Managing Resource Allocation in Kubernetes

Scheduling in Kubernetes happens in various ways. Depending on the workload, you might need different algorithms like 𝗚𝗮𝗻𝗴 𝗦𝗰𝗵𝗲𝗱𝘂𝗹𝗶𝗻𝗴. Volcano, a CNCF project, supports this and can optimize complex workflows such as AI training, inference pipelines, and distributed data processing.  🚀 𝗪𝗵𝗮𝘁 𝗶𝘀 𝗚𝗮𝗻𝗴 𝗦𝗰𝗵𝗲𝗱𝘂𝗹𝗶𝗻𝗴? Gang scheduling ensures all pods in a group ("gang") start simultaneously or none do. This prevents partial execution, which is critical for interdependent tasks like distributed training or multi-stage AI pipelines. Without it, a single delayed pod could stall an entire workflow, wasting resources. 𝗘𝘅𝗮𝗺𝗽𝗹𝗲: In distributed AI training, if three worker pods are needed, Volcano’s gang scheduler waits until all 3 are available. If even one fails to schedule, the scheduler releases reserved resources to avoid cluster deadlocks. ⚡ 𝗪𝗵𝘆 𝗩𝗼𝗹𝗰𝗮𝗻𝗼? Volcano extends Kubernetes’ default scheduler to handle batch workloads and multi-pod dependencies. It’s ideal for: → AI/ML workflows (e.g., TensorFlow/PyTorch jobs). → Big Data processing (Spark, Flink). → High-performance computing (HPC). Key features: ✅ PodGroup orchestration: Treats multiple pods as a single schedulable unit. ✅ Fair-share resource allocation: Balances cluster resources across teams. ✅ Preemption/Reclaim: Prioritizes critical workloads without manual intervention. 🌟 𝗥𝗲𝗮𝗹-𝗪𝗼𝗿𝗹𝗱 𝗨𝘀𝗲 𝗖𝗮𝘀𝗲 Imagine training a large language model (LLM) across 3 GPUs. With gang scheduling: → Volcano groups all worker pods into a PodGroup. → The scheduler reserves resources only when all 3 GPUs are available. → If a node fails, Volcano retries or releases resources instantly, avoiding idle clusters. This eliminates "resource hoarding" and ensures cost-efficient scaling for AI teams. #Kubernetes #mlops

𝐀𝐫𝐞 𝐘𝐨𝐮 𝐋𝐞𝐚𝐯𝐢𝐧𝐠 𝐆𝐏𝐔 𝐌𝐨𝐧𝐞𝐲 𝐨𝐧 𝐭𝐡𝐞 𝐊𝐮𝐛𝐞𝐫𝐧𝐞𝐭𝐞𝐬 𝐓𝐚𝐛𝐥𝐞? Many teams see available GPUs in their Kubernetes clusters yet still face pending jobs or lower than expected throughput. A common reason is topology awareness. GPUs are often scheduled as simple scalar resources, while real workloads require specific placement such as same node, same NUMA domain, or shared NVLink. This can lead to fragmentation where capacity exists but cannot be used efficiently. Another frequent issue is hidden bottlenecks outside the GPU. Training workloads may be limited by dataloaders, checkpoint I O, or memory bandwidth, while inference pipelines are often constrained by CPU based tokenization, batching, or networking. In these cases GPUs are allocated but spend significant time waiting, which reduces effective utilization. Partitioning also plays a role. MIG and time slicing can improve density, but only when slice sizes align with workload demand. Without guardrails, clusters can accumulate unused slices that do not match incoming requests. Mixing MIG based inference and full GPU training in the same pool often amplifies this effect. The takeaway is simple. Improving GPU utilization through topology aware scheduling, balanced CPU and I O provisioning, and intentional partitioning directly translates into real cost savings. Teams that focus on these fundamentals often unlock meaningful efficiency gains without adding more hardware. #Kubernetes #GPUComputing #AIInfrastructure

