Cloud-Based Resource Allocation

Kubernetes is where the next cloud waste needs to be checked I am not saying, Kubernetes is inefficient but most organizations measure its cost too late. The cloud bill shows the cost of the nodes. It rarely explains which cluster, namespace, workload, feature, or engineering team actually consumed them. So a team requests two CPUs “for safety.” The workload uses half of one. That request gets copied into the next deployment, multiplied across replicas, and repeated across environments. Six months later, the waste is no longer a configuration issue. It has hardened into architecture. This is why cluster level visibility is not enough. Platform leaders need allocation across three layers: Node cost tells you what you purchased. Cluster cost tells you where capacity sits. Workload cost tells you what is creating business value and what is simply reserving infrastructure. The impact can be significant. In one CNCF case study, an enterprise AI learning platform combined Kubernetes, intelligent autoscaling, GitOps, and FinOps governance across more than 200 nodes. It reduced cloud costs by approximately 60% while maintaining 99.99% uptime. That result did not come from negotiating cheaper instances. It came from connecting architecture decisions to workload economics. Kubernetes cost management cannot be bolted onto a generic cloud dashboard after deployment. It must sit inside the platform operating model through accurate allocation, rightsizing, autoscaling, ownership, and policy. For platform and infrastructure leaders, the question is simple: Can you identify your most expensive Kubernetes workload before finance identifies your most expensive cluster? #Kubernetes #CloudComputing #FinOps #PlatformEngineering

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?

FinOps: Treating Cloud Spend as an Engineering Discipline FinOps is not a finance function. It is an engineering discipline applied to cloud economics. The FinOps Foundation defines three phases: Inform (understand what you are spending and why), Optimize (reduce waste and improve efficiency), and Operate (build cost awareness into ongoing engineering decisions). Unit economics matter more than absolute spend. The question is not "did we spend 500k on cloud this month?" but "what is our cost per active user, per transaction, or per API call?" If revenue per unit is growing faster than cost per unit, you are scaling efficiently. If not, you have an engineering problem. Cost allocation requires tagging discipline. Every resource must be tagged with team, environment, application, and cost centre. Without this, you cannot answer "which team owns this S3 bucket that is costing 12k/month?" Enforce tagging with OPA policies that reject untagged resources before they are created. Shared services allocation: some infrastructure (networking, monitoring, logging) is shared. Allocate shared costs to consuming teams proportionally. There are three methods: show-back (informational only), charge-back (actual internal billing), and budget alerts per team. Start with show-back and graduate to charge-back as the culture matures. The FinOps review cycle: weekly anomaly detection (cost spike alerts), monthly optimisation review (act on Trusted Advisor and Advisor recommendations), quarterly commitment review (right-size Reserved Instance coverage), annual architecture review (are we on the right services?). Engineering and finance must share the same numbers. When engineers see the financial impact of their architectural decisions, they make better ones. #FinOps #CloudCost #CloudNative #Engineering #AWS

I have pointed to the challenges of multi-sided agent marketplaces. There is a massive opportunity to establish custom platforms for commercial agent interaction. Agent Exchange (AEX) provides an interesting starting point. A generalized agent market is unlikely to emerge for some time (though the potential value is immense). Consider your industry and what the dynamics of a useful agent marketplace might be. Who will take that opportunity? Below are some of the key ideas and insights in the recent paper "Agent Exchange: Shaping the Future of AI Agent Economics". 🧠 Agents become economic actors—not just tools. LLM-based agents are evolving into autonomous economic participants that can make strategic decisions, form coalitions, and bid for tasks with minimal human input. This transition underpins the rise of an “agent-centric economy,” where decentralized coordination replaces top-down control. 💸 Enhanced Auction structure provides balanced performance across real-world conditions. The authors compared five allocation methods—greedy, random, cost-optimal, capability-first, and their proposed Enhanced Auction. The Enhanced Auction was selected because it consistently delivered the best trade-off between cost efficiency, adaptability, and robustness across varying task complexities and market liquidity. It uses a weighted scoring system that factors in capability match, expected quality, cost, and timing, outperforming the narrower focus of the alternatives. ⚖️ Shapley values ensure fair credit for multi-agent collaboration. To allocate rewards fairly, the system uses the Shapley value—a game theory method that calculates each agent’s marginal contribution by averaging their added value across all possible team combinations. This approach captures interdependencies and avoids over- or under-rewarding agents in collaborative tasks. 🛠️ Adaptive coordination models for different markets. AEX supports four auction-assignment configurations—from full auctions to direct assignments—mirroring real-world systems like consulting services or cloud computing. This adaptability ensures efficient resource allocation under varying market liquidity. 💼 Specialized agents outperform large models in niche tasks. Despite the power of foundation models, the paper argues they are economically inefficient for many tasks. Specialized agents deliver better cost-performance in routine, domain-specific contexts due to lower inference costs and more targeted capabilities. AEX’s simulation shows promising performance under controlled assumptions, including static capabilities and perfect information. This work is just a starting point, as any real-world platform would need to deal with dynamic agent behaviors, strategic manipulation, and the realities of deployment, participant onboarding etc.

