SLA Monitor View Improves Insight into SLA Workflows
ActiveBatch's SLA Monitor helps manage and monitor business expectations by providing insight into the execution and completion of workflows. A real-time view that displays both text and visual representations of relationships in your IT environment, SLA Monitor helps operations teams track critical jobs and processes.
SLA Monitor alerts operations as early as possible if the system detects that a critical SLA workflow may run late or breach and can run automated remediation actions and alert operators, allowing them to take action and make adjustments to ensure timely and successful workflows.
Dynamic Event Alerting
Users can choose from a series of alert notifications to notify users of an impending SLA breach and select the appropriate action. For example, users can set various custom alert thresholds —such as warning, critical, or take action— that notify users of a potential workflow breach, providing the opportunity to take appropriate action to reduce the risk of a breach.
Improve Performance Time
ActiveBatch highlights SLA-dependent workflows and can act to prevent them from breaching by doing things such as:
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Building a priority fence around the SLA-marked job(s) to allow only SLA-sensitive jobs to proceed for execution
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Increasing the OS priority of executing workflows when the workflows exceed a defined percentage of their expected/historical runtimes
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Allocating additional computing resources to the SLA job(s)
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Increasing the queue priority for the SLA-marked job(s), meaning that any SLA-tied workflows will receive critical path priority over other workflows within the queue
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Using Dynamic, User-Defined Queue Characteristics to identify which execution queues best fit the needs of SLA-dependent workflows
ActiveBatch also offers capabilities such as automated server provisioning, reporting and dashboard capabilities, scheduling analytics, alerting and monitoring, and more. With these capabilities and others, developers can set SLA policies for workflows and effectively manage and monitor physical, virtual, and cloud-based resources, ensuring the timely execution of workflows and processes. Users can also apply machine learning to SLA workloads, using Heuristic Queue Allocation to continuously improve performance through optimized allocation of job loads.