Adaptive Enterprise Automation in the Cloud
Keywords:
Cloud Automation, Cloud Computing, Context-Aware Computing, Contextual Signals, Enterprise Workflows, Event Detection, Monitoring and Alerting, Proactive Decision Support, Signal-Aware Automation, Signal Processing, Virtualized Cloud Resources, Workflow Automation, Workflow Context, Workflow Execution.Abstract
The public cloud provides a scalable, cost-efficient platform for automatically provisioning, deploying, and executing enterprise workflows as virtualized resources. However, fully automated execution can overlook implicit enterprise context — the kind of contextual knowledge that decision-support experts encode as signals to enable adaptive, proactive, and better-controlled execution. Omitting this context weakens the reliability and success rate of cloud-based enterprise workflow execution.
To address this gap, we propose the Signal-Aware Cloud Automation Framework, which extends existing cloud automation frameworks with signal-processing and signal-detection modalities that supply contextual information to the executing workflow. The framework introduces the concept of contextual signals — signals derived directly from the building blocks of the executing workflow itself. This capability substantially improves enterprise workflow execution in cloud environments and enables a richer, more context-aware execution experience.
Enterprise workflows differ from conventional cloud-aware workflows in their reliance on specialized service requests. Their execution typically depends on an external signal-support infrastructure that surfaces implicit enterprise context, letting experts capture decision-support information — explicitly or implicitly, and often bound to time, duration, or other constraints — while also serving as a resource for managing both success and failure conditions. Even with these enhancements, execution remains unpredictable due to non-deterministic and implicit factors such as fluctuating workload or resource availability. Unlike other cloud-aware workflows, these factors cannot be monitored or detected through the workflow’s own sub-workflows and services. Instead, such uncertainties are better captured through contextual signals, which are transient by nature and represent enterprise-specific, higher-level monitoring-and-alerting conditions defined by non-experts.
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