All you need to know about Akamas and Autonomous Performance Optimization. What is it? Why do you need it? What’s the role of AI within Akamas?
Autonomous Performance Optimization is a new approach to application and IT systems optimization that leverages AI and machine learning techniques to automatically and continuously optimize the technology stack, delivering unprecedented application performance, deployment agility, and cost savings.
The complexity of on-prem and cloud software configurations and continuous integration processes have grown beyond the reach of even the most skilled performance experts. Software gets built faster and faster, technology platforms are complex with hundreds of possible configurations. Cloud vendors offer a growing array of configuration and tuning options. Traditional manual performance tuning is ineffective at this scale, which means organizations are leaving significant performance and cost savings on the table.
Akamas is technology-agnostic and optimizes many market-leading technologies thanks to its Optimization Pack library. Supported technologies include cloud services, big data, databases, OS, containers, and application runtimes. Custom application settings can be easily optimized too, thanks to Akamas flexible AI-driven optimizer and powerful automation capabilities. A full list of currently-released Akamas Optimization Packs can be found here.
Akamas can be tasked with a variety of high-level performance and cost optimization goals. For example, you can ask it to maximize application business KPIs such as payments/second, minimize a database query latency, or decrease the cloud cost of your containers. Akamas routinely generates results in the range of 20-70% improvements, with peaks up to 2x to 4x.
Akamas automated and continuous optimization approach is designed to fit nicely into modern CI/CD pipelines. By adding an Akamas optimization step in the pipeline, just before the production deployment step, developers can ensure optimal application performance in production.
Akamas proprietary AI was designed to converge on optimal configurations faster and more precisely than with any alternative approach, through automatic and iterative experiments. The actual optimization time generally correlates with the complexity of the optimization space (e.g. number of parameters). Akamas is continuously searching for the best configuration for a given goal and is typically able to converge towards optimal configurations in 12 to 24 hours.
Akamas uses AI to navigate the vast configuration space of today’s tech stacks and quickly find optimal settings. Unlike current AIOps solutions, Akamas does not simply suggest configuration changes, but instead, it autonomously applies them to target systems and evaluates how well they perform towards achieving the optimization goals you set. In that respect, Akamas is a true closed-loop, autonomous optimization system.
No. Competing optimization solutions use simple performance models, based on past resource usage, to suggest changes. This approach does not work with the configuration optimization problem, as the effect of parameter changes on application performance is unpredictable. Akamas solves the problem by testing the configurations on real systems and measuring the resulting performance. This makes it possible to identify the best configuration for each specific application stack and workload.
No. Code-level optimizations can provide significant benefits, but they are manually intensive and lengthy. We recommend starting optimizing technology stack configurations first, as they take a fraction of the time and provide immediate and significant improvements. However, being a full-stack solution, Akamas can also optimize application-level settings, provided that they can be changed via API calls or configuration files, for example.
Akamas requires three integration points:
No. There are no Akamas agents that need to be installed to start optimizing. Akamas provides several strategies, for example via API calls and SSH, to integrate with the existing configuration management, load testing, CI/CD, and monitoring solutions you already have in place.
