Cloud-Based Resource Allocation

Excited to share that our latest research article, “𝐓𝐨𝐰𝐚𝐫𝐝𝐬 𝐑𝐞𝐬𝐨𝐮𝐫𝐜𝐞-𝐄𝐟𝐟𝐢𝐜𝐢𝐞𝐧𝐭 𝐑𝐞𝐚𝐜𝐭𝐢𝐯𝐞 𝐚𝐧𝐝 𝐏𝐫𝐨𝐚𝐜𝐭𝐢𝐯𝐞 𝐀𝐮𝐭𝐨-𝐬𝐜𝐚𝐥𝐢𝐧𝐠 𝐟𝐨𝐫 𝐌𝐢𝐜𝐫𝐨𝐬𝐞𝐫𝐯𝐢𝐜𝐞 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞𝐬,” has been published in the Journal of Systems and Software. This collaborative work between the University of Adelaide, Monash University, and Singapore Management University introduces 𝐏𝐫𝐨𝐒𝐦𝐚𝐫𝐭 𝐇𝐏𝐀, an AI-driven extension of our previously developed auto-scaler, 𝐒𝐦𝐚𝐫𝐭 𝐇𝐏𝐀 (presented at the International Conference on Software Architecture ICSA 2024). ProSmart HPA not only 𝐩𝐫𝐞𝐝𝐢𝐜𝐭𝐬 𝐦𝐢𝐜𝐫𝐨𝐬𝐞𝐫𝐯𝐢𝐜𝐞 𝐝𝐞𝐦𝐚𝐧𝐝𝐬 to effectively manage resource requirements but also enables 𝐩𝐫𝐞𝐞𝐦𝐩𝐭𝐢𝐯𝐞 𝐫𝐞𝐬𝐨𝐮𝐫𝐜𝐞 𝐬𝐡𝐚𝐫𝐢𝐧𝐠 among microservices in resource-constrained environments. 𝐊𝐞𝐲 𝐅𝐢𝐧𝐝𝐢𝐧𝐠𝐬: - 𝐑𝐞𝐝𝐮𝐜𝐞𝐝 𝐫𝐞𝐬𝐨𝐮𝐫𝐜𝐞 𝐨𝐯𝐞𝐫𝐮𝐭𝐢𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧: 25.40% vs. Smart HPA (25.73% vs. Kubernetes HPA) - 𝐑𝐞𝐝𝐮𝐜𝐞𝐝 𝐫𝐞𝐬𝐨𝐮𝐫𝐜𝐞 𝐨𝐯𝐞𝐫𝐩𝐫𝐨𝐯𝐢𝐬𝐢𝐨𝐧𝐢𝐧𝐠: 28.24% vs. Smart HPA (86.43% vs. Kubernetes HPA) - 𝐑𝐞𝐝𝐮𝐜𝐞𝐝 𝐫𝐞𝐬𝐨𝐮𝐫𝐜𝐞 𝐮𝐧𝐝𝐞𝐫𝐩𝐫𝐨𝐯𝐢𝐬𝐢𝐨𝐧𝐢𝐧𝐠: 25.34% vs. Smart HPA (26.73% vs. Kubernetes HPA) - 𝐈𝐦𝐩𝐫𝐨𝐯𝐞𝐝 𝐫𝐞𝐬𝐨𝐮𝐫𝐜𝐞 𝐚𝐥𝐥𝐨𝐜𝐚𝐭𝐢𝐨𝐧: 2.17% vs. Smart HPA (20.75% vs. Kubernetes HPA) For more insights, check out the full paper here: https://lnkd.in/gBQDQHau Grateful to A/Prof. Claudia Szabo, A/Prof. Markus Wagner and A/Prof. Christoph Treude for their invaluable insights and contributions to this work. Faculty of Sciences, Engineering and Technology, Adelaide University #SoftwareEngineering #CloudComputing #AutoScaling #microservices #optimization #AI #DevOps #kubernetes #innovation

