13 docs tagged with "BEP"

With the introduction of the BEP execution platform:

With the introduction of the BEP execution platform:

A Worker is a single instance of a Worker application process started by BEP to process BEP Worker tasks.

To plan capacity means to ensure the availability of a cost-effective capacity that meets or exceeds the business needs as established in Service Level Agreements (SLAs) at any given time. Within the context, capacity is defined as the maximum load the Work.AI platform can handle.

The figure below illustrates the BEP Worker task distribution flow within BEP:

This article provides recommendations on how to adjust AutoML Worker parameters, such as CPU and memory consumption, for better cluster utilization and throughput.

The Worker scaling strategy is a mechanism for calculating an optimal Worker quantity to satisfy, to the maximum possible extent, the multiple task execution requirements on a cluster with fixed limited resources (non-elastic cluster). The strategy is supposed to adapt when cluster resources are added or removed.

Bot Execution Platform (BEP) is a platform allowing you to execute large volumes of tasks on a cluster in a scalable and fault-tolerant manner.

The Java native mechanism enables you to write and deploy custom pure Java-based Workers with any libraries without additional loading or linking.

The main goal of AutoML and BEP integration is to use the BEP platform for the following:

The Worker management system (WMS) starts and stops BEP Workers, as well as scales them up or down based on received streams of BEP Worker tasks queued from client apps.

To support cases with a non-constant load of available cluster resources and allow reducing TCO, you require elastic cluster scaling. Virtual machines (VM) in the cluster are added on demand and reduced when there is no load and they are no longer needed.

BEP Worker framework