In traditional Kubernetes autoscaling, scaling is often tied to CPU and memory thresholds. But real-world workloads don’t always spike in predictable patterns. We needed a way to scale based on external event metrics-like message queue length, API request rates, or database lag. That’s where KEDA (Kubernetes Event-Driven Autoscaler) came in. Real-World Implementation Use Case: Autoscale Kubernetes workloads based on custom metrics like Prometheus alerts, Kafka lag, and SQS message depth. Execution: Deployed KEDA as a lightweight controller in our EKS cluster Defined ScaledObjects with custom Prometheus queries as event sources Integrated with external systems (Kafka, Redis, AWS SQS, PostgreSQL) using KEDA scalers Tuned cooldown periods, polling intervals, and scale target thresholds per workload type Monitored metrics using Grafana, confirmed responsiveness in production spikes Used Metrics Server and Prometheus Adapter to bridge HPA requirements with KEDA triggers Benefits Realized Enabled fine-grained autoscaling for asynchronous and background jobs Reduced idle pod costs in low-traffic windows by over 60% Ensured instant scale-up during peak event load-no need for pre-provisioned buffers Centralized scaling logic into GitOps-managed ScaledObjects Achieved tighter alignment between actual demand and resource provisioning Event-driven scaling helped us optimize cost, performance, and resource efficiency in a unified Kubernetes-native model. Tools Used KEDA, Kubernetes, Prometheus, Metrics Server, Grafana, Kafka, SQS, Redis, PostgreSQL, ScaledObject, Helm #Kubernetes #KEDA #Autoscaling #EventDrivenArchitecture #SRE #CloudNative #Prometheus #Kafka #AWS #Redis #PostgreSQL #PlatformEngineering #GitOps #CI_CD #Helm #MetricsServer #JobSearch #Observability #SiteReliabilityEngineering #InfrastructureAsCode #Scalability #CloudEfficiency #TechCareers #SREJobs #DevOpsJobs #C2C

Post 68: Real-Time Cloud & DevOps Scenario Scenario: Your organization runs applications on Kubernetes with multiple teams deploying frequently. Recently, a production outage occurred because a deployment accidentally requested excessive CPU and memory, causing node pressure and eviction of other critical pods. As a DevOps engineer, your task is to enforce resource governance and prevent noisy-neighbor issues in shared Kubernetes clusters. Solution Highlights: ✅ Define Resource Requests and Limits Enforce CPU and memory requests/limits for all workloads to ensure fair scheduling. resources: requests: cpu: "250m" memory: "256Mi" limits: cpu: "500m" memory: "512Mi" ✅ Apply ResourceQuotas at Namespace Level Restrict total resource consumption per team or environment. apiVersion: v1 kind: ResourceQuota metadata: name: team-quota spec: hard: requests.cpu: "4" requests.memory: "8Gi" ✅ Use LimitRange for Default Constraints Automatically apply default limits to pods that forget to define them. apiVersion: v1 kind: LimitRange metadata: name: default-limits spec: limits: - default: cpu: "500m" memory: "512Mi" type: Container ✅ Enforce Policies with OPA / Kyverno Block deployments that do not define resource limits. Prevent oversized resource requests that exceed team quotas. ✅ Monitor Node Pressure and Evictions Use Prometheus + Grafana to track: Node memory pressure Pod evictions CPU throttling ✅ Use HPA and Cluster Autoscaler Together Scale pods automatically with HPA. Scale nodes automatically with Cluster Autoscaler to meet demand safely. Outcome: Stable Kubernetes clusters with predictable performance. No more noisy-neighbor incidents or accidental resource exhaustion. Clear accountability and governance for multi-team environments. 💬 How do you enforce resource governance in shared Kubernetes clusters? 👉 Share your approach below! ✅ Follow CareerByteCode daily real-time Cloud & DevOps scenarios — practical lessons from real production environments. #CloudComputing #DevOps #Serverless #AWSLambda #DynamoDB #RealTimeScenarios #APIGateway #PerformanceOptimization #TechTips #LinkedInLearning #usa #jobs #cloudbythiru #careerbytecode CareerByteCode 

This case study offers good insights for DevOps Engineers. In Kubernetes, One of the custom scheduling policies is called "MostAllocated." MostAllocated Strategy Saves Millions of Dollars For ClickHouse Here is how 👇 ClickHouse is an open-source columnar database designed for online analytical processing (OLAP). ClickHouse Cloud (serverless version of ClickHouse), runs on EKS aced rapidly rising infrastructure costs due to underutilized worker nodes. To address this inefficiency, the team switched to a MostAllocated scheduling policy. In this blog, you will learn the following - Inefficient Resource Usage by default LeastAllocated policy - How clickhouse used Bin-Packing with MostAllocated policy - Dual-scheduler approach - Rolling Out the Custom Scheduler without service interruption. 𝗗𝗲𝘁𝗮𝗶𝗹𝗲𝗱 𝗕𝗹𝗼𝗴 & 𝗦𝗼𝘂𝗿𝗰𝗲𝘀: https://lnkd.in/eie3VQVh As a DevOps engineer, you can learn the following key concepts. - Understanding how to use bin packing strategies like MostAllocated to optimize resource usage and reduce infrastructure costs. - How to deploy and manage a custom scheduler in Kubernetes for specific workload optimization. - Limiting disruptions during pod rescheduling to ensure minimal service interruptions using PodDisruptionBudget - The importance of phased rollouts and monitoring to manage changes in production environments. #DevOps #kubernetes

