01/14/2021 | News release | Distributed by Public on 01/14/2021 06:05
Serving more than approximately25 million Internet properties is not an easy thing, and neither is serving 20 million requests per second on average. At Cloudflare, we achieve this by running a homogeneous edge environment: almost every Cloudflare server runs all Cloudflare products.Figure 1. Typical Cloudflare service model: when an end-user (a browser/mobile/etc) visits an origin (a Cloudflare customer), traffic is routed via the Internet to the Cloudflare edge network, and Cloudflare communicates with the origin servers from that point.
As we offer more and more products and enjoy the benefit of horizontal scalability, our edge stack continues to grow in complexity. Originally, we only operated at the application layer with our CDN service and DoS protection. Then we launched transport layer products, such as Spectrum and Argo. Now we have further expanded our footprint into the IP layer and physical link with Magic Transit. They all run on every machine we have. The work of our engineers enables our products to evolve at a fast pace, and to serve our customers better.
However, such software complexity presents a sheer challenge to operation: the more changes you make, the more likely it is that something is going to break. And we don't tolerate any of our mistakes slipping into the production environment and affecting our customers.
In this article, we will discuss one of the techniques we use to fight such software complexity: simulations. Simulations are basically system tests that run with synthesized customer traffic and applications. We would like to introduce our simulation system, SOAR, i.e. Simulation for Observability, reliAbility, and secuRity.
What is SOAR? Simply put, it's a data center built specifically for simulations. It runs the same software stack as our production data centers, but without any production traffic. Within SOAR, there are end-user servers, product servers, and origin servers (Figure 2). The product servers behave exactly the same as servers in our production edge network, and they are the targets that we want to test. End-user servers and origin servers run applications that try to simulate customer behaviors. The simplest case is to run network benchmarks through product servers, in order to evaluate how effective the corresponding products are. Instead of sending test traffic over the Internet, everything happens in the LAN environment that Cloudflare tightly controls. This gives us great flexibility in testing network features such as bring-your-own-IP (BYOIP) products.Figure 2. SOAR architectural view: by simulating the end users and origin on Cloudflare servers in the same VLAN, we can focus on examining the problems occurring in our edge network.
To demonstrate how this works, let's go through a running example using Magic Transit.
Magic Transit is a product that provides IP layer protection and acceleration. One of the main functions of Magic Transit is to shield customers from DDoS attacks.Figure 3. Magic Transit workflow in a nutshell
Customers bring their IP ranges to advertise from Cloudflare edge. When attackers initiate a DoS attack, Cloudflare absorbs all the customer's traffic, drops the attack traffic, and encapsulates clean traffic to customers. For this product, operational concerns are multifold, and here are some examples:
To ease these concerns, we run simulated customers with SOAR. Yes, simulated, not real. For example, assume a customer Alice onboarded an IP range 192.0.2.0/24 to Magic Transit. To simulate this customer, in SOAR we can configure a test application (e.g. iperf server) on one origin server to represent Alice's service. We also bring up a product server to run Magic Transit. This product server will filter traffic toward a.b.c.0/24, and GRE encapsulated cleansed traffic to Alice's specified GRE endpoint. To make it work, we also add routing rules to forward packets destined to 192.0.2.0/24 to go through the product server above. Similarly, we add routing rules to deliver GRE packets from the product server to the origin servers. Lastly, we start running test clients as eyeballs to evaluate the functional correctness, performance metrics, and resource usage.
For the rest of this article, we will talk about the design and implementation of this simulation system, as well as several real cases in which it helped us catch problems early or avoid problems altogether.
Before we created SOAR, we had already built a 'performance simulation' for our layer 7 services. It is based on SaltStack, our configuration management software. All the simulation cases are system test cases against Cloudflare-owned HTTP sites. These cases are statically configured and run non-stop. Each simulation case produces multiple Prometheus metrics such as requests per second and latency. We monitor these metrics daily on our Grafana dashboard.
While this simulation system is very useful, it becomes less efficient as we have more and more simulation cases and products to run and analyze.
As more types of simulations are onboarded, it is critical to ensure each simulation runs in a clean environment, and all tasks of a simulation run together. This challenge is specific to providers like Cloudflare, whose products are not virtualized because we want to maximize our edge performance. As a result, we have to isolate simulations and clean up by ourselves; otherwise, different simulations may cross-affect each other.
