Smart Resource Scaling: Balancing Performance and Cost in Azure

Introduction 

Cloud computing has revolutionized the way businesses deploy and run their applications, offering unprecedented scalability, flexibility, and agility. However, cloud resources are not free, and managing them effectively can be a challenge for IT professionals. How do you ensure that your applications have enough resources to perform well, without wasting money on unused or overprovisioned resources? How do you balance the trade-offs between performance and cost in a dynamic and unpredictable environment? 

Examples of Scaling Design Strategies 

To design a reliable scaling strategy for your workloads, focus on identifying load patterns for the user and system flows for each workload that leads to a scaling operation. Here are examples of the different load patterns: 

• Static Load Patterns: Every night by 11 PM EST, the number of active users is below 100 and the CPU utilization for the app servers drops by 90% across all nodes. 
• Dynamic, Regular, and Predictable Patterns: Every Monday morning, 1,000 employees across multiple regions sign into the ERP system. 
• Dynamic, Irregular, and Predictable Patterns: A product launch happens on the first day of the month and there’s historical data from previous launches on how the traffic increases in these situations. 
• Dynamic, Irregular, and Unpredictable Patterns: A large-scale event causes a spike in demand for a product. For example, companies manufacturing and selling dehumidifiers can experience a sudden surge in traffic after a hurricane or other flooding event when people in affected areas need to dry rooms in their home. 

As you can see, grasping these patterns is essential for creating a responsive, cost-effective, and dependable infrastructure that can adeptly manage any workload with both agility and efficiency. After you’ve identified these types of load patterns, you can: 

• Identify how the load change associated with each pattern affects your infrastructure. 
• Build automation to address the scaling reliably. 

For the previous examples, your scaling strategies could be: 

• Static Load Patterns: You have a scheduled scale of your compute nodes to the minimum count (2) between 11 PM and 6 AM EST. 
• Dynamic, Regular, and Predictable Patterns: You have a scheduled scale out of your compute nodes to the normal daily capacity before the first region starts work. 
• Dynamic, Irregular, and Predictable Patterns: You define a one-time scheduled scale up of your compute and database instances on the morning of a product launch, and you scale back down after one week. 
• Dynamic, Irregular, and Unpredictable Patterns: You have auto scale thresholds defined to account for unplanned traffic spikes. 

When designing your scaling automation, be sure to account for these issues: 

• That all components of your workload should be candidates for scaling implementation. In most cases, global services like Microsoft Entra ID scale automatically and transparently to you and your customers. Be sure to understand the scaling capabilities of your networking ingress and egress controllers and your load balancing solution. 
• Those components that can’t be scaled out. An example would be large, relational databases that don’t have sharing enabled and can’t be refactored without significant impact. Document the resource limits published by your cloud provider and monitor those resources closely. Include those specific resources in your future planning for migrating to scalable services. 

• The time it takes to perform the scaling operation so that you properly schedule the operation to happen before the extra load hits your infrastructure. For example, if a component like API Management takes 45 minutes to scale, adjusting the scaling threshold to 65% instead of 90% might help you scale earlier and prepare for the anticipated increase in load. 
• The relationship of the flow’s components in terms of order of scale operations. Ensure that you don’t inadvertently overload a downstream component by scaling an upstream component first. 
• Any stateful application elements that might be interrupted by a scaling operation and any session affinity (or session stickiness) that’s implemented. Stickiness can limit your scaling ability and introduces single points of failure. 

Azure Auto-Scaling: A Smart Solution for Resource Management 

One of the key features of Azure is its ability to automatically scale resources based on the demand and load of your applications. Azure auto-scaling allows you to define rules and parameters that determine when and how your resources should scale up or down, such as CPU utilization, memory usage, queue length, or custom metrics. You can also set minimum and maximum limits for your resources, to ensure that you always have enough capacity to handle the peak load, but to also avoid overspending on unnecessary resources. 

Azure auto-scaling can help you achieve several benefits, such as: 

• Improved performance and availability: By scaling your resources according to the demand, you can ensure that your applications always have enough resources to deliver optimal performance and availability, without compromising on quality or user experience. 
• Reduced costs and waste: By scaling your resources down when they are not needed, you can avoid paying for idle or underutilized resources, and optimize your cloud spending. You can also save on operational costs, as you don’t have to manually monitor and adjust your resources. 
• Increased agility and flexibility: By scaling your resources automatically, you can respond quickly and efficiently to changing business needs and customer expectations and adapt to fluctuations in demand and traffic patterns. You can also experiment with different scaling strategies and configurations and find the best fit for your applications. 

