Improved AOE Workbooks documentation

docs/optimization-engine/README.md

@@ -32,7 +32,7 @@ The Azure Optimization Engine (AOE) is an extensible solution designed to genera
 
 ## 🙋‍♀️ Why an Optimization Engine?
 
-The Azure Optimization Engine (AOE) was initially developed to augment Virtual Machine right-size recommendations coming from Azure Advisor with additional metrics and properties (see the whole blog series dedicated to this idea, starting [here](https://techcommunity.microsoft.com/t5/core-infrastructure-and-security/augmenting-azure-advisor-cost-recommendations-for-automated/ba-p/1339298)) but quickly evolved to a generic framework for [Well-Architected Framework](https://docs.microsoft.com/en-us/azure/architecture/framework/)-inspired optimizations of all
+The Azure Optimization Engine (AOE) was initially developed to augment Virtual Machine right-size recommendations coming from Azure Advisor with additional metrics and properties (see the whole blog series dedicated to this idea, starting [here](https://aka.ms/AzureOptimizationEngine/rightsizeblogpt1)) but quickly evolved to a generic framework for [Well-Architected Framework](https://docs.microsoft.com/en-us/azure/architecture/framework/)-inspired optimizations of all
 kinds, developed by the community. Besides the recommendations generated by Azure Advisor, AOE includes several custom recommendations, mostly from the Cost pillar, and allows for the rapid development of new ones. AOE complements Azure Advisor and other first party Azure services with additional optimization insights and allows for full customization.
 
 ## 🌟 Benefits
@@ -67,7 +67,7 @@ Besides collecting **all Azure Advisor recommendations**, AOE includes other cus
     * Empty subnets and subnets with low free IP space or with too much IP space wasted
     * Orphaned NICs
 
-In addition to the custom recommendations generated every week, AOE includes a set of Azure Workbooks providing deep insights about Azure commitments (Reservations and Savings Plans), Azure Storage usage, Cost anomalies, Identity and RBAC Governance ([see blog post](https://techcommunity.microsoft.com/t5/core-infrastructure-and-security/azure-identities-and-roles-governance-dashboard-at-your/ba-p/3068613)), and Azure Policy compliance.
+In addition to the custom recommendations generated every week, AOE includes a set of Azure Workbooks providing deep insights about Azure commitments (Reservations and Savings Plans - [see blog post](https://aka.ms/AzureOptimizationEngine/commitmentsblog)), Azure Storage usage, Cost anomalies, Identity and RBAC Governance ([see blog post](https://aka.ms/AzureOptimizationEngine/identitygovblog)), and Azure Policy compliance.
 
 ## 📦 What's included
 

docs/optimization-engine/faq.md

@@ -19,7 +19,7 @@ All the frequently asked questions about AOE in one place.
 
 * **Why is my VM right-size recommendations overview page empty?** The AOE depends on Azure Advisor Cost recommendations for VM right-sizing. If no VMs are showing up, try increasing the CPU threshold in the Azure Advisor configuration... or maybe your infrastructure is not oversized after all!
 
-* **Why are my VM right-size recommendations showing up with so many Unknowns for the metrics thresholds?** The AOE depends on your VMs being monitored by Log Analytics agents and configured to send a set of performance metrics that are then used to augment Advisor recommendations. See more details [here](https://techcommunity.microsoft.com/t5/core-infrastructure-and-security/augmenting-azure-advisor-cost-recommendations-for-automated/ba-p/1457687).
+* **Why are my VM right-size recommendations showing up with so many Unknowns for the metrics thresholds?** The AOE depends on your VMs being monitored by Log Analytics agents and configured to send a set of performance metrics that are then used to augment Advisor recommendations. See more details [here](https://aka.ms/AzureOptimizationEngine/rightsizeblogpt2).
 
 * **Why am I getting values so small for costs and savings after setting up AOE?** The Azure consumption exports runbook has just begun its daily execution and only got one day of consumption data. After one month - or after manually kicking off the runbook for past dates -, you should see the correct consumption data.
 

docs/optimization-engine/reports.md

@@ -16,6 +16,13 @@ Visualize the Azure Optimization Engine rich recommendations and insights.
 
