Change the discription of background of multi cluster scheduling

Signed-off-by: JesseStutler <[email protected]>
Signed-off-by: suyiiyii <[email protected]>

Author: JesseStutler

content/en/docs/multi_cluster_scheduling.md

@@ -17,9 +17,13 @@ type = "docs"  # Do not modify.
 
 ## Background
 
-With the rapid growth of enterprise business, a single Kubernetes cluster often cannot meet the demands of large-scale AI training and inference tasks. Users typically need to manage multiple Kubernetes clusters to achieve unified workload distribution, deployment, and management. Currently, multi-cluster orchestration systems in the industry (such as [Karmada](https://karmada.io/)) primarily target microservices scenarios, providing high availability and disaster recovery deployment capabilities. However, in terms of AI job scheduling, Karmada's capabilities are still limited. It lacks support for **Volcano Job** and cannot meet requirements such as queue management, multi-tenant fair scheduling, and job priority scheduling.
-
-To address the scheduling and management challenges of AI jobs in a multi-cluster environment, the **Volcano community** has incubated the **[Volcano Global](https://github.com/volcano-sh/volcano-global)** sub-project. Based on Karmada, this project extends Volcano's powerful scheduling capabilities in single clusters, providing a unified scheduling platform for multi-cluster AI jobs. It supports cross-cluster task distribution, resource management, and priority control.
+With the rapid growth of enterprise business, a single Kubernetes cluster often cannot meet the demands of large-scale AI training and inference tasks.
+Users typically need to manage multiple Kubernetes clusters to achieve unified AI workload distribution, deployment, and management. 
+Currently, many users are running Volcano across multiple clusters and using [Karmada](https://github.com/karmada-io/karmada) for management.
+To better support AI tasks in multi-cluster environments with features such as global queue management, task priority, and fair scheduling,
+the Volcano community has incubated the [Volcano Global](https://github.com/volcano-sh/volcano-global) sub-project.
+This project extends Volcano's scheduling capabilities from single clusters to multi-cluster scenarios, providing a unified scheduling platform for multi-cluster AI tasks.
+It supports cross-cluster task distribution, resource management, and priority control.
 
 ## Features
 

content/en/docs/v1-11-0/multi_cluster_scheduling.md

@@ -17,9 +17,13 @@ type = "docs"  # Do not modify.
 
 ## Background
 
-With the rapid growth of enterprise business, a single Kubernetes cluster often cannot meet the demands of large-scale AI training and inference tasks. Users typically need to manage multiple Kubernetes clusters to achieve unified workload distribution, deployment, and management. Currently, multi-cluster orchestration systems in the industry (such as [Karmada](https://karmada.io/)) primarily target microservices scenarios, providing high availability and disaster recovery deployment capabilities. However, in terms of AI job scheduling, Karmada's capabilities are still limited. It lacks support for **Volcano Job** and cannot meet requirements such as queue management, multi-tenant fair scheduling, and job priority scheduling.
-
-To address the scheduling and management challenges of AI jobs in a multi-cluster environment, the **Volcano community** has incubated the **[Volcano Global](https://github.com/volcano-sh/volcano-global)** sub-project. Based on Karmada, this project extends Volcano's powerful scheduling capabilities in single clusters, providing a unified scheduling platform for multi-cluster AI jobs. It supports cross-cluster task distribution, resource management, and priority control.
+With the rapid growth of enterprise business, a single Kubernetes cluster often cannot meet the demands of large-scale AI training and inference tasks. 
+Users typically need to manage multiple Kubernetes clusters to achieve unified AI workload distribution, deployment, and management. 
+Currently, many users are running Volcano across multiple clusters and using [Karmada](https://github.com/karmada-io/karmada) for management. 
+To better support AI tasks in multi-cluster environments with features such as global queue management, task priority, and fair scheduling, 
+the Volcano community has incubated the [Volcano Global](https://github.com/volcano-sh/volcano-global) sub-project. 
+This project extends Volcano's scheduling capabilities from single clusters to multi-cluster scenarios, providing a unified scheduling platform for multi-cluster AI tasks. 
+It supports cross-cluster task distribution, resource management, and priority control.
 
 ## Features
 

content/en/docs/v1-12-0/multi_cluster_scheduling.md

@@ -17,9 +17,13 @@ type = "docs"  # Do not modify.
 
