Application availability is not just the measure of “being up”. Many apps can claim that status. Technically they are running and responding to requests, but at a rate which users would certainly interpret as being down. That’s because excessive load times can (and will be) interpreted as “not available.” That’s why it’s important to view ensuring application availability as requiring attention to all its composite parts: scalability, performance, and security.
The paradox begins when we consider how we ensure scale, performance, and security: monitoring and measuring. That is, we observe certain characteristics about the network, compute, and application resources to gain an understanding of the status of the application. That necessarily means we have to interact with those components that need monitoring and measuring and thus we enter the world of physics.
The Observer Effect simply states that observing something necessarily changes the thing being observed. When it’s a sentient being, this often takes the form of the Hawthorne Effect, which claims that sentient beings will change their behavior when they know they’re being observed. Go ahead, try it out on your kids. If they know they’re being watched they’re angels. But turn your back on them for a minute and wham! They’ve destroyed their play room and littered popcorn all over the floor.
Within the realm of IT, the effect is no less active:
In information technology, the observer effect is the potential impact of the act of observing a process output while the process is running. For example: if a process uses a log file to record its progress, the process could slow. Furthermore, the act of viewing the file while the process is running could cause an I/O error in the process, which could, in turn, cause it to stop.
Another example would be observing the performance of a CPU by running both the observed and observing programs on the same CPU, which will lead to inaccurate results because the observer program itself affects the CPU performance (modern, heavily cached and pipelined CPUs are particularly affected by this kind of observation).
The act of measuring capacity and performance of a system* – say an app or an individual microservice - alters its state by consuming resources that in turn increase total load which, based on operational axiom #2 says, ultimately degrades both capacity and performance. This is one of the reasons agent-based monitoring has always been a less favorable choice for APM, because the presence of the agent on the system necessarily reduces capacity and performance.
The Observer Effect is going to be particularly impactful on applications composed of microservices because of, well, math. If the act of measuring and monitoring one monolithic application degrades performance by X then the act of measuring and monitoring a microservices-based application is going to degrade performance by many more X. It could be argued that the impact on a microservices-based application is actually not X per service, but some fraction of X given that the point is to distribute services in such a way that not all services are being taxed at the same rate as in a monolithic application. That would be true if it the microservices were being used as part of a single application, but one of the benefits – and target uses – of microservices is reuse. That implies that multiple apps or APIs are going to make use of each service, thus increasing the need to measure and monitor the capacity and performance of each service.
This is where architecture and technique matters. Where the design and implementation of the measuring and monitoring for performance and load of microservices becomes an important piece of ensuring availability. While each and every point of control – an API gateway or service discovery system or load balancer or proxy - can measure each microservice for which it performs its assigned tasks, it is likely to unnecessarily increase the impact of the Observer Effect on the microservice. That’s because most points of control take an active approach to monitoring and measuring load and performance. That is, they purposefully poll a system so as to enquire regarding the status and responsiveness of the system. They use ICMP pings, they use TCP half opens, and they use HTTP content requests to gather the data they need.
Each of these methods interacts with the system in question and thus fulfills the Observers Effect prediction. The more systems gathering this data, the more interaction occurs, the greater the impact of the Observer Effect.
That means there must be greater attention paid to the way in which microservices are monitoring and measured – including the techniques used to accomplish it.
Passive approaches to measuring and monitoring provide one means of avoiding the Observer Effect. That’s because they – as the term implies – passively observe status and measure performance without actively probing systems for this data. This is typically achieved by leveraging intermediate systems like load balancers and proxies through which requests and responses necessarily flow to capture status information as it is passing through.
The measurements are then used by the intermediary, of course, to manage distribution of load but are also exposed via APIs for collection by other systems. Those statistics gathered from an intermediary are likely* to have no impact on performance because they are managed by a system separate from the real-time execution of the intermediary.
It is important to consider the availability of the statistics via APIs to external systems when architecting a solution based on passive monitoring and measurement techniques. If the system performing the monitoring and measuring makes available the data it has collected, it relieves other systems of needing to directly measure each services’ status and performance and further reduces the impact of the Observer Effect on the overall system.
This is one of the ways in which the collaborative aspects of DevOps can provide significant value. As ops and net ops work together to establish a more efficient means of measuring and monitoring the availability of systems like microservices they can provide as valuable input to dev those statistics using APIs directly or through integration with other established systems.
At an operational level this effort also establishes a more centralized location from which performance-related data can be retrieved (in real-time) and used to trigger other actions such as auto-scaling (up and down) – a critical capability when moving to microservices architectures in which the number and variability of usage and services requires a more automated approach to operations than their monolithic predecessors.
* This is less applicable to virtual network appliances because they are purposefully designed to separate operational and actionable systems to ensure that management – measuring, monitoring, modifying – the system does not impact the performance of the actionable system. This is carried over from their roots in hardware, where “lights out” management is a requirement.