Cloud Infrastructure Optimization

Alongside building resilient, highly available systems and strengthening security posture, I’ve been exploring a new focus area, optimising cloud costs. Over the last few months, this has led to some clear lessons for me that are worth sharing. 1. Compute planning is the foundation. Standardising on machine families and analysing workload patterns allows you to commit to savings plans or reserved instances. This is often the highest ROI move, delivering big savings without actually making a lot of technical changes. 2. Account structures impact cost. Multiple AWS accounts improve governance and security but make it harder to benefit from bulk discounts. Using consolidated billing and commitment sharing across accounts brings the efficiency back. 3. Kubernetes compute checks are important. Nodes in K8s are often over-provisioned or underutilised. Automated rebalancing tools help, as does smart use of spot instances selected for reliability. On top of this, workload resizing during off hours, reducing CPU and memory when demand is low, delivers direct and recurring savings. 4. Watch for operational leaks. Debug logs on CDNs and load balancers, once useful, often stay enabled long after issues are fixed. They quietly pile up costs until someone takes notice. 5. Right-sizing is a continuous process. Urgent projects often lead to overprovisioned instances for anticipated load that never fully arrives. Monitoring and regular reviews are the only way to keep infrastructure aligned with reality. The real win in cloud cost optimisation comes from treating it as a continuous practice, not a one-off project. Small inefficiencies compound fast, so important to be on the lookout! #CloudCostOptimization #AWS #Kubernetes #DevOps #CloudInfrastructure #RightSizing #WorkloadManagement #SavingsPlans #SpotInstances #CloudEfficiency #TechInsights #CloudOps #CostManagement #CloudBestPractices

30% of cloud spending is wasted due to inefficiencies. I keep seeing the same pattern in AVD environments. VMs overprovisioned "just to be safe". Auto-scaling policies that were never actually configured. Storage accounts nobody's looked at in months. Meanwhile, finance is questioning every Azure invoice. Applying DevOps principles to your cloud desktop environment genuinely fixes this: 🔹 Infrastructure as Code - Use Terraform, Bicep or Nerdio Manager to automate resource provisioning. When infrastructure is code, environments become reproducible, auditable, and cost-optimised by default. No more inconsistent deployments that drift and accumulate waste. 🔹 Automated Scaling - Configure Nerdio AVD scaling plans properly. Enable Start VM on Connect so session hosts stay deallocated until users actually need them. You only pay for compute when someone's working. 🔹 Continuous Monitoring - Azure Monitor or Nerdio Manager autoscaling history gives you visibility into usage patterns. Once you have that data, you can identify which host pools are overprovisioned and which storage accounts are burning money overnight. 🔹 Right-Sizing Resources - Match VM SKUs to actual workload requirements. I've seen customers running D16S for users who barely touch 4 vCPUs. That's expensive guesswork. Use metrics to validate your sizing decisions. 🔹 Regular Cost Audits - Schedule quarterly reviews of your cloud resources. Orphaned disks, unattached public IPs, oversized FSLogix storage tiers... these accumulate quietly and compound monthly. 🔹 Automation Tooling - Nerdio Manager for Enterprise automates much of this for AVD. Intelligent autoscaling, cost reporting, right-sizing recommendations. Takes the manual effort out of continuous optimisation. The organisations I work with that treat cloud desktop infrastructure as code rather than clicking through portals consistently see material cost reductions. Most teams know what to do - actually implementing it consistently is where things fall apart. What's the biggest cost-waste generator you've found in your environment? #AVD #DevOps #Azure #Nerdio #FinOps #AzureVirtualDesktop #Azure #Nerdio

How I Used Load Testing to Optimize a Client’s Cloud Infrastructure for Scalability and Cost Efficiency A client reached out with performance issues during traffic spikes—and their cloud bill was climbing fast. I ran a full load testing assessment using tools like Apache JMeter and Locust, simulating real-world user behavior across their infrastructure stack. Here’s what we uncovered: • Bottlenecks in the API Gateway and backend services • Underutilized auto-scaling groups not triggering effectively • Improper load distribution across availability zones • Excessive provisioned capacity in non-peak hours What I did next: • Tuned auto-scaling rules and thresholds • Enabled horizontal scaling for stateless services • Implemented caching and queueing strategies • Migrated certain services to serverless (FaaS) where feasible • Optimized infrastructure as code (IaC) for dynamic deployments Results? • 40% improvement in response time under peak load • 35% reduction in monthly cloud cost • A much more resilient and responsive infrastructure Load testing isn’t just about stress—it’s about strategy. If you’re unsure how your cloud setup handles real-world pressure, let’s simulate and optimize it. #CloudOptimization #LoadTesting #DevOps #JMeter #CloudPerformance #InfrastructureAsCode #CloudXpertize #AWS #Azure #GCP