 - [📒 Power BI recommendations report](#-power-bi-recommendations-report)
 - [📒 Workbooks](#-workbooks)
+    - [💉 Recommendations](#-recommendations)
+    - [🤝 Azure Commitments Insights](#-azure-commitments-insights)
+    - [📈 Costs Growing](#-costs-growing)
+    - [📖 Resources Inventory](#-resources-inventory)
+    - [🕵 Identities and Roles](#-identities-and-roles)
+    - [📚 Block Blob Storage Usage](#-block-blob-storage-usage)
+    - [✅ Policy Compliance](#-policy-compliance)
 
 </details>
 
@@ -71,22 +78,58 @@ The **Recommendation Details** option takes you to a page where you can see all
 
 ## 📒 Workbooks
 
-With AOE's Log Analytics Workbooks, you can explore many perspectives over the data that is collected every day. For example, costs growing anomalies, Microsoft Entra ID and Azure RM principals and roles assigned, how your resources are distributed, how your Block Blob Storage usage is distributed, how your Azure Benefits usage is distributed (supports only Enterprise Agreement customers) or exploring Azure Policy compliance results over time.
+With AOE's Log Analytics Workbooks, you can explore many perspectives over the data that is collected every day. For example, costs growing anomalies, Microsoft Entra ID and Azure RM principals and roles assigned, how your resources are distributed, or getting insights about your Azure commitments usage (supports only EA and MCA customers). See below a short description of each Workbook.
+
+### 💉 Recommendations
+
+The Recommendations workbook is the go-to report to start with your AOE-based Azure optimization journey. It reports on the optimization recommendations generated every week by both AOE and Azure Advisor, across the five pillars of the Well Architected Framework - Cost, Operational Excellence, Performance, Reliability, and Security.
 
 ![An overview of all your optimization recommendations](../assets/images/aoe/workbooks-recommendations-overview.jpg "An overview of all your optimization recommendations")
 
 ![An overview of your Cost optimization opportunities](../assets/images/aoe/workbooks-recommendations-costoverview.jpg "An overview of your Cost optimization opportunities")
 
+### 🤝 Azure Commitments Insights
+
+For a complete Azure Reservations and Savings Plans performance analysis and purchase simulations, you have several Workbooks available:
+
+- **Benefits Simulation** allows for simulations of Savings Plans and Reservations commitments savings and coverage based on on-demand Virtual Machines usage history.
+- **Benefits Usage** reports on the distribution of the different pricing models usage (Savings Plans, Reservations, Spot, and On-Demand) and on the savings each pricing model is achieving compared to others.
+- **Reservations Potential** reports on On-Demand Virtual Machines usage and its potential for Reservations commitments, with historical analysis and details of resources potentially consuming those reservations.
+- **Reservations Usage** reports on Reservations usage and allows for usage aggregation by resource tags and deeper insights about real savings (including unused reservations).
+- **Savings Plans Usage** reports on Savings Plans usage and allows for usage aggregation by resource tags and deeper insights about real savings (including unused savings plans).
+
+Check [this blog post](https://aka.ms/AzureOptimizationEngine/commitmentsblog) for a complete description of each Workbook.
+
+![Azure Benefits usage analysis with a comparison between Reservations and On-Demand/Savings Plan prices](../assets/images/aoe/workbooks-benefitsusage-reservations.jpg "Azure Benefits usage analysis with a comparison between Reservations and On-Demand/Savings Plan prices")
+
+### 📈 Costs Growing
+
+The **Costs Growing** Workbook reports on usage growth anomalies detected across multiple perspectives: subscription, meter category, meter sub-category, meter name, resource group, or individual resources.
+
 ![Costs growing anomalies](../assets/images/aoe/workbooks-costsgrowing-anomalies.jpg "Costs growing anomalies")
 
+### 📖 Resources Inventory
+
+The **Resources Inventory** Workbook reports on the distribution of the most relevant Azure resource types (mostly IaaS) across different perspectives, including its historical evolution.
+
 ![Virtual Machines perspectives over time](../assets/images/aoe/workbooks-resourcesinventory-vms.jpg "Virtual Machines perspectives over time")
 