For example, for Magic Transit simulations, we need to create a GRE tunnel on an origin server and set up several routes on all three servers, to make sure simulated traffic can flow as real Magic Transit customers would. We cannot leave these routes after the simulation finishes, or there might be a conflict. We once ran into a situation where different simulations required different source IP addresses to the same destination. In our original performance simulation environment, we will have to modify simulation applications to avoid these conflicts. This approach is less desirable as different engineering teams have to pay attention to other teams' code.
Moreover, the performance simulation addresses only the most basic system test cases: a client sends traffic to a server and measures the performance characteristics like request per second and latency quantile. But the situation we want to simulate and validate in our production environment can be far more complex.
In our previous example of Magic Transit, customers can configure complicated network topology. Figure 4 is one simplified case. Let's say Alice establishes four GRE tunnels with Cloudflare; two connect to her data center 1, and traffic will be ECMP hashed between these two tunnels. Similarly, she also establishes another two tunnels to her data center 2 with similar ECMP settings. She would like to see traffic hit data center 1 normally and fail over to data center 2 when tunnels 1 and 2 are both down.Figure 4. The customer configured Magic Transit to establish four tunnels to her two data centers. Traffic to data center 1 is hashed between tunnel 1 and 2 using ECMP, and traffic to data center 2 is hashed between tunnel 3 and 4. Data center 1 is the primary one, and traffic should failover to data center 2 if tunnels 1 and 2 are both down. Note the number '2' is purely symbolic, as real customers can have more than just 2 data centers, or 2 paths per ECMP route.
In order to examine the effectiveness of route failover, we would need to inject errors on the product servers only after the traffic on the eyeball server has started. But this type of coordination is not achievable with statically defined simulations.
Our performance simulation is not engineer-friendly. Not just because it is all statically configured in SaltStack (most engineering teams do not possess Salt expertise), but it is also not integrated with an engineer's daily routine. Can engineers trigger a simulation on every branch build? Can simulation results get back in time to inform that a performance problem occurs? The answer is no, it is not possible with our static configuration. An engineer can submit a Salt PR to config a new simulation, but this simulation may have to wait for several hours because all other unfinished simulations need to complete first (recall it is just a static loop). Nor can an engineer add a test to the team's repository to run on every build, as it needs to reside in the SRE-managed Salt repository, making it unmanageable as the number of simulations grows.
To address the above limitations, we designed SOAR.Figure 5. The Architecture of SOAR
The architecture is a performance simulation structure, extended. We created an internal coordinator service to:
The coordinator is secured by Cloudflare Access, so that only employees can visit this service. The coordinator will serve two types of simulations: one-time simulation to be run in an ad-hoc way and mainly on a per pull request manner, to ease development testing. It's also callable from our CI system. Another type is repetitive simulations that are stored in the coordinator's persistent storage. These simulations serve daily monitoring purposes and will be executed periodically.
Each simulation server runs a simulation agent. This agent will execute two types of tasks received from the coordinator: system tasks and user tasks. System tasks change the system-wide configurations and will be reverted after each simulation terminates. These will include but are not limited to route change, link change, address change, ipset change and iptables change.
User tasks, on the other hand, run benchmarks that we are interested in evaluating, and will be terminated if it exceeds an allocated execution budget. Each user task is isolated in a cgroup, and the agent will ensure all user tasks are executed with dedicated resources. The generic runtime metrics of user tasks is monitored by Cadvisor and sent to Prometheus and Alert Manager. A user task can export its own metrics to Prometheus as well.
For SOAR to run reliably, we provisioned a dedicated environment that enforces the same settings for the production environment and operates it as a production system: hardened security, standard alerts on watch, no engineer access except approved tools. This to a large extent allows us to run simulations as a stable source of anomaly detection.
An important ability of SOAR is to simulate for a specific customer. This will provide the customer with more guarantees that both their configurations and our services are battle-tested with traffic before they go live. It can also be used to bisect problems during a customer escalation, helping customer support to rule out unrelated factors more easily.
All of our edge servers know how to dispatch an incoming customer packet. This factor greatly reduces difficulties in simulating a specific customer. What we need to do in simulation is to mock routing and domain translation on simulated eyeballs and origins, so that they will correctly send traffic to designated product servers. And the problem is solved-magic!
The actual implementation is also straightforward: as simulations run in a LAN environment, we have tight control over how to route packets (servers are on the same broadcast domain). Any eyeball, origin, or product server can just use a direct routing rule and a static DNS entry in /etc/hosts to redirect packets to go to the correct destination.
Running a simulation this way allows us to separate customer configuration management from the simulation service: our products will manage it, so any time a customer configuration is changed, they will already reflect in simulations without special care.