Post 16: Real-Time Cloud & DevOps Scenario Scenario: Your organization manages a critical API on Google Cloud Platform (GCP) that experiences traffic spikes during peak hours. Users report slow response times and timeouts, highlighting the need for a scalable and resilient solution to handle the load effectively. Step-by-Step Solution: Use Google Cloud Load Balancing: Deploy Google Cloud HTTP(S) Load Balancer to distribute incoming traffic across backend instances evenly. Enable global routing for optimal latency by routing users to the nearest backend. Enable Autoscaling for Compute Instances: Configure Managed Instance Groups (MIGs) with autoscaling based on CPU usage, memory utilization, or custom metrics. Example: Scale out instances when CPU utilization exceeds 70%. yaml Copy code minNumReplicas: 2 maxNumReplicas: 10 targetCPUUtilization: 0.7 Cache Responses with Cloud CDN: Integrate Cloud CDN with the load balancer to cache frequently accessed API responses. This reduces backend load and improves response times for repetitive requests. Implement Rate Limiting: Use API Gateway or Cloud Endpoints to enforce rate limiting on API calls. This prevents abusive traffic and ensures fair usage among users. Leverage GCP Pub/Sub for Asynchronous Processing: For high-throughput tasks, offload heavy computations to a message queue using Google Pub/Sub. Use workers to process messages asynchronously, reducing load on the API service. Monitor Performance with Stackdriver: Set up Google Cloud Monitoring (formerly Stackdriver) to track key metrics like latency, request count, and error rates. Create alerts for threshold breaches to proactively address performance issues. Optimize Database Performance: Use Cloud Spanner or Cloud Firestore for scalable and distributed database solutions. Implement connection pooling and query optimizations to handle high-concurrency workloads. Adopt Canary Releases for API Updates: Roll out updates to a small percentage of users first using Cloud Run or Traffic Splitting. Monitor performance and rollback if issues arise before full deployment. Implement Resiliency Patterns: Use circuit breakers and retry mechanisms in your application to handle transient failures gracefully. Ensure timeouts are appropriately configured to avoid hanging requests. Conduct Load Testing: Use tools like k6 or Apache JMeter to simulate traffic spikes and validate the scalability of your solution. Identify bottlenecks and fine-tune the architecture. Outcome: The API service scales dynamically during peak traffic, maintaining consistent response times and reliability.Enhanced user experience and improved resource efficiency. 💬 How do you handle traffic spikes for your applications? Let’s share strategies and insights in the comments! ✅ Follow Thiruppathi Ayyavoo for daily real-time scenarios in Cloud and DevOps. Let’s learn and grow together! #DevOps #CloudComputing #GoogleCloud #careerbytecode #thirucloud #linkedin #USA CareerByteCode

We replaced AWS ALB with 1990s tech — and handled 10× more traffic for $0.01/hour. Sounds insane. It isn’t. Our Application Load Balancers were quietly eating $3,800/month just to forward packets. Latency was fine. Reliability was fine. But the cost-to-value ratio made no sense anymore. So we did something most teams don’t even consider in 2026: We removed ALB entirely Moved load balancing into the Linux kernel Used IPVS (yes, that IPVS) What changed Instead of managed L7 load balancers, we run: • IPVS as a Kubernetes DaemonSet • One tiny node per AZ • Elastic IPs via kube-vip • Direct Server Return (DSR) Result: • 10× higher throughput • Sub-millisecond connection setup • No LCU tax • No proxying response traffic • $0.009/hour per AZ The load balancer stopped being the bottleneck. “But IPVS is dumb L4” Exactly. That’s the point. We push intelligence inward, not outward: • L4 performance at the edge (IPVS) • L7 routing via Envoy inside the pod • Kernel speed where it matters • Flexibility where it belongs The real takeaway Managed ≠ optimal. AWS load balancers are amazing for: • Fast setup • Generic workloads • Default architectures They are not optimized for: • High-throughput systems • Cost-disciplined platforms • Teams that know their traffic patterns We traded a few hours of setup for: • ~$45K/year savings • Better latency • Full control of the data path Sometimes the most “cloud-native” move is remembering how systems worked before abstraction hid the costs. Curious what others think Would you ever drop managed LBs in production — or is this a step too far? #AWS #DevOps #Kubernetes #CloudArchitecture #SiteReliabilityEngineering #Infrastructure #CostOptimization #PlatformEngineering #Linux #Networking #Scaling Beacon Hill TEKsystems Randstad Digital Americas Northern Trust

