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 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 Dynatrace blog post Kubernetes
Optimizing your Kubernetes clusters without breaking the bank

Organizations across the world are fast adopting Kubernetes. That is because Kubernetes provides several benefits from a performance perspective. Its ability to densely schedule containers into the underlying machines translates to low infrastructure costs. It prevents a runaway container from impacting

Akamas Kubernetes optimization SLOs
How I learned to love Kubernetes and stop worrying about costs and SLOs

The benefits of Kubernetes from a performance perspective are undisputable. Let’s just consider the efficiency provided by Kubernetes, thanks to its ability to densely schedule containers into the underlying machines, which translates to low infrastructure costs. Or the mechanisms available to isolate

Akamas safely cut cloud bills blog
Safely cut cloud bills with no impact on service performance – is it a dream?

Several cloud cost optimization solutions are today available both by Cloud Providers, such as AWS Compute Optimizer or Google machine type recommendations, and by specialized COTS vendors. These tools may help you choosing the right cloud instance and volume sizes, allocating resources in

Akamas faster spark jobs lower AWS costs blog
Wouldn’t it be nice to get faster Spark jobs and lower AWS costs?

Big data applications often offer relevant opportunities for gains both in terms of performance and of cost reduction. Typically, the underlying infrastructure – whether on-premise or on cloud – is both inefficient and over-provisioned to ensure a good performance vs

Akamas JVM tuning optimization choose the right GC
Make sure to choose the right GC for your application to achieve maximum performance

The Java platform continues to be developed and improved over time. The OpenJDK community has been quite active in improving the performance of the JVM and the garbage collector (GC): new GCs are being developed and existing ones are constantly

AKamas Java tuning short blanket blog
Java application throughput, response time, and cost. Is there a short blanket?

Developers and system owners usually take for granted that there are some intrinsic tradeoffs in the Java design. For example, it is commonly accepted that if you aim at reducing resource usage (e.g. CPU), you must accept some performance degradation.

Beware: improving GC performance may lead to slower applications

One of the most established approaches to improve Java application performance is to tune JVM options, in particular Garbage Collector (GC) parameters.  Indeed, the main task of garbage collection is to free memory up which requires stopping the application threads