Maestro Blog

Building effective communication is a keystone for arranging effective and transparent infrastructure management. The user needs to know a lot: from the status of the specific freshly run instance to global costs trends, security issues, and access requests. So, building a dialog actually needs several steps to be performed: Collecting the necessary data (collecting performance metrics, collecting data from integrated tools, building analytics) Establishing communication (deciding on the type of notifications to be sent, and their content) Building communication strategy (identifying the most effective way to deliver the content without spamming or overloading the users with the information). So how does Maestro face this? Collecting data is more of a technical question, which depends on the abilities of the in-built engines and integrated tools. Deciding on communication means is the next step which needs us to analyze the scope of the retrieved information, the recipient groups, and ...

Cloud usage patterns, as well as the request for the specific types of cloud-related expertise, develop in cycles. Initially, the core aim of the cloud management tools was to provide the possibility to perform the basic infrastructure management operations. The ability to create, change and remove resources was enough to meet the majority of technical and business tasks. Still, the more the cloud technology developed, the more complicated the infrastructures became. Additional services arose, bringing more value to the customers, and needing more effort to track them properly.   Does wisdom come extra effort? Eventually, effective infrastructure setup, analytics, management, and optimization turned out to be not a scope for a typical IT department expert, but a set of separate specializations within the Cloud-related community. To be able to perform these operations successfully, and to take best of the cloud-native offerings, you need to spend plenty of time and effort for respec...

During the recent years, the classic mathematics-based models used to create optimization recommendations have been empowered by Machine Learning mechanisms. This resulted in significant improvements in recommendations scope and accuracy of existing tools, as well as the creation of new ones. Having reviewed the ML-based rightsizing offerings by top cloud providers, the Cloud Competency team from Ukraine (Rostyslav Myronenko, Yevhen Nadin, Oleksandr Onsha, Bohdan Onsha) took the challenge to create their own POC. We claimed we would get the initial version of a cloud-agnostic ML-based rightsizing solution within the shortest terms – 2 weeks only. Look, what we got as a re sult! Background and Investigation Modern cloud management needs to cover a wide range of tasks and expectations from businesses and users of all kinds. Resource and cost optimization, effective performance, reliability, fault-tolerance are among them. Naturally, any cloud provider offers its own tools to help users f...