Misconfigured Kubernetes resource requests and limits are a primary driver of cloud waste and performance instability. This often leads to either costly over-provisioning or critical application instability due to OOMKills and CPU throttling. Balancing resource allocation is fundamental for operational efficiency and managing cloud expenditure within any containerized environment. Inadequate resource definitions directly impact node utilization, scheduling efficiency, and application reliability. Proper definition of resources.requests and resources.limits within your pod specifications is paramount. Requests define guaranteed minimums, influencing pod scheduling. Limits define hard maximums, preventing noisy neighbor issues and resource exhaustion. # Example: Efficient resource definition for a Kubernetes container containers: - name: my-app image: my-repo/my-app:1.0.0 resources: requests: cpu: "250m" # Guaranteed 0.25 CPU core for scheduling memory: "512Mi" # Guaranteed 512 MiB RAM for scheduling limits: cpu: "500m" # Capped at 0.5 CPU core, prevents noisy neighbors memory: "1Gi" # Capped at 1 GiB RAM, prevents OOMKills This configuration ensures predictable performance while providing headroom, preventing resource starvation and unnecessary eviction. Over-requesting leads to underutilized nodes; under-requesting leads to unstable applications. Pro Tip: Do not rely solely on initial estimations. Implement robust monitoring (e.g., Prometheus and Grafana) to track actual pod resource utilization over time. Use this empirical data to continuously fine-tune your requests and limits, applying an iterative, data-driven approach rather than a static "set and forget" strategy. Consider using Kubernetes Vertical Pod Autoscaler (VPA) in recommendation mode to inform these adjustments, but always validate manually for critical workloads. #DevOps #Kubernetes #CloudNative #ResourceManagement #CostOptimization #PerformanceTuning #CloudArchitecture #SRE #K8s #Containerization #Infrastructure #TechInsight #CloudComputing #FinOps #ReliabilityEngineering #InfrastructureAsCode

Imagine you’re filling a bucket from what seems like a free-flowing stream, only to discover that the water is metered and every drop comes with a price tag. That’s how unmanaged cloud spending can feel. Scaling operations is exciting, but it often comes with a hidden challenge of increased cloud costs. Without a solid approach, these expenses can spiral out of control. Here are important strategies to manage your cloud spending: ✅ Implement Resource Tagging → Resource tagging, or labeling, is important to organize and manage cloud costs. → Tags help identify which teams, projects, or features are driving expenses, simplify audits, and enable faster troubleshooting. → Adopt a tagging strategy from day 1, categorizing resources based on usage and accountability. ✅ Control Autoscaling → Autoscaling can optimize performance, but if unmanaged, it may generate excessive costs. For instance, unexpected traffic spikes or bugs can trigger excessive resource allocation, leading to huge bills. → Set hard limits on autoscaling to prevent runaway resource usage. ✅ Leverage Discount Programs (reserved, spot, preemptible) → For predictable workloads, reserve resources upfront. For less critical processes, explore spot or preemptible Instances. ✅ Terminate Idle Resources → Unused resources, such as inactive development and test environments or abandoned virtual machines (VMs), are a common source of unnecessary spending. → Schedule automatic shutdowns for non-essential systems during off-hours. ✅ Monitor Spending Regularly → Track your expenses daily with cloud monitoring tools. → Set up alerts for unusual spending patterns, such as sudden usage spikes or exceeding your budgets. ✅ Optimize Architecture for Cost Efficiency → Every architectural decision impacts your costs. → Prioritize services that offer the best balance between performance and cost, and avoid over-engineering. Cloud cost management isn’t just about cutting back, it’s about optimizing your spending to align with your goals. Start with small, actionable steps, like implementing resource tagging and shutting down idle resources, and gradually develop a comprehensive, automated cost-control strategy. How do you manage your cloud expenses?

A 2023 report by Gartner predicted that in 2025, AI-enhanced cloud management tools will reduce cloud costs by up to 30% in organizations that implement them. Here’s a Case Study every business should read. How AI Frameworks Drive Cost Reduction About the Client - A multinational company with a cloud infrastructure spread across multiple providers. Challenge > The client was facing escalating cloud costs, with expenditures growing at 20% annually. > The complexity of their cloud environment made it difficult to track resource allocation and usage efficiency. Solution - We implemented an AI-driven cloud cost management framework designed to analyze and optimize cloud spend across various platforms. This framework used machine learning algorithms to: > Monitor and analyze usage patterns in real-time. > Identify idle and underutilized resources. > Provide automated recommendations for resource reallocation. > Predict future usage and cost trends based on historical data. Results: Within the first year of implementation, the client saw a 25% reduction in cloud spend. Idle resource usage was cut by 40%. Overall operational efficiency improved due to better resource allocation and usage forecasting. #Cloudmodernisation

