Energy-Efficient Workflow Scheduling in Hybrid Azure Cloud

Use Case: Hospitals, research institutions, and enterprises that run large-scale scientific workflows (e.g., genome sequencing, AI model training, financial risk simulations) need to optimize energy usage and reduce carbon footprint while still meeting deadlines and performance SLAs.

Minimize energy consumption of workflow execution in a hybrid Azure cloud (public + on-premises).

Balance workload distribution across edge, private, and Azure public cloud resources.

Ensure SLA compliance while lowering operational costs and environmental impact.

This project proposes an energy-aware workflow scheduling system that uses predictive analytics and intelligent scheduling algorithms to allocate workflow tasks between on-premises private cloud and Azure public cloud.
Workflows are decomposed into tasks.

Scheduler predicts task resource demand and energy cost.

Tasks are mapped to the most energy-efficient infrastructure (edge, private DC, or Azure).

Uses auto-scaling, monitoring, and ML models to continuously adapt to workload changes.

Results: Reduced energy usage, cost savings, SLA compliance, and sustainability.

Azure Service	Purpose in Project
Azure Kubernetes Service (AKS)	Runs workflow containers, supports auto-scaling to optimize energy usage.
Azure Batch	Schedules and executes large-scale workflow jobs with energy-aware policies.
Azure Machine Learning	Predicts task runtime, resource demand, and energy consumption.
Azure Monitor + Application Insights	Tracks energy usage, performance, and SLA compliance in real time.
Azure Arc	Extends hybrid workflow management to on-premises/private cloud resources.
Azure Logic Apps	Automates scheduling decisions and task orchestration workflows.
Azure Cost Management + Advisor	Monitors energy/cost efficiency and recommends optimizations.
Power BI	Visualizes workflow performance, energy savings, and carbon footprint.

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