Most companies optimize cloud costs by focusing on the wrong part of the equation. Here's the formula that drives every cloud bill: Cloud Cost = Usage × Price Most FinOps teams attack the price component: - Negotiate enterprise agreements with AWS, Azure etc - Buy reserved instances for discounts - Commit to spending quotas for better rates You can get 60% off through aggressive pricing negotiations, but here's the problem: If an engineer launches a server and never uses it, that's 100% waste. Even with a 60% discount, you're still wasting 40%. The better strategy: Optimize usage first, then negotiate price. → Get your $30M annual spend down to $10M through better resource utilization. → Then go to AWS and negotiate 10% off that $10M instead of negotiating 20% off the wasteful $30M. The usage component is entirely in engineers' hands: - What services do they choose? - How do they configure them? - How much CPU and memory? But companies avoid this because it's harder. Most take the easy path and just negotiate with vendors. That's why we built Infracost at the usage layer - it's where the real optimization happens.

If you’re in cloud and not looking at optimization end-to-end, you’re missing out — here are the key strategies you should know.. → Compute ↳ Right-size instances, use auto-scaling/serverless, and leverage spot/preemptible VMs ↳ Consolidate workloads with Kubernetes/Fargate/Cloud Run → Storage ↳ Use lifecycle policies to move infrequently used data to cheaper tiers ↳ Deduplication, compression, and smart replication strategies reduce costs → Networking ↳ CDN for static content, private networking to cut egress, and traffic shaping with load balancers ↳ Always optimize data transfer (avoid unnecessary cross-region costs) → Databases ↳ Use managed services, read replicas, and caching ↳ Shard/partition for scale, and pick the right DB for the workload → Big Data ↳ Spot clusters for jobs, serverless analytics, and data partitioning ↳ Stream only what’s critical, batch the rest → Security ↳ Enforce least privilege IAM, encrypt in transit/at rest ↳ Automate threat detection and centralize secrets with KMS/Vault → AI/ML ↳ Track experiments, use AutoML/pre-trained APIs ↳ Share GPUs, and clean/optimize data before training Essential Note: Cloud optimization isn’t a one-time exercise. You have to keep at it — especially now, with AI workloads driving cloud costs to new highs. Start with one area → measure impact → repeat. What other strategies would you add? • • • If you found this useful.. 🔔 Follow me (Vishakha) for more Cloud & DevOps insights ♻️ Share so others can learn as well!

9 𝐏𝐨𝐰𝐞𝐫𝐟𝐮𝐥 𝐖𝐚𝐲𝐬 𝐭𝐨 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐞 𝐘𝐨𝐮𝐫 𝐂𝐥𝐨𝐮𝐝 𝐟𝐨𝐫 𝐄𝐧𝐡𝐚𝐧𝐜𝐞𝐝 𝐃𝐞𝐯𝐎𝐩𝐬 𝐏𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞 Looking to streamline your DevOps workflow and optimize your cloud environment? This post dives into 9 impactful strategies that will empower your DevOps team to achieve optimal cloud performance 1️⃣Right-sizing Your Cloud Footprint - Match your services to your workload requirements. Don't overspend on excessive resources or struggle with underprovisioning that hinders performance. - Continuously monitor and analyze your cloud usage to ensure you're utilizing resources effectively. 2️⃣Leveraging Auto-Scaling for Dynamic Resource Allocation - Automate scaling policies to adjust resources based on real-time demand. This helps optimize costs during low-traffic periods and prevents bottlenecks during peak usage. 3️⃣ Embracing Cloud-Native Architecture with Microservices - Build applications as a collection of small, independent microservices. This enables independent scaling for each service, leading to precise resource allocation and increased flexibility. 4️⃣ Optimizing Data Storage for Efficiency and Cost-Effectiveness - Implement tiered storage based on data access frequency. Frequently accessed data can reside on high-performance storage, while less frequently accessed data can be stored on cost-efficient tiers. - Utilize data compression and deduplication techniques to minimize storage needs and associated costs. 5️⃣ Implementing Proactive Cost Management - Maintain close track of your cloud spending with detailed cost analysis tools provided by your cloud provider. - Set up budget alerts and notifications to avoid unexpected charges and maintain financial control. 6️⃣ Exploring Multi-Cloud Strategies for Enhanced Benefits - This approach can also improve disaster recovery capabilities by ensuring redundancy across different cloud environments. 7️⃣ Implementing Effective Caching Strategies for Faster Data Retrieval - Deploy caching mechanisms to store frequently accessed data temporarily, reducing server load and improving application responsiveness. - Explore edge caching to store data closer to users for geographically distributed applications. - Utilize in-memory caching to store frequently accessed data in server memory for lightning-fast retrieval. 8️⃣ Leveraging Managed Services for Expert Support and Resource Efficiency - Offload day-to-day management tasks to your cloud provider's managed services, freeing up your DevOps team to focus on core development activities. 9️⃣ Adopting Infrastructure as Code (IaC) for Automation and Consistency - Manage and provision your cloud infrastructure through code (IaC). This enables automated infrastructure deployments, reduces manual errors, and ensures consistency across your cloud environment. - IaC also simplifies scaling processes by allowing updates to infrastructure code to reflect changes in resource requirements.

