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A few weeks ago, I was in the midst of a conversation about EVPNs, how they work, and the use cases for deploying them, when one of the participants exclaimed: “This is so complicated… why don’t we stick with the older way of doing things with multi-chassis link aggregation and virtual chassis device?” Sometimes it does seem like we create complex solutions when a simpler solution is already available. Since simpler is always better, why not just use them? After all, simpler solutions are easier to understand, which means they are easier to deploy and troubleshoot.

The problem is we too often forget the other side of the simplicity equation—complexity is required to solve hard problems and adapt to demanding environments. While complex systems can be fragile (primarily through ossification), simple solutions can flat out fail just because they can’t cope with changes in their environment.

As an example, consider MLAG. On the surface, MLAG is a useful technology. If you have a server that has two network interfaces but is running an application that only supports a single IP address as a service access point, MLAG is a neat and useful solution. Connect a single (presumably high reliability) server to two different upstream switches through two different network interface cards, which act like one logical Ethernet network. If one of the two network devices, optics, cables, etc., fails, the server still has network connectivity.

Neat.

But MLAG has well-known downsides, as well. There is a little problem with the physical locality of the cables and systems involved. If you have a service split among multiple services, MLAG is no longer useful. If the upstream switches are widely separated, then you have lots of cabling fun and various problems with jitter and delay to look forward to.

There is also the little problem of MLAG solutions being mostly proprietary. When something fails, your best hope is a clueful technical assistance engineer on the other end of a phone line. If you want to switch vendors for some reason, you have the fun of taking the entire server out of operation for a maintenance window to do the switch, along with the vendor lock-in in the case of failure, etc.

EVPN, with its ability to attach a single host through multiple virtual Ethernet connections across an IP network, is a lot more complex on the surface. But looks can be deceiving… In the case of EVPN, you see the complexity “upfront,” which means you can (largely) understand what it is doing and how it is doing it. There is no “MLAG black box;” useful in cases where you must troubleshoot.

And there will be cases where you will need to troubleshoot.

Further, because EVPN is standards-based and implemented by multiple vendors, you can switch over one virtual connection at a time; you can switch vendors without taking the server down. EVPN is a much more flexible solution overall, opening up possibilities around multihoming across different pods in a butterfly fabric, or allowing the use of default MAC addresses to reduce table sizes.
Virtual chassis systems can often solve some of the same problems as EVPN, but again—you are dealing with a black box. The black box will likely never scale to the same size, and cover the same use cases, as a community-built standard like EVPN.

The bottom line

Sometimes starting with a more complex set of base technologies will result in a simpler overall system. The right system will not avoid complexity, but rather reduce it where possible and contain it where it cannot be avoided. If you find your system is inflexible and difficult to manage, maybe its time to go back to the drawing board and start with something a little more complex, or where the complexity is “on the surface” rather than buried in an abstraction. The result might actually be simpler.

One “sideways” place to look for value in the network is in a place that initially seems far away from infrastructure, data gravity. Data gravity is not something you might often think about directly when building or operating a network, but it is something you think about indirectly. For instance, speeds and feeds, quality of service, and convergence time are all three side effects, in one way or another, of data gravity.

As with all things in technology (and life), data gravity is not one thing, but two, one good and one bad—and there are tradeoffs. Because if you haven’t found the tradeoffs, you haven’t looked hard enough. All of this is, in turn, related to the CAP Theorem.

Data gravity is, first, a relationship between applications and data location. Suppose you have a set of applications that contain customer information, such as forms of payment, a record of past purchases, physical addresses, and email addresses. Now assume your business runs on three applications. The first analyzes past order information and physical location to try to predict potential interests in current products. The second fills in information on an order form when a customer logs into your company’s ecommerce site. The third is used by marketing to build and send email campaigns.

Think about where you would store this information physically, especially if you want the information used by all three applications to be as close to accurate as possible in near-real time. CAP theorem dictates that if you choose to partition the data, say by having one copy of the customer address information in an on-premises fabric for analysis and another in a public cloud service for the ecommerce front-end, you are you going to have to live with either locking one copy of the record or the other while it is being updated, or you are going to have to live with one of the two copies of the record being out-of-date while its being used (the database can either have a real time locking system or be eventually consistent). If you really want the data used by all three applications to be accurate in near-real time, the only solution you have is to run all three applications in the same physical infrastructure so there can be one unified copy of all three data.

This causes the first instance of data gravity. If you want the ordering front end to work well, you need to unify the data stores, and move them physically close to the application. If you want the other applications that interact with this same data to work well, you need to move them to the same physical infrastructure as the data they rely on. Data gravity, in this case, acts like a magnet; the more data that is stored in a location, the more applications will want to be in that same location. The more applications that run on a particular infrastructure, the more data will be stored there, as well. Data follows applications, and applications follow data. Data gravity, however, can often work against the best interests of the business. The public cloud is not the most efficient place to house every piece of data, nor is it the best place to do all processing.

Minimizing the impact of the CAP theorem by building and operating a network that efficiently moves data minimizes the impact of this kind of data gravity.

