SKU/Artículo: AMZ-B0FQB2MWT1
Cognitive Authentication Systems In Cloud Resource Allocation
Format:
Paperback
Detalles del producto
Disponibilidad:
En stock
En stock
Peso con empaque:
0.26 kg
0.26 kg
Devolución:
Sí
Sí
Condición
Nuevo
Nuevo
Producto de:
Amazon
Amazon
Viaja desde
USA
USA
Sobre este producto
- Cloud platforms don’t just need more servers—they need smarter decisions. This book shows how to make resource allocation and identity verification work together in real time. Blending queueing theory, practical scheduling, and modern AI, it lays out a closed-loop approach—sense → predict → decide → act → learn—that meets SLOs at minimum cost while managing risk.You’ll start with the foundations (IaaS/PaaS/SaaS, deployment models, elasticity, multi-tenancy, observability, FinOps) and progress to intelligent control: admission control, load balancing, autoscaling, placement, and DVFS. The book then introduces machine-learning pipelines (GBDT, MLP, temporal CNN/LSTM) for demand forecasting and SLA-risk prediction, plus optimization methods (heuristics, dual prices, GA/NSGA-II, MPC) that respect capacity, locality, and egress constraints. A dedicated section develops a DRL+VAE framework for long-horizon trade-offs, followed by the key idea that sets this work apart: cognitive authentication—using device, network, and behavioral signals to adapt friction and align access posture with resource policy.What you’ll learnDesign SLO-aware autoscaling using quantile forecasts and queueing insightsPlace workloads under vector capacity, (anti)affinity, locality, and egress constraintsPredict SLA violations with calibrated ML and act before they happenOptimize policies with GA/MPC/DRL under strict safety and churn limitsCouple identity risk with allocation to deliver “right-sized” friction and performanceWho should read it Cloud architects, SREs, platform engineers, and researchers building scalable, cost-efficient, and secure systems where performance and access decisions must co-evolve.
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AR$55.084
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