Category: traffic engineering

The Mythical Use Cases: Traffic Engineering for Data Center Backups

Vendor product managers love discussing mythical use cases to warrant complex functionality in their gear. Long-distance VM mobility was one of those (using it for disaster avoidance was Mission Impossible under any real-world assumptions), and high-volume network-based backups seems to be another. Here’s what someone had to say about that particular unicorn in a LinkedIn comment when discussing whether we need traffic engineering in a data center fabric.

When you’re dealing with a large cluster on a fabric, you will see things like inband backup. The most common one I’ve seen is VEEAM. Those inband backups can flood a single link, and no amount of link scheduling really solves that; depending on the source, they can saturate 100G. There are a couple of solutions; IPv6 or eBGP SID has been used to avoid these links or schedule avoidance for other traffic.

It is true that (A) in-band backups can be bandwidth intensive and that (B) well-written applications can saturate 100G server links. However:

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Worth Reading: Was MPLS TE Worth the Effort?

Bruce Davie continues documenting the tradeoffs we had to make in networking, this time with Was MPLS Traffic Engineering Worthwhile? I found this bit particularly familiar:

It wasn’t hard to make a theoretical argument that MPLS-TE could improve network performance and average link utilization, by moving traffic from congested links to uncongested ones. The hard part was proving that it would actually do a better job in practice than the more traditional methods such as using link weights and multipath routing to achieve the same ends.

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Sample Lab: SR-MPLS on Junos and SR Linux

Last week I published a link to Pete Crocker’s RSVP-TE lab, but there’s more: he created another lab using the same topology that uses SR-MPLS with IS-IS to get the job done.

Jeroen Van Bemmel did something similar for SR Linux: his lab topology has fewer devices (plus SR Linux runs in containers), so it’s easily deployable on machines without humongous amount of memory.

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Machine Learning and Network Traffic Management

A while ago Russ White (answering a reader question) mentioned some areas where we might find machine learning useful in networking:

If we are talking about the overlay, or traffic engineering, or even quality of service, I think we will see a rising trend towards using machine learning in network environments to help solve those problems. I am not convinced machine learning can solve these problems, in the sense of leaving humans out of the loop, but humans could set the parameters up, let the neural network learn the flows, and then let the machine adjust things over time. I tend to think this kind of work will be pretty narrow for a long time to come.

Guess what: as fancy as it sounds, we don’t need machine learning to solve those problems.

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Why Would You Need BGP-LS and PCEP?

My good friend Tiziano Tofoni (the organizer of wonderful autumn seminars in Rome) sent me these questions after attending the BGP-LS and PCEP Deep Dive webinar, starting with:

Are there real use cases for BGP-LS and PCEP? Are they really useful? Personally I do not think they will ever be used by ISP in their (large) networks.

There are some ISPs that actually care about the network utilization on their expensive long-distance links.

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New Webinar: BGP-LS and PCEP

I was often asked about two emerging technologies that enable standard controller-based WAN traffic engineering: BGP-LS to extract the network topology and PCEP to establish end-to-end tunnels from a controller.

Unfortunately, I never found time to explore these emerging technologies and develop a webinar. However, after Julian Lucek from Juniper did such a great job on the NorthStar podcast, I asked him whether he would be willing to do a deep dive technology webinar on the two technologies and he graciously agreed to do it.

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Fibbing: OSPF-Based Traffic Engineering with Laurent Vanbever

You might be familiar with the idea of using BGP as an SDN tool that pushes forwarding entries into routing and forwarding tables of individual devices, allowing you to build hop-by-hop path across the network (more details in Packet Pushers podcast with Petr Lapukhov).

Researchers from University of Louvain, ETH Zürich and Princeton figured out how to use OSPF to get the same job done and called their approach Fibbing. For more details, listen to Episode 45 of Software Gone Wild podcast with Laurent Vanbever (one of the authors), visit the project web site, or download the source code.

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Optimizing Traffic Engineering with NorthStar Controller on Software Gone Wild

Content providers were using centralized traffic flow optimization together with MPLS TE for at least 15 years (some of them immediately after Cisco launched the early MPLS-TE implementation in their 12.0(5)T release), but it was always hard to push the results into the network devices.

PCEP and BGP-LS all changed that – they give you a standard mechanism to extract network topology and install end-to-end paths across the network, as Julian Lucek of Juniper Networks explained in Episode 43 of Software Gone Wild.

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