 1Gbps, when the traffic that it “historically” sends never exceeds 100 Mbps?

The figure below shows the different sources feeding into a typical big data analytics cluster that feeds the output to the SDN controller.

Flow telemetry and network telemetry will help in monitoring the traffic flowing inside the service provider networks. We could use this to gain a deep understanding of what a network looks like during normal operations and how it looks like when an anomaly is present in the network.

If one understands the “normal”, the abnormal can become apparent. What comprises abnormal may vary from network to network and from attack to attack. It could include large traffic spikes from a single source in the network, higher-than-typical traffic “bursts” from several or many devices in the network, or traffic types detected that are not normally sent from a known device type. Once the abnormal has been identified, the attacks can be controlled and eliminated.

Network telemetry will also help in peering analytics to select the most cost-effective peering and transit connections based on current and historic traffic trends. Correlating this data with BGP feeds from route servers can help in visualizing how the traffic flows/shifts from one AS to the other.

Data collected from different sources is fed to a scalable publish/subscribe pipeline that feeds this to the big data analytics platform. Some of this can be fed to a real time streaming analytics platform for deriving rich real time insights from the network. This can then be fed to a machine learning cluster for predictive analytics.

The data is stored in a scalable data lake which can be optimized for complex, multi-dimensional queries that becomes the building block for the SDN controller to do something useful. This data can be coupled with the other data that is being learnt off different sources (syslog records, DNS and CDN logs, IX views, etc) and all this can be processed and transformed into actionable intelligence. For example, this can help service providers understand the amount of Facebook, Netflix, Youtube and Amazon Prime Video traffic thats flowing in their networks. It can help them construct a “heat map” of the most active sources and the sinks. Combine this with anonymized subscriber demographics, and the big data analytics framework can provide high fidelity insights into how the subscribers, applications and the network are correlated.

This level of insight cannot be derived by merely observing the telemetry feeds alone since it is not straightforward to correlate flows with specific applications, services and subscriber end points. The ability to mine data from a panoply of sources (as shown on the left side of the figure above — DNS servers, repositories that can identify servers and end points by owner, geo-location, type and purpose) and being able to correlate them is what differentiates the new age intelligent networks from the ones that exist today.

This level of sophistication can not be achieved without a solid big data analytics framework supporting the SDN controller. The limitless potential of what can be achieved will only unfold as more real deployments start happening in the next few years. We’re living in very interesting times, and I’m waiting with bated breath to see what the future holds and how the networking industry becomes “great again”!

Software defined WAN (SD-WAN) is really about Intelligence ..

Lets admit that most of us in the networking domain know as much about SD-WAN as an average 6th grader on fluid mechanics — which is to say pretty much nothing. We take it as something much grander and exotic than what it really is and are obviously surrounded by friends and well-wishers who wink conspiratorially that they “know it all” and consider themselves on an intellectual high ground to educate us on matters of this rich and riveting biological social interaction. Like most others in that tender and impressionable age, i did get swayed by what i heard and its only later that i was able to sort things out in my head, till it all became somewhat clear.

The proverbial clock’s wound backwards and i experience that feeling of deja-vu each time i read an article on SD-WAN that either extols its virtues or vilifies it as something that has always existed and is being speciously served on a platter dressed up as something that it is not. And like the big boys then, there are men who-know-it-all, who have already written SD-WAN off as something that has always existed and really presents nothing new here. Clearly, i disagree with that view.

I presume, perhaps a trifle rashly, that you are already aware of basic concepts of SDN and NFV (and this) and hence wouldnt waste any more oxygen explaining those.

So what really is the SD-WAN technology and the precise problem that its trying to solve?

SD-WAN is a way of architecting, designing and deploying enterprise WANs using commodity Internet connections in a manner that makes those “magically” appear as a private “MPLS-like” connection. Its the claim that it can appear “MPLS-like” that really peeves the regular-big-mpls-vendors-and-consultants. I will delve into the “MPLS-like” aspect a little later, so please hold on to your sabers till then. What makes the “magic” work is the control plane that implements and enforces the network access policies (VOIP is high priority/low latency/low jitter, big data sync medium priority and all else low priority, no VOIP via Afghanistan, etc) and the data plane that weaves an L2/L3 overlay on top of the existing consumer-grade Internet links (broadband links and in a few cases the LTE/4G connections).