𝐊𝐮𝐛𝐞𝐫𝐧𝐞𝐭𝐞𝐬 𝐂𝐨𝐬𝐭 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧 𝐓𝐞𝐜𝐡𝐧𝐢𝐪𝐮𝐞𝐬 Kubernetes, a powerful container orchestration platform, can significantly reduce costs when used effectively. Here are some key strategies for optimizing your Kubernetes environment: 1. Rightsizing and Resource Allocation Pod Limits and Requests: Set precise resource limits and requests for each pod to prevent over-allocation and under-utilization. Node Sizing: Choose the appropriate node size based on your workload requirements to avoid paying for excess resources. Horizontal Autoscaling: Automatically scale pods up or down based on demand to ensure optimal resource utilization. Vertical Autoscaling: Adjust the resource allocation for pods to match their workload requirements. 2. Cost Monitoring and Analysis Utilize Cloud Provider Tools: Leverage cloud-specific tools (e.g., AWS Cost Explorer, GCP Cost Management) to track spending and identify cost-saving opportunities. Third-Party Tools: Consider using tools like Kubecost or Prometheus for detailed cost analysis and visualization. Regular Reviews: Regularly review your cost data to identify trends and areas for optimization. 3. Spot Instances and Preemptible VMs Leverage Spot Instances: Use spot instances or preemptible VMs for non-critical workloads to significantly reduce costs. Implement Fault Tolerance: Ensure your applications can handle interruptions caused by spot instance terminations. 4. Image Optimization Minimize Image Size: Remove unnecessary files and layers from your container images to reduce download and storage costs. Use Multi-Stage Builds: Create optimized images by building in multiple stages and copying only necessary artifacts. 5. Network Optimization Network Policies: Use network policies to restrict traffic between pods and reduce unnecessary network traffic. Load Balancing: Implement efficient load balancing strategies to distribute traffic evenly across pods. 6. Storage Optimization Persistent Volume Claims (PVCs): Use PVCs to manage persistent storage efficiently and avoid over-provisioning. Storage Classes: Create storage classes to define different storage types and their associated costs. Storage Provisioners: Choose appropriate storage provisioners based on your workload requirements and cost considerations. 7. Cluster Sharing Consolidate Clusters: If possible, consolidate multiple clusters into a single, shared cluster to reduce overhead costs. Namespace Isolation: Use namespaces to logically isolate different workloads within a shared cluster. 8. Consider Managed Kubernetes Services Evaluate Managed Offerings: Explore managed Kubernetes services (e.g., EKS, GKE, AKS) that often provide cost-effective solutions and managed infrastructure. Check here for more kubernetes Projects - https://lnkd.in/g5jCpiQg Share this post with your devops friends :)