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

💡 Research Spotlight: 𝗠𝗦𝗰 𝗥𝗲𝘀𝗲𝗮𝗿𝗰𝗵 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 𝗣𝘂𝗯𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻 (𝗠𝗮𝗺𝗯𝗮𝗡𝗲𝘁𝟬) ❓ 𝗠𝗮𝗺𝗯𝗮𝗡𝗲𝘁𝟬: 𝐌𝐚𝐦𝐛𝐚-𝐁𝐚𝐬𝐞𝐝 𝐒𝐮𝐬𝐭𝐚𝐢𝐧𝐚𝐛𝐥𝐞 𝐂𝐥𝐨𝐮𝐝 𝐑𝐞𝐬𝐨𝐮𝐫𝐜𝐞 𝐏𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐨𝐧 𝐅𝐫𝐚𝐦𝐞𝐰𝐨𝐫𝐤 𝐓𝐨𝐰𝐚𝐫𝐝𝐬 𝐍𝐞𝐭 𝐙𝐞𝐫𝐨 𝐆𝐨𝐚𝐥𝐬 🅰️ Read our latest publication led by our 𝐄𝐄𝐂𝐒-𝐐𝐌𝐔𝐋 𝐌𝐒𝐜 𝐀𝐥𝐮𝐦𝐧𝐢 Thananont Chevaphatrakul, sheds light on the utilisation of 𝗔𝗜 (𝗠𝗮𝗺𝗯𝗮) for 𝗖𝗹𝗼𝘂𝗱 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 to enable 𝗖𝗮𝗿𝗯𝗼𝗻 𝗡𝗲𝘂𝘁𝗿𝗮𝗹 𝗖𝗼𝗺𝗽𝘂𝘁𝗶𝗻𝗴 to contribute towards 𝗡𝗲𝘁 𝗭𝗲𝗿𝗼 𝗧𝗮𝗿𝗴𝗲𝘁𝘀. 📚This work is published by an Open Access Future Internet MDPI Journal. Last week, Thananont Chevaphatrakul has passed his MSc with 𝐃𝐢𝐬𝐭𝐢𝐧𝐜𝐭𝐢𝐨𝐧 at Queen Mary School of Electronic Engineering and Computer Science of Queen Mary University of London 𝑯𝒊𝒈𝒉𝒍𝒊𝒈𝒉𝒕𝒔: 1️⃣ 𝗠𝗮𝗺𝗯𝗮𝗡𝗲𝘁𝟬 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸: MambaNet0 uses 𝗔𝗜 𝗳𝗼𝗿𝗲𝗰𝗮𝘀𝘁𝗶𝗻𝗴 model (𝗠𝗮𝗺𝗯𝗮) to manage resources effectively in the Google cloud environment, with the goal of improving 𝗲𝗻𝗲𝗿𝗴𝘆 𝗲𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆 and 𝘀𝘂𝘀𝘁𝗮𝗶𝗻𝗮𝗯𝗶𝗹𝗶𝘁𝘆 for carbon-neutral cloud services. 2️⃣ 𝗥𝗲𝗮𝗹-𝘁𝗶𝗺𝗲 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻: The MambaNet0 framework is implemented using an Google Cloud’s Vertex AI environment, and the experimental results demonstrate that the 𝗠𝗮𝗺𝗯𝗮𝗡𝗲𝘁𝟬 enhances forecasting accuracy allowing for more precise resource allocation. 3️⃣𝗥𝗲𝘀𝗲𝗮𝗿𝗰𝗵 𝗜𝗺𝗽𝗮𝗰𝘁: The MambaNet0 framework enables more sustainable and cost-effective operations that further support 𝗻𝗲𝘁-𝘇𝗲𝗿𝗼 𝗲𝗺𝗶𝘀𝘀𝗶𝗼𝗻𝘀 𝗼𝗯𝗷𝗲𝗰𝘁𝗶𝘃𝗲𝘀. 🔗 𝗚𝗶𝘁𝗛𝘂𝗯: Are you interested in extending this work for modern applications? Open source code is released on https://lnkd.in/ejD-fdXx 📒 𝙊𝙥𝙚𝙣 𝘼𝙘𝙘𝙚𝙨𝙨 𝑳𝒊𝒏𝒌 𝒕𝒐 𝒕𝒉𝒆 𝒂𝒓𝒕𝒊𝒄𝒍𝒆: https://lnkd.in/ehFes__w 📺 𝗬𝗼𝘂𝗧𝘂𝗯𝗲 𝗩𝗶𝗱𝗲𝗼: A YouTube video explains this work, enabling future authors to easily understand its workings: https://lnkd.in/eP2NAex3 Many thanks to co-author: Han Wang #EECS #QMUL #Cloudcomputing #Machinelearning #Sustainablecomputing #AI #researchpaper #computing #edge #Cloud #applications #IoT #computerscience #Research #industry #academics #journals #journal #qmul #postdoc #Scientificresearch #conference #PhD #university #publications #Computing #academiclife #ArtificialIntelligence #academia #engineering #Academic #NetZero

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