Dynamic Workloads: Predictive Scaling Strategies
Forecast capacity for repeat peaks, combine a predictive baseline with reactive HPA/KEDA, and apply cost guardrails and retraining.
Read moreBlog posts in the Cloud Optimization category
Forecast capacity for repeat peaks, combine a predictive baseline with reactive HPA/KEDA, and apply cost guardrails and retraining.
Read moreCompare AWS EDP and Azure MACC: how to size commitments, manage exclusions, and time renewals to avoid costly contract shortfalls.
Read moreHybrid cloud cost control fails when billing, usage and ownership data sit in silos—unify models, enforce tags and allocate shared spend.
Read moreCut waste, shorten lead times and lower cloud spend with value stream mapping, CI/CD automation, WIP limits and cost controls.
Read moreStandardise fleet-wide Kubernetes policy in Git, deploy with GitOps, use Kyverno or Gatekeeper, audit then enforce, time-box exceptions.
Read morePay only for the multi-region resilience and speed you need: limit replication, keep writes local, optimise routing and test failover.
Read moreMeasure CPU, memory, storage and network over 60–90 days, align monitoring with billing and tags, then act to remove idle cloud spend.
Read moreAI matches workloads to on‑demand, reserved and spot pricing to cut cloud waste while protecting performance and control.
Read moreTight, time-limited, role-based IAM across staff and machine identities secures hybrid retail.
Read moreStandardise CI/CD with reusable templates, policy-as-code and cost controls to cut pipeline chaos, speed releases and reduce cloud spend.
Read moreTagging only works for cost allocation when simple, enforced and tied to finance: small schema, automation and mapped cost centres.
Read moreUnify metrics, logs, traces and network paths; set tags, SLOs and ownership; run hybrid cloud monitoring as a daily practice.
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