🌟 𝗖𝗼𝘀𝘁 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝗶𝗻 𝗖𝗹𝗼𝘂𝗱 𝗗𝗲𝘃𝗢𝗽𝘀 𝗪𝗶𝘁𝗵 𝗔 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 🌟 Managing cloud expenses effectively while ensuring top-notch performance and reliability is a critical challenge for businesses today. At DevOps Shack, we’ve put together a comprehensive guide that delves deep into Cost Optimization Strategies in Cloud DevOps, complete with a hands-on project to implement these strategies. 🎯 What’s Inside? 𝗞𝗲𝘆 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗲𝘀 𝗳𝗼𝗿 𝗖𝗼𝘀𝘁 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻: 𝗥𝗶𝗴𝗵𝘁𝘀𝗶𝘇𝗶𝗻𝗴 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀: Avoid over-provisioning with tools like AWS Cost Explorer or Azure Advisor. 𝗔𝘂𝘁𝗼-𝗦𝗰𝗮𝗹𝗶𝗻𝗴: Dynamically adjust resources with AWS Auto Scaling Groups, Kubernetes HPA, and Cluster Autoscaler. 𝗟𝗲𝘃𝗲𝗿𝗮𝗴𝗶𝗻𝗴 𝗥𝗲𝘀𝗲𝗿𝘃𝗲𝗱 𝗮𝗻𝗱 𝗦𝗽𝗼𝘁 𝗜𝗻𝘀𝘁𝗮𝗻𝗰𝗲𝘀: Utilize AWS Spot Fleets and reserved instances for predictable savings. 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗶𝗻𝗴 𝗦𝘁𝗼𝗿𝗮𝗴𝗲: Implement S3 lifecycle policies and transition old data to cost-efficient tiers like S3 Glacier. 𝗦𝗲𝗿𝘃𝗲𝗿𝗹𝗲𝘀𝘀 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲𝘀: Cut infrastructure costs with AWS Lambda and Azure Functions. 𝗠𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴 𝗕𝘂𝗱𝗴𝗲𝘁𝘀 𝗮𝗻𝗱 𝗔𝗹𝗲𝗿𝘁𝘀: Stay on top of costs with AWS Budgets and CloudWatch Alarms. 𝗚𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 𝗮𝗻𝗱 𝗣𝗼𝗹𝗶𝗰𝗶𝗲𝘀: Enforce tagging and cost-efficient parameters with tools like Terraform Sentinel. 𝗛𝗮𝗻𝗱𝘀-𝗢𝗻 𝗣𝗿𝗼𝗷𝗲𝗰𝘁: Deploy a cost-optimized Kubernetes microservices application on AWS EKS. 𝗞𝗲𝘆 𝗙𝗲𝗮𝘁𝘂𝗿𝗲𝘀: - Use Terraform to provision an EKS cluster with a mix of on-demand and spot instances. - Set up Kubernetes Metrics Server, HPA, and Cluster Autoscaler. - Optimize storage with lifecycle policies. - Monitor usage and costs using Prometheus, Grafana, and AWS Cost Explorer. 𝗖𝗵𝗲𝗰𝗸𝗹𝗶𝘀𝘁 𝗳𝗼𝗿 𝗖𝗼𝗻𝘁𝗶𝗻𝘂𝗼𝘂𝘀 𝗠𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴: - Track compute, storage, and networking costs. - Regularly review resources for optimization opportunities. - Set up alerts for unexpected spikes. - Audit governance policies for better compliance. 🚀 𝗢𝘂𝘁𝗰𝗼𝗺𝗲: This guide and project demonstrate how to achieve scalable, reliable, and financially efficient cloud DevOps environments. By applying these strategies, businesses can unlock significant savings without compromising on performance. 📅 𝗕𝗮𝘁𝗰𝗵-𝟳 | 𝗗𝗲𝘃𝗦𝗲𝗰𝗢𝗽𝘀 & 𝗖𝗹𝗼𝘂𝗱 𝗗𝗲𝘃𝗢𝗽𝘀 [Started on 2nd November] 🌐 𝗥𝗲𝗴𝗶𝘀𝘁𝗲𝗿 𝗻𝗼𝘄: https://lnkd.in/gcinDR43 📘 𝗦𝘆𝗹𝗹𝗮𝗯𝘂𝘀: https://lnkd.in/gMrXa8CX ✉️ 𝗤𝘂𝗲𝗿𝗶𝗲𝘀: office@devopsshack.com | 𝗪𝗵𝗮𝘁𝘀𝗔𝗽𝗽: 8115430392 #CloudDevOps #CostOptimization #DevOpsShack #CloudComputing #Kubernetes #AWS #Azure #Terraform #Prometheus #Grafana