Intelligent Resource Optimization with ActiveBatch

In our fast-paced world, organizations require flexible IT infrastructures that can quickly adapt to dynamic, real-time demands. With ActiveBatch, users can easily optimize the distribution of workloads to improve the likelihood of on-time, successful job completions while reducing idle machines by leveraging machine learning and predictive analysis. Users can assign custom Dynamic Queue Characteristics, instructing ActiveBatch to evaluate multiple servers before submitting Jobs to the servers on which they will run. For example, execution machines can be monitored and evaluated at runtime for characteristics like available disk space, registry values, or the presence or absence of a particular application. Dynamic Queue Characteristics are useful because it is difficult, time-consuming, and tedious for IT personnel to manually search hundreds (or thousands) of servers with specific characteristics to successfully run tasks.