All SOAR components are built with Golang from scratch on Linux servers. It took three engineer-months to build the prototype and onboard the first engineering use case. While there are other mature platforms for job scheduling, task isolation, and monitoring, building our own allows us to better absorb new requirements from engineering teams, which is much easier and quicker than an external dependency.
In addition, we are currently integrating the simulation service into our release pipeline. Cloudflare built a release manager internally to schedule product version changes in controlled steps: a new product is first deployed into dogfooding data centers. After the product has been trialed by Cloudflare employees, it moves to several canary data centers with limited customer traffic. If nothing bad happens, in an hour or so, it starts to land in larger data centers spread across three tiers. Tier-3 will receive the changes an hour earlier than tier-2, and the same applies to tier-2 and tier-1. This ensures a product would be battle-tested enough before it can serve the majority of Cloudflare customers.
Now we move this further by adding a simulation step even before dogfooding. In this step, all changes are deployed into the simulation environment, and engineering teams will configure which simulations to run. Dogfooding starts only when there is no performance regression or functional breakage. Here performance regression is based on Prometheus metrics, where each engineering team can define their own Prometheus query to interpret the performance results. All configured simulations will run periodically to detect problems in releases that do not tie to a specific product, e.g. a Linux kernel upgrade. SREs receive notifications asynchronously if any issue is discovered.
Simulations are very useful inside Cloudflare. Let's see some real experiences we had in the past.
In our Magic Transit example, the engineering team was about to release physical network interconnect (PNI) support. With PNI, customer data centers physically peer with Cloudflare routers.Figure 6. The Magic Transit service flow for a customer without PNI support. Any Cloudflare data center can receive eyeball traffic. After mitigating a DoS attack traffic, valid traffic is encapsulated to the customer data center from any of the handling Cloudflare data centers.Figure 7. Magic Transit with PNI support. Traffic received from any data center will be moved to the PNI data center that the customer connects to. The PNI data center becomes a choke point.
However, this PNI functionality introduces a problem in our normal release process. However, PNI data centers are typically different from our dogfooding and canary data centers. If we still release with the normal process, then two critical phases are skipped. And what's worse, the PNI data center could be a choke point in front of that customer's traffic. If the PNI data center is taken down, no other data center can replace its role.
SOAR in this case is an important utility to help. The basic idea is to configure a server with PNI information. This server will act as if it runs in a PNI data center. Then we run simulated eyeball and origin to examine if there is any functional breakage:Figure 8. SOAR configures a server with PNI information and runs simulated eyeball and origin on this server. If a PNI related code release has a problem, then with proper simulation traffic it will be caught before rolling into production.
With such simulation capability, we were able to detect several problems early on and before releasing. For example, we caught a problem that impacts checksum offloading, which could encapsulate TCP packets with the wrong inner checksum and cause the packets to be dropped at the origin side. This problem does not exist in our virtualized testing environment and integration tests; it only happens when production hardware comes into play. We then use this simulation as a success indicator to test various fixes until we get the packet flow running normally again.
When a team configures a simulation, it runs on the same stack where all other teams run their products as well. This means when a simulation starts to show unhealthy results, it may or may not directly relate to the product associated with that simulation.
But with continuous simulations, we will have more chances to detect issues before things go south, or at least it will serve as a hint to quickly react to emerging problems. In an example early this year, we noticed one of our performance simulation dashboards showed that some HTTP request throughput was dropping by 20%. After digging into the case, we found our bot detection system had made a change that affected related requests. Luckily enough we moved fast thanks to the hint from the simulation (and some other useful tools like Opentracing).
Our recent enhancement from just HTTP performance simulation to SOAR makes it even more useful for customers. This is because we are now able to simulate with customer-specific configurations, so we might expose customer-specific problems. We are still dogfooding this, and hopefully, we can deploy it to our customers soon.
When we started to develop Magic Transit, a question worth monitoring was how effective our mitigation pipeline is, and how to apply thresholds for different customers. For our new ACK flood mitigation system, flowtrackd, we onboarded its performance simulation cases together with tunable ACK flood. Combined with customer-specific configuration, this allows us to compare the throughput result under different volumes of attacks, and systematically tune our mitigation threshold.
Another important factor that we will be able to achieve with our 'attack simulation' system is to mount attacks we have seen in the past, making sure the development of our mitigation pipelines won't ever pass on these known attacks to our customers.
In this article, we introduced Cloudflare's simulation system, SOAR. While simulation is not a new tool, we can use it to improve reliability, observability, and security. Our adoption of SOAR is still in its early stages, but we are pretty confident that, by fully leveraging simulations, we will push our quality of service to a new level.