Intel® Tiber™ App-Level Optimization’s agent automatically learns your application’s specific resource usage patterns and data flow.
By analyzing CPU scheduling order, oversubscribed locks, memory, network and disk access patterns, the agent identifies contended resources, bottlenecks and prioritization opportunities.
The agent tailors scheduling and prioritization decisions regarding CPU, locks, caches and memory accesses to improve an infrastructure’s application specific performance on Kubernetes and containerized environments, Big Data and stream processing, and custom applications.
App-Level Optimization unleashes your infrastructure’s performance, drastically improving the quality of service. The improved performance allows you to reduce cluster size and downsize your machines, which slashes compute costs.
Intel Tiber App-Level Optimization autonomously optimizes at the core, on the application level, to improve the efficiency of runtimes.
By ensuring that CPU isn’t wasted DevOps and Data Engineers can continuously achieve peak performance and lower compute costs.
Autonomous cloud optimization ensures lean and efficient runtimes, leading to enhanced performance.
Utilizing fewer resources and avoiding overprovisioning reduces response time and improves throughput.
Autonomous cloud optimization works to reduce the time it take for applications to respond, effectively reducing latency.
The continuous nature of Intel Tiber App-Level Optimization’s runtime optimization improves stability and the ability to handle peak traffic with less variance and more predictability.
By delivering better performance with smaller cluster size, fewer compute resources are needed, allowing organizations to lower their instance count.
When Intel Tiber App-Level Optimization’s runtime optimization benefits are used to eliminate cores, businesses are able to minimize their data center footprint and save 20-45% on compute costs.
“We deployed (App-Level Optimization) on Spark jobs using GCP Dataproc services and saw results upon activation. Our global time to completion improved, which led us to cost savings without infrastructure efforts or operational tuning of the cluster”.
Senior Data Engineer
