Defining optimization goal & constraints
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The first fundamental step in creating a study is to define the study . While this step might be perceived as somewhat straightforward (e.g. constraints could be simply translated from SLOs already in place), defining the optimization goal really requires carefully balancing complexity and effectiveness, also as part of the general (iterative) optimization process. Please also read the section here below.
In general, any performance engineering, tuning, and optimization activity involves complex tradeoffs among different - and potentially conflicting - goals and system performance metrics, such as:
Maximizing the business volume an application can support, while not making the single transaction slower or increasing errors above a desired threshold
Minimizing the duration of a batch processing task, while not increasing the cloud costs by more than 20% or using more than 8 CPUs
Akamas support all these (and other) scenarios by means of the optimization goal, that is the single metric or the formula combining multiple metrics that have to be either minimized or maximized, and one or more constraints among metrics of the system.
In general, constraints can be defined as either absolute constraints (e.g. app.response_time < 200 ms) or as relative constraints with respect to a baseline (e.g. app_response_time < +20% of the baseline), that is the current configuration in place, typically corresponding to the very first experiment in an offline optimization study which. Therefore, relative constraints are only applicable to offline optimization studies, while absolute constraints are applicable to both absolute and relative constraints.
Please notice that when defining constraints for an optimization study, it is required to also include those constraints listed in the Constraints section of the respective Optimization Packs which express internal constraints among parameters. For example, in case OpenJDK 11 components are to be tuned, the reference section is .
The page of the in the reference guide describes the corresponding structures. For offline optimization studies only, the Akamas UI allows the optimization goal and constraints to be defined as part of the visual procedure activated by the "Create a study" button (see the following figure).
Please notice that any experiment that does not respect the constraints is marked by Akamas as failed, even if correctly executed. The reason for this failure can be inspected in the experiment status. Similarly to workflow failures (see below), the Akamas AI engine automatically takes any failure due to constraint violations into account when searching the optimization space to identify the parameter configurations that might improve the goal metrics while matching constraints.
There are no general guidelines and best practices on how to best define goals & constraints, as this is where experience, knowledge, and processes meet.
Please refer to the section for a number of examples related to a variety of technologies and the guide for real-world examples.
