Chaos Engineering & Autonomous Optimization combined to maximize resilience to failure

This blog is co-authored by Kyle McMeekin, Head of Channel at Gremlin. Today’s enterprises are struggling to cope with the complexities of their environments, technologies, and applications. On top of these challenges, they face faster release rates, and the need

Akamas CMG Cloud top trends 2022
How to leverage ML-based optimization to balance Kubernetes performance, resilience and cost-efficie...

Stefano Doni (Akamas CTO) was one of the guest experts for the CMG virtual event on “Cloud’s top trends for 2022”. During his speech, Stefano describes how to address the challenge of ensuring that cloud-native applications get optimized in terms

Akamas Meetup IBM Research Kubernetes optimization
Using ML to automatically optimize Kubernetes for cost efficiency & reliability

Stefano Doni (CTO Akamas) presented his talk during the Meetup hosted by IBM Research for the Cloud Technology in the North, on March 14th. During his speech, Stefano covers key Kubernetes resource management concepts and demonstrates how machine learning techniques

Akamas Kubernets autoscaling blog
Beware of autoscaling if you want your Kubernetes services to be reliable and cost-efficient

Many companies delivering services based on applications running on cloud face much higher costs than expected. The problem is that over-provisioning is too often the approach taken to minimize risks, in particular when development and release cycles are getting shorter

Akamas Conf42 2021 Kubernetes optimization
Let the machines optimize the machines

Efficiency is one of Kubernetes’ top benefits, yet companies adopting Kubernetes often experience high infrastructure costs and performance issues, with applications failing to match latency SLOs. Even for experienced Performance Engineers and SREs, sizing of resource requests and limits to