docs: add Hierarchical Queue feature introduction

content/en/docs/key-features/_index.md

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+title = "Key Features"
+linktitle = "Key Features"
+type = "docs"
+toc = true
+
+[menu.docs]
+  parent = "scheduler"
+  weight = 2
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+
+## Overview
+
+Volcano is a cloud-native batch system built on Kubernetes, designed to run high-performance workloads such as AI, Big Data, and HPC jobs efficiently. This section introduces the key features that make Volcano powerful and flexible for diverse scheduling scenarios.
+
+## Core Capabilities
+
+### Unified Scheduling on Kubernetes
+Volcano is fully compatible with Kubernetes and provides unified scheduling capabilities for batch workloads, online services, and mixed workloads within a single cluster.
+
+### Gang Scheduling
+Volcano supports gang scheduling, ensuring that a group of related pods are scheduled together or not at all. This is critical for distributed workloads such as MPI and deep learning training.
+
+### Hierarchical Queues
+Hierarchical queues allow users to organize workloads into multi-level queues with fine-grained resource control, improving fairness and utilization across teams and applications.
+
+### Colocation of Online and Offline Workloads
+Volcano enables colocation of latency-sensitive online services and resource-intensive offline jobs, improving overall cluster utilization.
+
+### Extensible Scheduling Plugins
+Volcano provides a rich plugin system that allows users to customize scheduling behavior for different scenarios, such as GPU scheduling, topology-aware scheduling, and resource fairness.

content/en/docs/key-features/hierarchical-queue.md

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+title = "Hierarchical Queue"
+linktitle = "Hierarchical Queue"
+type = "docs"
+toc = true
+
+[menu.docs]
+  parent = "Key Features"
+  weight = 1
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+
+## Overview
+
+Hierarchical Queue is a core scheduling feature in Volcano that allows users to organize workloads into multi-level queues. It enables fine-grained resource management and fair sharing across teams, applications, or business units.
+
+By structuring queues hierarchically, cluster administrators can better control how resources are allocated and ensure that critical workloads receive appropriate priority.
+
+---
+
+## Why Hierarchical Queue Is Needed
+
+In shared Kubernetes clusters, multiple teams and applications often compete for limited resources. Without proper queue management, some workloads may starve while others monopolize resources.
+
+Hierarchical queues address this problem by:
+- Providing isolation between different teams or workloads
+- Enforcing resource quotas at different levels
+- Improving overall cluster fairness and utilization
+
+---
+
+## Queue Models in Volcano
+
+Volcano supports different queue models to meet various scheduling requirements.
+
+### Capacity Queue
+
+A capacity queue defines the **maximum amount of resources** that a queue can use. If a queue reaches its capacity limit, additional workloads must wait until resources are freed.
+
+Capacity queues are useful when:
+- Strong resource isolation is required
+- Teams have fixed resource budgets
+- Predictable resource usage is important
+
+---
+
+### Proportion Queue
+
+A proportion queue allocates resources based on a **percentage of the total cluster capacity**. Resources are distributed proportionally among queues according to their configured shares.
+
+Proportion queues are useful when:
+- Workloads are dynamic
+- Fair sharing is preferred over strict limits
+- Clusters need to adapt to changing demand
+
+---
+
+## Typical Use Cases
+
+Hierarchical queues are commonly used in scenarios such as:
+- Multi-tenant clusters shared by multiple teams
+- AI and big data platforms with diverse workloads
+- Enterprises requiring both fairness and isolation in scheduling
+
+---
+
+## Next Steps
+
+This page provides a high-level introduction to hierarchical queues. Detailed configuration examples and advanced scheduling policies will be covered in follow-up documentation.