“𝐇𝐨𝐰 𝐝𝐨𝐞𝐬 𝐊𝐮𝐛𝐞𝐫𝐧𝐞𝐭𝐞𝐬 𝐚𝐜𝐭𝐮𝐚𝐥𝐥𝐲 𝐦𝐚𝐧𝐚𝐠𝐞 𝐆𝐏𝐔𝐬 𝐟𝐨𝐫 𝐀𝐈 𝐰𝐨𝐫𝐤𝐥𝐨𝐚𝐝𝐬?”   If you have ever trained large AI models, you know that managing GPU resources across multiple nodes isn’t easy. That is where Kubernetes + NVIDIA Device Plugin comes in making GPU allocation seamless, automated, and scalable. 𝐇𝐞𝐫𝐞’𝐬 𝐡𝐨𝐰 𝐢𝐭 𝐰𝐨𝐫𝐤𝐬 𝐢𝐧 𝐬𝐢𝐦𝐩𝐥𝐞 𝐭𝐞𝐫𝐦𝐬: 𝟏. 𝐆𝐏𝐔 𝐑𝐞𝐬𝐨𝐮𝐫𝐜𝐞 𝐌𝐚𝐧𝐚𝐠𝐞𝐦𝐞𝐧𝐭 * NVIDIA’s Device Plugin registers all available GPUs on every worker node. * Kubelet (the node agent) communicates GPU availability to the API Server*. * The Scheduler then assigns Pods based on where GPUs are available. * You can define how many GPUs a Pod should use via YAML: `resources: limits: nvidia.com/gpu: 1` 𝟐. 𝐆𝐏𝐔 𝐀𝐥𝐥𝐨𝐜𝐚𝐭𝐢𝐨𝐧 𝐖𝐨𝐫𝐤𝐟𝐥𝐨𝐰 * The Plugin manages allocation and grants GPU access to Pods automatically. * Application Pods use GPUs for training or inference without manual setup. * GPU access is securely isolated between Pods. * This enables scalable, multi-node AI workloads all managed through Kubernetes. 𝟑. 𝐓𝐡𝐞 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞 𝐚𝐭 𝐚 𝐆𝐥𝐚𝐧𝐜𝐞 * The Scheduler creates a Pod and places it on a node with GPU capacity. * The Kubelet communicates with the NVIDIA Device Plugin through gRPC. * The Plugin allocates a GPU to the Pod and updates the API Server with resource usage. * Pods then run their tasks training models, running inference, or handling data-heavy jobs using the assigned GPUs. This design eliminates manual GPU configuration, improves utilization, and enables distributed AI workloads to run efficiently at scale. Imagine scaling model training across hundreds of GPUs and not touching a single config manually. That is the power of Kubernetes orchestration for AI infrastructure. Have you tried deploying GPU workloads on Kubernetes yet? What challenges did you face? ♻️ Repost this to help your network get started ➕ Follow Jaswindder for more

Kubernetes just got smarter about hardware — and that’s a big deal for AI. Dynamic Resource Allocation (DRA) that went GA in k8s 1.34 unlocks a new way to manage GPUs, FPGAs, and other specialized devices in Kubernetes. Instead of static allocation, DRA lets you define device classes and claims, so workloads get the exact resources they need — no more underutilization or rigid scheduling. Why it matters: 1. For GPU-intensive AI/ML workloads, DRA ensures fair sharing or dedicated allocation, improving performance and efficiency. 2. It simplifies scaling AI pipelines where multiple teams or models need controlled access to accelerators. 3. It future-proofs Kubernetes clusters for emerging workloads in generative AI, HPC, and data analytics. In our first two blog posts on the k8s DRA series, we break down: - Why DRA matters? - What DRA is and how it works - Roles of Cluster Admins and Workload Admins If you’re building or scaling AI workloads on Kubernetes, DRA is a must-know capability. 👉 https://lnkd.in/gEn5uwnS and https://lnkd.in/gVHKbjrx