Resource Allocation | Hokstad Consulting

Resource Allocation

Blog posts in the Resource Allocation category

Dynamic Chargeback Models for Cloud Resources

Link elastic cloud spend to teams using time-based usage, simple allocation rules, enforced tags and automated invoice reconciliation.

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Rightsizing Kubernetes Pods: Step-by-Step Guide

Measure 2-4 weeks of usage, set requests and limits from p90–p99, and test under live traffic to cut costs and avoid throttling or OOMs.

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Energy Efficiency in Hybrid vs. Public Clouds

Public cloud often uses less energy per workload; hybrid can be greener when latency, data residency or placement matter.

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AWS vs Azure: Enterprise Discount Models

Compare AWS EDP and Azure MACC: how to size commitments, manage exclusions, and time renewals to avoid costly contract shortfalls.

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Hybrid Cloud Cost Monitoring: Challenges and Solutions

Hybrid cloud cost control fails when billing, usage and ownership data sit in silos—unify models, enforce tags and allocate shared spend.

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How to Analyse Cloud Resource Utilisation

Measure CPU, memory, storage and network over 60–90 days, align monitoring with billing and tags, then act to remove idle cloud spend.

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Best Practices for Multi-Tenant Kubernetes Clusters

Treat isolation, quotas, autoscaling and cost reporting as one system to safely run shared Kubernetes clusters.

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How Automation Improves FinOps Cost Allocation

Automate tags, billing exports and allocation rules so every pound is assigned, unallocated spend drops and AAI reaches finance-grade 95%+.

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Service Mesh Resource Costs: Optimisation Guide

Measure and cut service mesh compute and observability costs by scoping the mesh, right-sizing sidecars and trimming telemetry.

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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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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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