In cloud computing, flexibility and scalability are king. But with great power comes great responsibility... to your budget. Many organizations find themselves with a hefty cloud bill, often due to virtual machines (VMs) running 24/7, even when they're not actively in use. This is where the intelligent combination of process automation and AI can do wonders for your processes.

Imagine an organization, let's call them "InnovateTech," grappling with this exact challenge. They had a significant footprint in the cloud, utilizing numerous VMs for development, testing, and various business applications. While the cloud offered agility, the always-on nature of many of their VMs was leading to considerable wasted expenditure.

InnovateTech knew they needed a solution that went beyond manual monitoring or simple scheduled shutdowns. They sought a way to dynamically manage their VM instances, ensuring they were available when needed and powered down when not, all while minimizing human intervention. Their answer comes in the form of business process automation, supercharged with AI.

The Foundation: How FlowWright Automates VM Lifecycle Management

InnovateTech started by implementing FlowWright to orchestrate the start and stop processes of their VMs. This was a critical first step. Instead of developers or IT staff manually logging into cloud consoles to power down machines at the end of the day and power them up in the morning, FlowWright took over.

Here's how they initially configured it:

1. Scheduled Shutdowns: Our workflows were designed to identify VMs associated with specific teams or projects and initiate a graceful shutdown at predefined times (e.g., 7 PM local time).
2. Scheduled Startups: Conversely, workflows were also set up to power up necessary VMs before the start of the typical workday (e.g., 7 AM local time).
3. Ad-Hoc Requests: For scenarios where a VM was needed outside of scheduled hours, FlowWright provided a portal where authorized users could trigger an immediate startup, with appropriate logging and approvals.

This initial phase alone yielded significant savings. By ensuring VMs were only running during core business hours, InnovateTech saw an immediate reduction in their cloud compute costs. However, they realized this was still a somewhat blunt instrument. What about those times when a VM was genuinely needed outside of a rigid schedule? Or when a development team was working late?

Adding AI for Intelligent Decision-Making

This is where InnovateTech introduced the element of Artificial Intelligence. They integrated an AI model into their FlowWright workflows to provide a more nuanced understanding of VM utilization. The goal was to predict, with a high degree of accuracy, when a VM really needed to be on.

The AI model was trained on various data points, including:

• Historical Usage Patterns: Analyzing past CPU, memory, and network activity for each VM.
• User Login Data: Tracking when users typically accessed specific VMs or applications.
• Project Management Data: Integrating with their project management system to understand active sprints, deployment schedules, and critical deadlines.
• Calendar Data: Considering public holidays, team leave, and planned maintenance windows.
• Application Logs: Monitoring application-specific events that might indicate usage.

The AI model continuously learned from this data. Instead of simply relying on a static schedule, the updated FlowWright workflow now incorporated a decision-making step:

1. AI Prediction: Before initiating a shutdown, FlowWright would query the AI model: "Does VM X truly need to be on right now, or in the immediate future?"
2. Dynamic Adjustment: Based on the AI's prediction, the workflow would either proceed with the shutdown or intelligently defer it, extending the VM's uptime for a specified period, or even keep it running if the AI predicted imminent, legitimate use.
3. Proactive Startups (Optional): In some advanced scenarios, the AI could even trigger a VM startup before a scheduled time if it detected an unusual pattern or a high probability of early access based on past behavior.
4. Anomaly Detection: The AI also helped identify "ghost" VMs – instances that were running but showed no legitimate activity, indicating potential misconfigurations or forgotten resources.

The Results: Smart Savings and Enhanced Agility

InnovateTech's journey with FlowWright and AI-powered automation resulted in a transformation of their cloud cost management. They achieved:

• Significant Cost Reductions: By optimizing VM uptime, they slashed their cloud compute costs by an additional 25% beyond what basic scheduling offered.
• Improved Resource Utilization: VMs were no longer idly consuming resources, leading to a more efficient cloud environment.
• Enhanced Developer Experience: While cost savings were paramount, developers also benefited. The AI ensured that critical development or testing environments were available when truly needed, reducing frustration and waiting times.
• Reduced Manual Overhead: The IT team was freed from the tedious task of manually managing VM lifecycles, allowing them to focus on more strategic initiatives.
• Data-Driven Decisions: The AI provided valuable insights into actual VM usage, helping InnovateTech make better decisions about future cloud provisioning and resource planning.

What's Next for Enterprise Companies

InnovateTech's success story is a testament to the power of intelligent automation. As cloud environments continue to grow in complexity, the need for sophisticated tools to manage resources efficiently will only intensify. Combining robust process automation platforms like FlowWright with the predictive capabilities is becoming a necessity for any organization serious about optimizing its cloud spend and maximizing its operational agility.

By understanding when VMs truly need to be on, organizations can move beyond reactive cost-cutting measures to a proactive, intelligent, and sustainable approach to cloud savings. Schedule a demo to explore our microservices capabilities and discover we can help your team and business scale using workflow automation.