اپلیکیشن اندرویدی فلش‌کارت آموزشی Google Cloud Platform: بهینه سازی هزینه برای اپلیکیشن های خدمات پلتفرم ابری در سازمان ها

🎓 دوره آموزشی جامع

📚 اطلاعات دوره

عنوان دوره: دوره Google Cloud Platform: بهینه سازی هزینه برای اپلیکیشن های خدمات پلتفرم ابری در سازمان ها

موضوع کلی: برنامه نویسی

موضوع میانی: Google Cloud Platform (GCP)

📋 سرفصل‌های دوره

• 1. مبانی رایانش ابری
• 2. مقدمه ای بر Google Cloud Platform
• 3. مقایسه مدل های ابری (IaaS, PaaS, SaaS)
• 4. معرفی GCP و اکوسیستم آن
• 5. مزایای استفاده از GCP برای سازمان ها
• 6. نقش GCP در بهینه سازی هزینه
• 7. مبانی مدیریت هزینه در محیط های ابری
• 8. اصطلاحات کلیدی در مدیریت هزینه GCP
• 9. انواع هزینه ها در GCP
• 10. مدل های قیمت گذاری GCP (On-Demand, Reserved Instances, Spot Instances)
• 11. معرفی ابزارهای مدیریت هزینه GCP
• 12. Google Cloud Billing Account Structure
• 13. Google Cloud Projects and Folders
• 14. Resource Hierarchy in GCP
• 15. Cost Allocation and Tagging Strategies
• 16. Best Practices for Cost Allocation
• 17. Introduction to Cost Management Tools
• 18. Google Cloud Cost Management Dashboard
• 19. Understanding Billing Reports
• 20. Forecasting Future Costs
• 21. Setting Budgets and Alerts
• 22. Alerting Mechanisms and Configuration
• 23. Using Cost Breakdown Reports
• 24. Analyzing Service-Specific Costs
• 25. Identifying Cost Drivers
• 26. Optimizing Compute Engine Costs
• 27. Compute Engine Machine Types and Families
• 28. Choosing the Right Machine Type
• 29. Using Custom Machine Types
• 30. Sizing Virtual Machines Appropriately
• 31. Understanding Preemptible VMs (Spot VMs)
• 32. When to Use Spot VMs
• 33. Pricing for Spot VMs
• 34. Benefits and Risks of Spot VMs
• 35. Optimizing Persistent Disk Costs
• 36. Types of Persistent Disks (Standard, Balanced, SSD)
• 37. Choosing the Right Disk Type
• 38. Disk Size Optimization
• 39. Disk Deletion Policies
• 40. Snapshots and their Cost Implications
• 41. Optimizing Cloud Storage Costs
• 42. Cloud Storage Classes (Standard, Nearline, Coldline, Archive)
• 43. Choosing the Right Storage Class
• 44. Lifecycle Management for Cloud Storage
• 45. Object Versioning and its Cost
• 46. Interconnect and Network Egress Costs
• 47. Understanding Network Egress Pricing
• 48. Optimizing Network Traffic
• 49. Content Delivery Network (CDN) for Cost Savings
• 50. Using Cloud CDN
• 51. Caching Strategies for CDN
• 52. Optimizing Database Costs
• 53. Cloud SQL Pricing Models
• 54. Choosing the Right Instance Size for Cloud SQL
• 55. Read Replicas and their Cost
• 56. High Availability Configurations and Costs
• 57. Memorystore Pricing
• 58. Optimizing for Memorystore Usage
• 59. Cloud Spanner Pricing
• 60. Optimizing Cloud Spanner Configuration
• 61. Optimizing Kubernetes Engine (GKE) Costs
• 62. GKE Node Pool Sizing
• 63. Autoscaling GKE Nodes
• 64. Cluster Autoscaler Configuration
• 65. Using Spot VMs for GKE Nodes
• 66. Optimizing Pod Scheduling
• 67. Resource Requests and Limits in GKE
• 68. Kubernetes Cost Management Tools
• 69. Using Namespaces for Cost Isolation
• 70. Optimizing Serverless Costs
• 71. Cloud Functions Pricing
• 72. Optimizing Cloud Functions Execution Time
• 73. Cloud Run Pricing
• 74. Optimizing Cloud Run Container Sizes
• 75. Memory and CPU Allocation for Serverless
• 76. Choosing Between Cloud Functions and Cloud Run
• 77. Introduction to Anthos and its Cost Implications
• 78. Anthos Cost Management Features
• 79. Strategies for Cost Optimization with Anthos
• 80. Introduction to BigQuery and its Cost Model
• 81. Query Pricing in BigQuery
• 82. Storage Pricing in BigQuery
• 83. Optimizing BigQuery Queries for Cost
• 84. Data Partitioning and Clustering
• 85. Streaming Inserts and their Cost
• 86. Data Transfer Costs to and from BigQuery
• 87. Introduction to Dataflow and its Cost Model
• 88. Dataflow Pricing Factors
• 89. Optimizing Dataflow Job Performance
• 90. Autoscaling Dataflow Workers
• 91. Choosing the Right Dataflow Machine Types
• 92. Introduction to AI Platform and Vertex AI Costs
• 93. Training Costs for ML Models
• 94. Prediction Costs
• 95. Storage Costs for ML Artifacts
• 96. Optimizing AI/ML Workloads for Cost
• 97. Cost Management for Data Pipelines
• 98. Integrating Cost Management into DevOps Practices
• 99. CI/CD Pipelines for Cost Optimization
• 100. Infrastructure as Code (IaC) and Cost Control