The second instance of data gravity is sometimes called Kai-Fu Lee’s Virtuous Cycle, or the KL-VC. Imagine the same set of applications, only now look at the analysis part of the system more closely. Clearly, if you knew more about your customers than their location, you could perform better analysis, know more about their preferences, and be able to target your advertising more carefully. This might (or might not—there is some disagreement over just how fine-grained advertising can be before it moves from useful to creepy and productive to counterproductive) lead directly to increased revenues flowing from the ecommerce site.

But obtaining, storing, and processing this data means moving this data, as well—a point not often considered when thinking about how rich of a shopping experience “we might be able to offer.” Again, the network provides the key to moving the data so it can be obtained, stored, and processed.

At first glance, this might all still seem like commodity stuff—it is still just bits on the wire that need to be moved. Deeper inspection, however, reveals that this simply is not true. Understanding where data lives and how applications need to use it, the tradeoffs of CAP against that data, and the changes in the way the company does business if that data could be moved more quickly and effectively. Understanding which data flow has real business impact, and when, is a gateway into understanding how to add business value.

If you could say to your business leaders: let’s talk about where data is today, where it needs to be tomorrow, and how we can build a network where we can consciously balance between network complexity, network cost, and application performance, would that change the game at all? What if you could say: let’s talk about building a network that provides minimal jitter and fixed delay for defined workloads, and yet allows data to be quickly tied to public clouds in a way that allows application developers to choose what is best for any particular use case?

This topic is a part of my talk at NXTWORK 2019—if you’ve not yet registered to attend, right now is a good time to do so.

A post on Martin Fowler’s blog this week started me thinking about lock-in—building a system that only allows you to purchase components from a single vendor so long as the system is running. The point of Martin’s piece is that lock-in exists in all systems, even open source, and hence you need to look at lock-in as a set of tradeoffs, rather than always being a negative outcome. Given that lock-in is a tradeoff, and that lock-in can happen regardless of the systems you decide to deploy, I want to go back to one of the foundational points Martin makes in his post and think about avoiding lock-in a little differently than just choosing between open source and vendor-based solutions.

If cannot avoid lock-in either by choosing a vendor-based solution or by choosing open source, then you have two choices. The first is to just give up and live with the results of lock-in. In fact, I have worked with a lot of companies who have done just this—they have accepted that lock-in is just a part of building networks, that lock-in results in a good transfer of risk to the vendor from the operator, or that lock-in results in a system that is easier to deploy and manage.

Giving in to lock-in, though, does not seem like a good idea on the surface, because architecture is about creating opportunity. If you cannot avoid lock-in and yet lock-in is antithetical to good architecture, what are your other options?

First, you can deepen your understanding of where and how lock-in matters, and when it doesn’t. Architecture often fails in one of three ways. The first way is designing something that is so closely fit-to-purpose today that is inflexible against future requirements. Think of a pair of pants that fit perfectly today; if you gain a few pounds, you might have to start over with your body-covering pant solution after that upcoming holiday. The second way is designing something that is so flexible it is wasteful. Think of a pair of pants that are sized to fit you no matter what your body size might be. Perhaps you might try to create a pair of pants you can wear when you are two, but also when you are seventy-two? You could design such a thing, I’m certain, but the amount of material you’d need to use in creating it would probably involve enough waste that you might as well just go ahead and sew multiple sets of pants across your lifetime. The third way is in building something that is flexible, but only through ossification. Something which can only be changed by adding or multiplying, and never through subtraction or division. Think of a pair of pants that can be changed to any color by adding a thin layer of new material each time.

All three of these are overengineered in one sense or another. They are trying to solve problems that probably don’t exist, that may never exist, or that can only be solved by adding another layer on top. All three of these are also forms of lock-in which should be avoided.

In order to buy the right kind of pants, you have to know how long you expect the pants to fit, and you have to know how your body is going to change over that time period. This is where the architect needs to connect with the business and try to understand what kinds of changes are likely, and what those changes might mean for the network. This is also the place where the architect needs to feed some technical reality back into the business; most of the time business leaders don’t think about technology challenges because no-one has ever explained to them that networks and information technology systems are not infinitely pliable balls of wax.

Second, you can try to contain lock-in, and intentionally refuse to be locked-in above a modular level. For instance, say you build a data center fabric using a marketecture sold by a vendor. If that marketecture can be contained within a single data center fabric, and you have more than one fabric, then you can avoid some degree of lock-in by building your other fabrics with different solutions. If the marketecture doesn’t allow for this, then maybe you should just say “no.” Modular design is not just good for controlling the leakage of state between modules, it can also be good for controlling the leakage of lock-in.

Finally, you can consider proper modularization vertically in your network, from layer to layer in your network stack. Enforce IPv4 or IPv6 as a single “wasp waist,” in the network transport; don’t allow Ethernet to wander all over the place, across modules and layers, as if it were the universal transport. Enforce separate and underlay and overlay control planes. Enforce a single form of storing information (such as all YANG formatted data, or all JSON formatted data, etc.) in your network management systems.

This is what open standards are good for. It will be painful to build to these standards, but these standards represent a point of abstraction where you can split lock-in systems apart, preventing them from entangling with one another in a way that prevents ever replacing one component with another.

Lock-in is not something you can avoid. On the other hand, you can learn to accept it when you know it will not impact your future options, and contain it when you know it will.