In virtual and cloud environments, network traffic often flows into a virtualization, then back out, forwarded to another device, usually security, before it re-enters the virtual environment. I call this a “sadly defined network,” not software-defined. Many of my colleagues claim that this is not true. They say that an SDN keeps east-west traffic within the hypervisor and that north-south would not need to do this. I disagree. This will happen when bad design is implemented in virtual and physical security. “Ah!” some will say, “this is solved by micro-segmentation,” but that is not always true, either.
To identify the problem, we need to start by looking at our network and security architectures. The goal should be to keep as much traffic inside the virtual or cloud environment as possible, only heading out when absolutely necessary. I often see this goal implemented so badly that I don’t understand why some systems run at all; they look like they should crater under their own weight. The worst offenders use hair-pinning to implement security features. Hair-pinning will at least double, and often triple, bandwidth requirements. This occurs even if the components are east-west and not north-south. Decision points should be made closer to the virtual machines. Alas, this is where NSX’s distributed micro-segmentation firewall comes into play. Most people want edge-like functionality such as network address translation; this doesn’t work with micro-segmentation. So, once more we are faced with either placing an edge on every host, sending traffic north-south, or sending it east-west. In any case we are potentially moving lots of data outside our virtualization hosts to somewhere else, or even between hosts on the same hardware.
Figure 1 shows a typical physical firewall configuration, with paths in, out, and then back in to implement this method of security. For good or passed traffic, there is a 3x bandwidth cost. Data goes in, then out, then back in via the top-of-rack switch.
We can fix this by putting the firewall within the virtual environment. Yet, that just forms a new type of hairpin which will ignore perhaps just one switching layer as we see in Figure 2. In this case the traffic comes in and is then redirected to another host which then passes the data back to the originating host.
This is an improvement, but the common perception that moving data between hosts (the green line in the diagram above) is east-west traffic. However, that’s not the case. Traffic can only travel along existing physical wires, and as such the traffic passes to the top-of-rack switch for redirection between hosts. This is still classified as north-south traffic in my lexicon and still doubles or triples the bandwidth required to implement the security measure.
GigaMon might help us here, but it is less than a full solution. GigaMon has a routing technology that redirects a series of requests to wherever they need to go very quickly. It is ideal for monitoring your environment, as you can easily pass network traffic to a network monitoring layer. Figure 3 is a sample implementation. Once there is more, traffic is often depicted as using the green path, but it really uses the orange path. All we’ve done here is add another need for bandwidth, a fourth path for our data. What GigaMon does is very useful in its own right, but it is not quite what we want. We need to reduce bandwidth requirements along all our wires.
Here is where micro-segmentation really comes into the for, as does tools like Illumio, and Cloud Passage. The workload itself does all the heavy firewall implementations, while using other controls to pass on the packets to yet another security device to capture, identify, etc.
The orange or monitoring traffic is still often shown as using the green lines where no wires actually exist. This is the ideal situation, but it only really works if the firewall does not require being an edge firewall that implements NAT, load balancing, and other security measures that need state information before sending on the traffic. For that, we need a new form of software defined networking: one that does not change flows after the security device but instead moves security devices and workloads to reduce the number of hairpins we need. Perhaps a method to have multiple edge firewalls (one per host) where the state is maintained between them and the state is updated based on the ultimate destination or flow. This possibility is shown in Figure 5.
In Figure 5, we use a SDN top-of-rack switch that works with whatever SDN is within our virtual environment to send workloads to the destination locations on the appropriate hosts. State for any firewall is passed via the same networking but is maintained across hosts. In this way, our security can stay on the system that hosts the destination server. This requires complete integration between physical and virtual networks. It also requires stateful security devices that can move state around and not traffic. State is usually a whole lot smaller in size than incoming and outgoing packets.
No matter how you look at SDN, once you introduce load balancing, security devices, and monitoring devices, you need to rethink your software defined network, or you will end up with a sadly defined network: one that could triple, quadruple, or 10x your network bandwidth requirements. Item #1 to understand is that your traffic ultimately only go where you have wires running. No wire, no traffic. Item #2 to understand is that adding security is not about just adding in a new device, but determining the impact of that new security device on your network, your bandwidth, and your systems.
As system engineers, we need to consider the impact of a device on the entire network architecture, and not just throw in security devices simply because we can. Architecture needs to be considered, and actual network testing is an essential part of building and maintaining an SDN.
Share this Article:
Latest posts by Edward Haletky (see all)
- Common Product Security Questions - November 23, 2016
- Sorry Support: Not Getting My Data - November 18, 2016
- Moving to the Future: Strategies for Handling Data Scale - November 14, 2016