+### 🕵 Identities and Roles
+
+The **Identities and Roles** Workbook reports on Microsoft Entra ID objects (users, groups and applications) and their respective roles across the Entra ID tenant and Azure resources. For a more detailed analysis of this Workbook, check [this blog post](https://aka.ms/AzureOptimizationEngine/identitygovblog).
+
 ![Microsoft Entra ID/Azure Resource Manager principals and roles summary, with service principal credentials expiration](../assets/images/aoe/workbooks-identitiesroles-summary.jpg "Microsoft Entra ID/Azure Resource Manager principals and roles summary, with service principal credentials expiration")
 
 ![Privileged Microsoft Entra ID roles and assignment history](../assets/images/aoe/workbooks-identitiesroles-rolehistory.jpg "Priviliged Microsoft Entra ID roles and assignment history")
 
+### 📚 Block Blob Storage Usage
+
+The **Block Blob Storage Usage** Workbook reports on the distribution of Block Blob Storage usage across different types of Storage Accounts, file structure, replication options, and tiering; allows for simulations of hot to cool tiering savings.
+
 ![Block Blob Storage usage analysis with Lifecycle Management recommendations](../assets/images/aoe/workbooks-blockblobusage-standardv2.jpg "Block Blob Storage usage analysis with Lifecycle Management recommendations")
 
-![Azure Benefits usage analysis with a comparison between Reservations and On-Demand/Savings Plan prices](../assets/images/aoe/workbooks-benefitsusage-reservations.jpg "Azure Benefits usage analysis with a comparison between Reservations and On-Demand/Savings Plan prices")
+### ✅ Policy Compliance
+
+The **Policy Compliance** Workbook reports on Azure Policy compliance across the whole tenant, with an historical perspective and also the ability to filter and group by resource tags.
 
 ![Policy Compliance state, with evolution over time](../assets/images/aoe/workbooks-policycompliance.jpg "Policy Compliance state, with evolution over time")

src/optimization-engine/README.md

@@ -12,7 +12,7 @@ On this page:
 ## 🏯 Architecture
 
 AOE runs mostly on top of Azure Automation and Log Analytics. The diagram below depicts the architectural components. For a more detailed description, please
-read [this blog post](https://techcommunity.microsoft.com/t5/core-infrastructure-and-security/augmenting-azure-advisor-cost-recommendations-for-automated/ba-p/1339298).
+read [this blog post](https://aka.ms/AzureOptimizationEngine/rightsizeblogpt1).
 
 ![Azure Optimization Engine architecture](../../docs/assets/images/aoe/architecture.jpg "Azure Optimization Engine architecture")
 

src/optimization-engine/views/workbooks/README.md

@@ -8,7 +8,7 @@ This folder contains the several AOE workbooks reports that leverage the collect
     - [Block Blob Storage Usage](./blockblobstorage-usage.json): reports on the distribution of Block Blob Storage usage across different types of Storage Accounts, file structure, replication options, and tiering; allows for simulations of hot to cool tiering savings.
     - [Costs Growing](./costs-growing.json): reports on usage growth anomalies detected across multiple perspectives: subscription, meter category, meter sub-category, meter name, resource group, or individual resources.
     - [Reservations Potential](./reservations-potential.json): reports on On-Demand Virtual Machines usage and its potential for Reservations commitments, with historical analysis and details of resources potentially consuming those reservations.
-    - [Reservations Usage](./reservations-usage.json): reports on Virtual Machines Reservations usage and allows for usage aggregation by resource tags and deeper insights about real savings (including unused reservations).
+    - [Reservations Usage](./reservations-usage.json): reports on Reservations usage and allows for usage aggregation by resource tags and deeper insights about real savings (including unused reservations).
     - [Savings Plans Usage](./savingsplans-usage.json): reports on Savings Plans usage and allows for usage aggregation by resource tags and deeper insights about real savings (including unused savings plans).
 - Governance
     - [Identities and Roles](./identities-roles.json): reports on Microsoft Entra ID objects (users, groups and applications) and their respective roles across the Entra ID tenant and Azure resources.