Best Practices for Predictive Scaling 

While Azure auto-scaling can help you manage your resources effectively, it is not a magic bullet that can solve all your scaling challenges. Auto-scaling relies on reactive scaling, which means that it scales your resources after a certain threshold or condition is met. This can result in some latency or lag between the time the demand increases and the time the resources are scaled up, which can affect your application performance and user satisfaction. To avoid this, you need to implement predictive scaling, which means that you scale your resources before the demand spikes, based on historical data and trends. 

Predictive scaling can help you improve your application performance and user experience, by ensuring that your resources are ready and available before the demand increases. Predictive scaling can also help you save costs, by avoiding over-scaling or under-scaling your resources, and reducing the frequency and intensity of scaling operations. To implement predictive scaling, you need to follow some best practices, such as: 

• Analyze your application workload and behavior: You need to understand how your application behaves and performs under different load and demand scenarios and identify the patterns and trends that affect your resource consumption. You can use Azure Monitor and Application Insights to collect and analyze metrics and logs from your application and resources and visualize and track your performance and health indicators. 
• Define your scaling goals and objectives: You need to define what you want to achieve with your scaling strategy, along with the key metrics and indicators that you want to optimize. For example, you may want to improve your response time, throughput, availability, or customer satisfaction, or reduce your costs, errors, or downtime. You also need to define your scaling budget and constraints, such as the minimum and maximum limits for your resources, and the frequency and duration of scaling operations. 
• Design your scaling rules and parameters: You need to design the rules and parameters that will trigger your scaling actions, based on your scaling goals and objectives. You can use Azure’s built-in metrics, such as CPU utilization, memory usage, or queue length, or create your own custom metrics, such as requests per second, transactions per minute, or user sessions. You can also use Azure’s time-based scaling, which allows you to schedule your scaling actions based on the time of day, week, or month, or use Azure’s event-based scaling, which allows you to scale your resources based on external events, such as deployments, promotions, or holidays. 
• Test and refine your scaling strategy: You need to test and validate your scaling strategy and measure its effectiveness and efficiency. You can use Azure’s load testing tools, such as Azure DevOps or Visual Studio, to simulate different load and demand scenarios, and observe how your resources scale up or down, and how your application performs and responds. You can also use Azure’s auto scale history and notifications, to review and audit your scaling actions and outcomes, and identify any issues or anomalies. You can then refine and adjust your scaling rules and parameters and optimize your scaling strategy. 

Tips for Leveraging Azure’s Cost Management Tools 

Another important aspect of smart resource scaling is cost management, which means that you monitor and control your cloud spending and optimize resource utilization and allocation. Azure provides several tools and features that can help you manage cloud costs, such as: 

• Azure Cost Management and Billing: This is a comprehensive service that allows you to track and analyze your cloud spending and optimize your cloud efficiency. You can use Azure Cost Management and Billing to view and download your invoices and statements, create and manage budgets and alerts, forecast and optimize costs, and allocate and distribute costs across your organization. 
• Azure Advisor: This is a personalized and proactive service that provides you with recommendations and best practices to improve your Azure performance, security, reliability, and cost-effectiveness. You can use Azure Advisor to identify and eliminate idle or underutilized resources, resize or scale down your overprovisioned resources, and switch to more cost-efficient pricing or purchasing options. 

• Azure Pricing Calculator: This is a handy tool that allows you to estimate and compare the costs of different Azure services and configurations and plan your cloud budget and spending. You can use Azure Pricing Calculator to select and customize the Azure services and features you need and see the breakdown and summary of your costs and potential savings. 

Conclusion 

Smart resource scaling is a crucial skill for IT professionals who want to leverage the power and potential of Azure, while managing their cloud costs and efficiency. By using Azure’s auto-scaling capabilities, implementing predictive scaling best practices, and leveraging Azure’s cost management tools, you can achieve a balanced and optimal cloud environment, that delivers high performance and availability, and ensures cost-effectiveness and value.