Introduction to Akamas
A quick introduction to Akamas
Akamas is the AI-powered optimization platform designed to maximize service quality and cost efficiency without compromising on application performance. Akamas supports both production environments under live, dynamic workloads, and in test/pre-production environments against any what-if scenario and workload.
Thanks to Akamas, performance engineers, DevOps, CloudOps, FinOps and SRE teams can keep complex applications, such as Kubernetes microservices applications, optimized to avoid any unnecessary cost and any performance risks.
The Akamas optimization platform leverages patented AI techniques that can autonomously identify optimal full-stack configurations driven by any custom-defined goals and constraints (SLOs), without any human intervention, any agents, and any code or byte-code changes.
Akamas high-level architecture
Akamas optimal configurations can be applied either i) under human approval (human-in-the-loop mode) or ii) automatically, as a continuous optimization step in a CI/CD pipeline (in-the-pipe) or iii) autonomously by Akamas (autopilot).
Akamas can optimize any system with respect to any set of parameters chosen from the application, middleware, database, cloud, and any other underlying layers.
Akamas provides dozens of out-of-the-box Optimization Packs available for key technologies such as JVM, Go, Kubernetes, Docker, Oracle, MongoDB, ElasticSearch, PostgreSQL, Spark, AWS EC2 and Lambda, and more. Optimization Pack provides parameters, relationships, and metrics to accelerate the optimization process setup and support company-wide best practices. Custom Optimization Packs can be easily created without any coding.
The following figure is illustrative of Akamas coverage for both managed technologies and integrated components of the ecosystem.
Akamas optimizes any system and integrates with any ecosystem
Akamas can integrate with any ecosystem thanks to out-of-the-box and custom integrations with the following components:
- telemetry & monitoring tools and other sources of KPIs and cost data, such as Dynatrace, Prometheus, CloudWatch, and CSV files
- configuration management tools, repositories and interfaces to apply configurations, such as Ansible, Openshift, and Git
- value stream delivery tools to support a continuous optimization process, such as Jenkins, Dynatrace Cloud Automation, and GitLab
- load testing tools to generate simulated workloads in test/pre-production, such as LoadRunner, NeoLoad, and JMeter
Akamas has been designed around Infrastructure-as-Code (IaC) and DevOps principles. Thanks to a comprehensive set of APIs and integration mechanisms, it is possible to extend the Akamas optimization platform to manage any system and integrate with any ecosystem.
Akamas optimization platform supports a variety of use cases, including:
- Improve Service Quality: optimize application performance (e.g. maximize throughput, minimize response time and job execution time) and stability (lower fluctuations and peaks);
- Increase Business Agility: identify resource bottlenecks in early stages of the delivery cycle, avoid delays due to manual remediations - release higher quality services and reduce production incidents;
- Increase Service Resilience: improve service resilience under higher workloads (e.g. expected business growth) or failure scenarios identified by chaos engineering practices - improve SRE practice;
- Reduce IT Cost / Cloud Bill: reduce on-premise infrastructure cost and cloud bills due to resource over-provisioning - improve cost efficiency of Kubernetes microservices applications;
- Optimize Cloud Migration: safely migrate on-premise applications to cloud environments for optimal cost efficiency evaluate options to migrate to managed services (e.g. AWS Fargate);
- Improve Operational Efficiency: save engineering time spent on manual tuning tasks and enable Performance Engineering teams to do more in less time (and with less external consulting).
Akamas key use cases