Virtual machine (VM) allocation is arguably the holy grail of #cloud resource management. While the bulk of the algorithmic work in this area has focused on the assignment of VMs to servers, much less attention has been given to which allocator instance should perform each assignment task. In our upcoming #OSDI2025 paper, we design novel latency-driven cache-aware algorithms for allocator scheduling. We deployed our solution in all #Azure's production zones, reducing allocation latency by 11.9% (at the 90th percentile) and memory usage by 17%. The results described in the paper are the outcome of a fantastic collaboration between our group at MSR Redmond (Hugo Barbalho, Marco Molinaro), the Azure Core Platform Capacity & Efficiency Team (Kuan Liu, Abhisek Pan, David Dion, Thomas Moscibroda), and colleagues from Rutgers University (David Domingo who is on the job market, and Sudarsun Kannan). Paper link: https://lnkd.in/gxrtc_Hu

Post 14: Real-Time Cloud & DevOps Scenario Scenario: Your team manages a multi-cloud architecture where workloads are deployed across AWS and Azure. Recently, you noticed discrepancies in cost allocation and performance monitoring due to inconsistent tagging of cloud resources. This lack of standardization is causing inefficiencies in governance and billing.As a DevOps engineer, your task is to implement a unified resource tagging strategy to streamline cost management and resource tracking across clouds. Step-by-Step Solution: Define a Tagging Policy: Establish a standard naming convention for tags across AWS and Azure. Common tags include: Environment: prod, dev, staging. Owner: Responsible team or individual. Application: Associated app or service name. CostCenter: For billing and budgeting. Implement Resource Tagging Automation: Use AWS Tag Editor and Azure Policy to enforce consistent tagging on new resources.Automate tagging during resource creation using Infrastructure as Code (IaC) tools like Terraform or CloudFormation. Example for Terraform: hcl Copy code tags = { Environment = "prod" Owner = "DevOpsTeam" Application = "EcommerceApp" } Audit and Enforce Tag Compliance: Use AWS Config and Azure Resource Graph to audit existing resources for missing or inconsistent tags.Set up policies to deny the creation of resources that do not adhere to the tagging standards. Enable Cost Allocation Reports: Configure AWS Cost Explorer and Azure Cost Management to generate cost reports based on tags. Group costs by tags such as CostCenter and Application for better insights. Use Cross-Cloud Tagging Tools: Leverage tools like CloudHealth or Spot by NetApp for centralized visibility and management of tags across multiple cloud platforms. Educate Teams on Tagging Practices: Conduct training sessions to ensure all team members understand the importance of tagging and how to follow the standards. Share tagging templates and guidelines. Monitor and Automate Alerts for Non-Compliance: Use AWS CloudWatch and Azure Monitor to set up alerts for resources created without tags or with incorrect tags.Automate remediation scripts to update or add missing tags. Periodically Review and Update Tags: Schedule periodic reviews to ensure tags remain relevant as projects and teams evolve.Update tagging policies as business needs change. Outcome: Streamlined resource tracking and cost allocation across AWS and Azure. Improved governance and compliance with reduced manual intervention. 💬 How do you manage resource tagging in multi-cloud environments? Let’s share best practices in the comments! ✅ Follow Thiruppathi Ayyavoo for daily real-time scenarios in Cloud and DevOps. Let’s grow and succeed together! #DevOps #CloudComputing #MultiCloud #AWS #Azure #ResourceTagging #CostManagement #RealTimeScenarios #CloudEngineering #TechSolutions #LinkedInLearning #careerbytecode #thirucloud #linkedin #USA CareerByteCode

In today’s multi-tenant GPU cloud environments, fairness, governance, and efficiency are non-negotiable. Whether you’re a platform admin or leading a high-performance AI/ML team, understanding how to allocate GPU capacity intelligently is critical. In this blog, I break down how GPU cloud providers—and their tenant organizations—can implement fine-grained resource quotas at multiple levels: ✅ Org-level quotas for strategic capacity planning ✅ Project-level quotas for team-level delegation ✅ Per-user quotas to avoid resource contention and ensure fairness 💡 We walk through an example quota hierarchy, showing how resources can be allocated from provider to org, from org to project, and from project to individual users—preserving scalability, cost control, and operational transparency every step of the way. 📊 Whether you’re managing a GPU cloud or building on one, this framework provides a practical path to governed scale. 🔗 Read the full blog post https://lnkd.in/gVrK4JNv #GPUCloud #MultiTenancy #CloudComputing #QuotaManagement #PlatformEngineering #AIInfrastructure #Kubernetes #CloudGovernance #MLOps #FinOps