Kubernetes Resource Limits: Predictability vs Performance
Explains Kubernetes requests vs limits, CPU vs memory sizing, QoS choices and rightsizing to balance cost and reliability.
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Blog posts in the Resource Allocation category
Top KPIs for Cloud Spend Forecasting
Good cloud forecasts start with accuracy metrics, clean allocation and fast remediation to cut budget variance under 10%.
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Batch vs. On-Demand Workloads: Resource Allocation
Match Kubernetes allocation to workload: pack batch for high utilisation and reserve headroom for on-demand services.
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Serverless Cost Optimisation for Stateless Workloads
Cost-effective serverless is about doing less work per request, cutting invocations, and monitoring platform and logging costs continuously.
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Chargeback Models for Shared Cloud Services
Practical guide to allocating shared cloud costs: define cost objects, pick allocation drivers, choose usage/fixed/hybrid models and start with showback.
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Optimizing Reserved Instances for Mixed Workloads
Commit to proven baseline compute, layer RIs and Savings Plans, and keep 20–40% on‑demand to balance savings and flexibility.
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5 Feature Selection Methods for Cloud Spend Models
Compare five feature-selection methods to improve cloud cost forecasts, reduce complexity and boost accuracy.
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AI in Multi-Cluster Workload Scheduling
AI forecasts demand to optimise multi-cluster Kubernetes scheduling—cutting cloud costs, improving GPU job throughput and enforcing UK data rules.
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Cost Analysis for Multi-Cloud Workloads
Tactical guidance to reduce multi‑cloud spend: rightsizing, standardised tagging, centralised billing and egress optimisation.
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Shared Cloud Costs: Dev vs Testing Allocation
Allocate shared cloud costs between dev and test with automated shutdowns, ephemeral environments, autoscaling and better tagging.
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Azure Free Tier: Hidden Costs Explained
Azure Free Tier can quickly incur charges from deallocated disks, data egress and logging — see the hidden costs and how to prevent them.
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Dynamic Resource Allocation with Kubernetes
How Kubernetes dynamic resource allocation cuts cloud waste, optimises GPU/CPU use and automates autoscaling with real-time metrics.
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