The Overhead of Software Tunneling

[This post was written with Jesse Gross, Ben Basler, Bruce Davie, and Andrew Lambeth]

Tunneling has earned a bad name over the years in networking circles.

Much of the problem is historical. When a new tunneling mode is introduced in a hardware device, it is often implemented in the slow path. And once it is pushed down to the fastpath, implementations are often encumbered by key or table limits, or sometimes throughput is halved due to additional lookups.

However, none of these problems are intrinsic to tunneling. At its most basic, a tunnel is a handful of additional bits that need to be slapped onto outgoing packets. Rarely, outside of encryption, is there significant per-packet computation required by a tunnel. The transmission delay of the tunnel header is insignificant, and the impact on throughput is – or should be – similarly minor.

In fact, our experience implementing multiple tunneling protocols within Open vSwitch is that it is possible to do tunneling in software with performance and overhead comparable to non encapsulated traffic, and to support hundreds of thousands of tunnel end points.

Given the growing importance of tunneling in virtual networking (as evidenced by the emergence of protocols such as STT, NVGRE, and VXLAN, it’s worth exploring its performance implications.

And that is the goal of this post: to start the discussion on the performance of tunneling in software from the network edge.


An emerging method of network virtualization is to use tunneling from the edges to decoupled the virtual network address space from the physical address space. Often the tunneling is done in software in the hypervisor. Tunneling from within the server has a number of advantages: software tunneling can easily support hundreds of thousands of tunnels, it is not sensitive to key sizes, it can support complex lookup functions and header manipulations, it simplifies the server/switch interface and reduces demands on the in-network switching ASICs, and it naturally offers software flexibility and a rapid development cycle.

An idealized forwarding path is shown in the figure below. We assume that the tunnels are terminated within the hypervisor. The hypervisor is responsible for mapping packets from VIFs to tunnels, and from tunnels to VIFs. The hypervisor is also responsible for the forwarding decision on the outer header (mapping the encapsulated packet to the next physical hop).

Some Performance Numbers for Software Tunneling

The following tests show throughput and cpu overhead for tunneling within Open vSwitch.   Traffic was generated with netperf  attempting to emulate a high-bandwidth TCP flow. The MTU for the VM and the physical NICs are 1500bytes and the packet payload size is 32k. The test shows results using no tunneling (OVS bridge), GRE, and STT.

The results show aggregate bidirectional throughput, meaning that 20Gbps is a 10G NIC sending and receiving at line rate. All tests where done using Ubuntu 12.04 and KVM on an Intel Xeon 2.40GHz servers interconnected with a Dell 10G switch. We use standard 10G Broadcom NICs. CPU numbers reflect the percentage of a single core used for each of the processes tracked.

The following results show the performance of a single flow between two VMs on different hypervisors. We include the Linux bridge to show that performance is comparable. Note that the CPU only includes the CPU dedicated to switching in the hypervisor and not the overhead in the guest needed to push/consume traffic.

Throughput Recv side cpu Send side cpu
Linux Bridge: 9.3 Gbps 85% 75%
OVS Bridge: 9.4 Gbps 82% 70%
OVS-STT: 9.5 Gbps 70% 70%
OVS-GRE: 2.3 Gbps 75% 97%

This next table shows the aggregate throughput of two hypervisors with 4 VMs each. Since each side is doing both send and receive, we don’t differentiate between the two.

Throughput CPU
OVS Bridge: 18.4 Gbps 150%
OVS-STT: 18.5 Gbps 120%
OVS-GRE: 2.3 Gbps 150%

Interpreting the Results

Clearly these results (aside from GRE, discussed below) indicate that the overhead of software for tunneling is negligible. It’s easy enough to see why that is so. Tunneling requires copying the tunnel bits onto the header, an extra lookup (at least on receive), and the transmission delay of those extra bits when placing the packet on the wire. When compared to all of the other work that needs to be done during the domain crossing between the guest and the hypervisor, the overhead really is negligible.

In fact, with the right tunneling protocol, the performance is roughly equivalent to non-tunneling, and CPU overhead can even be lower.

STT’s lower CPU usage than non-tunneled traffic is not a statistical anomaly but is actually a property of the protocol. The primary reason is that STT allows for better coalescing on the received side in the common case (since we know how many packets are outstanding). However, the point of this post is not to argue that STT is better than other tunneling protocols, just that if implemented correctly, tunneling can have comparable performance to non-tunneled traffic. We’ll address performance specific aspects of STT relative to other protocols in a future post.

The reason that GRE numbers are so low is that with the GRE outer header it is not possible to take advantage of offload features on most existing NICs (we have discussed this problem in more detail before). However, this is a shortcoming of the NIC hardware in the near term. Next generation NICs will support better tunnel offloads, and in a couple of years, we’ll start to see them show up in LOM.

In the meantime, STT should work on any standard NIC with TSO today.

The Point

The point of this post is that at the edge, in software, tunneling overhead is comparable to raw forwarding, and under some conditions it is even beneficial. For virtualized workloads, the overhead of software forwarding is in the noise when compared to all of the other machinations performed by the hypervisor.

Technologies like passthrough are unlikely to have a significant impact on throughput, but they will save CPU cycles on the server. However, that savings comes at a fairly steep cost as we have explained before, and doesn’t play out in most deployment environments.


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