The SD-WAN evangelists want to wean enterprises off their dedicated prohibitively priced private WAN connections (read MPLS circuits) with commodity enterprise broadband links. Philosophically, adding a new branch should just mean shipping a CPE device (perhaps in a virtualized form-factor) that auto-magically dials into a central controller when brought to life. Once thats done and the credentials verified, the branch should just come online (viola!) and should be visible to all the geo-separated branches. Contrast this with the provisioning time (can go as high as a year in some remote locations) and the complexity it takes to get a remote branch online today with MPLS and you will understand why most IT folks have ulcers and are perennially on anti-anxiety/depressant medicines. And btw we’ve not even begun talking about the expenses and long term contracts with the MPLS connections here!

Typically SD-WAN solutions have a central SDN controller which is really a cluster of x86 devices (servers, VMs, containers, take your pick) and hence has computing and analytical horsepower much more than a dedicated HW network device. The controller has complete visibility right from the source all the way till the destination and can constantly analyze traffic and can carve out optimal network paths for applications and individual flows based on the user and application policies. In the first mile the Internet links are either coalesced to form a fatter pipe or are used separately as dictated by the customer policies. The customer traffic is continuously finger-printed and is routed dynamically based on the real time network conditions.

Where most people go wrong is when they believe that SD-WAN solutions lose control over the traffic once it leaves the customer premises or the SD-WAN edge node. Bear in mind that there is nothing in the SD-WAN technology that prevents further control over how the traffic is routed and this could perhaps be one aspect differentiating one SD-WAN offering from the other. Since SD-WAN is an overlay technology you will not have control over each physical hop, but you can surely do something more nuanced given the application and end-to-end network visibility that exists with the controller.


Its “MPLS-like” in the sense that you can, in most cases, guarantee the available bandwidth and network up time. The central controller can monitor each overlay circuit for loss/jitter/delay and can take corrective actions when routing traffic. Patently enterprise broadband connections in certain geographies dont come with the same level of reliability as MPLS and it behooves upon us to ask ourselves if we need that level of reliability (given the cost that we pay for such connections). An enterprise can always hedge its risks by commissioning a few backup enterprise broadband connections for those rainy days when the primary is out cold. Alternatively, enterprises can go in for a hybrid approach where they maintain a low bandwidth MPLS connection for their mission-critical traffic and use the SD-WAN solution for everything else OR can implement a policy to revert to the MPLS connection when the Internet connections are not working satisfactorily. This can also provide a plausible transition strategy to the enterprises who may not be comfortable switching to SD-WANs given that the technology is still relatively new.

And do note that even MPLS connections go down, so its really not fair to say that SD-WAN solutions stand on tenuous grounds with regard to the reliability. Yes i concede that there are SLAs given with MPLS that just dont exist with regular Internet pipes. However,  one could argue that you can get some bit of extra reliability by throwing in an additional Internet link (with a different provider?) thats only there as a standby. Also note that with service providers now giving fiber connections, the size and the quality of Internet links is only going to improve with time. A large site for instance can aggregate a 1Gbps Google Fiber and a 1Gbps Verizon FIOS connection and can retain a small MPLS connection as the standby. If the enterprise discovers that its MPLS connection is underutilized it can negotiate on pricing or can go with lower MPLS pipe and thereby save on its costs.

I recently read a blog which argued that enterprise broadband promising 350Mbps would mostly give only around 320Mbps on an average. Sure this might be true in a few geographies, but seriously, who cares? Given the cost difference between a broadband connection and an MPLS circuit i will gladly assume that i only had a 300Mbps connection and derive utmost pleasure any time it gives me anything more than that!

The central controller in the SD-WAN technologies amongst other things (analyzing traffic, links) can also continually learn about the customer network conditions and can predict when link qualities will deteriorate and can preemptively reroute traffic before the links start acting up. Given that the controller is monitoring paths end-to-end and is also monitoring and analyzing the traffic emanating from the branch sites there are insights that enterprises can draw that they could have never imagined when using traditional WAN architectures since in that world all connections are really only “dumb pipes”. SD-WAN changes all that — it changes how the enterprise connections and the applications running there are viewed. The WAN architecture is aligned to the application service requirements and its management is greatly simplified. You can implement complex network policies and let the SD-WAN infrastructure sweat on your behalf (HINT: intent driven networking).

So watch out before you disdainfully write off SD-WAN as a technology thats merely replacing your dumb MPLS pipes with the regular Internet connections, since i argue, it can really do a lot more than that. Perhaps a topic worth discussing some other day.