 ## Background
 
-With the rapid growth of enterprise business, a single Kubernetes cluster often cannot meet the demands of large-scale AI training and inference tasks. Users typically need to manage multiple Kubernetes clusters to achieve unified workload distribution, deployment, and management. Currently, multi-cluster orchestration systems in the industry (such as [Karmada](https://karmada.io/)) primarily target microservices scenarios, providing high availability and disaster recovery deployment capabilities. However, in terms of AI job scheduling, Karmada's capabilities are still limited. It lacks support for **Volcano Job** and cannot meet requirements such as queue management, multi-tenant fair scheduling, and job priority scheduling.
-
-To address the scheduling and management challenges of AI jobs in a multi-cluster environment, the **Volcano community** has incubated the **[Volcano Global](https://github.com/volcano-sh/volcano-global)** sub-project. Based on Karmada, this project extends Volcano's powerful scheduling capabilities in single clusters, providing a unified scheduling platform for multi-cluster AI jobs. It supports cross-cluster task distribution, resource management, and priority control.
+With the rapid growth of enterprise business, a single Kubernetes cluster often cannot meet the demands of large-scale AI training and inference tasks.
+Users typically need to manage multiple Kubernetes clusters to achieve unified AI workload distribution, deployment, and management. 
+Currently, many users are running Volcano across multiple clusters and using [Karmada](https://github.com/karmada-io/karmada) for management.
+To better support AI tasks in multi-cluster environments with features such as global queue management, task priority, and fair scheduling,
+the Volcano community has incubated the [Volcano Global](https://github.com/volcano-sh/volcano-global) sub-project.
+This project extends Volcano's scheduling capabilities from single clusters to multi-cluster scenarios, providing a unified scheduling platform for multi-cluster AI tasks.
+It supports cross-cluster task distribution, resource management, and priority control.
 
 ## Features
 

content/zh/docs/multi_cluster_scheduling.md

@@ -17,9 +17,9 @@ type = "docs"  # Do not modify.
 
 ## 背景
 
-随着企业业务的快速增长，单一Kubernetes集群往往无法满足大规模AI训练和推理任务的需求。用户通常需要管理多个Kubernetes集群，以实现工作负载的统一分发、部署和管理。目前，业界的多集群编排系统（如[Karmada](https://karmada.io/)）主要针对微服务场景，提供了高可用性和容灾部署能力。然而，在AI作业调度方面，Karmada的能力仍然有限，缺乏对**Volcano Job**的支持，也无法满足队列管理、多租户公平调度和作业优先级调度等需求。
-
-为了解决多集群环境下AI作业的调度与管理问题，**Volcano社区**孵化了**[Volcano Global](https://github.com/volcano-sh/volcano-global)**子项目。该项目基于Karmada，扩展了Volcano在单集群中的强大调度能力，为多集群AI作业提供了统一的调度平台，支持跨集群的任务分发、资源管理和优先级控制。
+随着企业业务的快速增长，单个 Kubernetes 集群通常无法满足大规模 AI 训练和推理任务的需求。用户通常需要管理多个 Kubernetes 集群，以实现统一的AI工作负载分发、部署和管理。
+目前，已经有许多用户在多个集群中使用 Volcano，并使用 [Karmada](https://github.com/karmada-io/karmada) 进行管理。为了更好地支持多集群环境中的 AI 任务，支持全局队列管理、任务优先级和公平调度等功能，Volcano 社区孵化了[Volcano Global](https://github.com/volcano-sh/volcano-global)子项目。
+该项目将 Volcano 在单个集群中的强大调度能力扩展到多集群场景，为多集群 AI 任务提供统一的调度平台，支持跨集群任务分发、资源管理和优先级控制。
 
 ## 功能
 

content/zh/docs/v1-11-0/multi_cluster_scheduling.md

@@ -17,9 +17,9 @@ type = "docs"  # Do not modify.
 
 ## 背景
 
-随着企业业务的快速增长，单一Kubernetes集群往往无法满足大规模AI训练和推理任务的需求。用户通常需要管理多个Kubernetes集群，以实现工作负载的统一分发、部署和管理。目前，业界的多集群编排系统（如[Karmada](https://karmada.io/)）主要针对微服务场景，提供了高可用性和容灾部署能力。然而，在AI作业调度方面，Karmada的能力仍然有限，缺乏对**Volcano Job**的支持，也无法满足队列管理、多租户公平调度和作业优先级调度等需求。
-
-为了解决多集群环境下AI作业的调度与管理问题，**Volcano社区**孵化了**[Volcano Global](https://github.com/volcano-sh/volcano-global)**子项目。该项目基于Karmada，扩展了Volcano在单集群中的强大调度能力，为多集群AI作业提供了统一的调度平台，支持跨集群的任务分发、资源管理和优先级控制。
+随着企业业务的快速增长，单个 Kubernetes 集群通常无法满足大规模 AI 训练和推理任务的需求。用户通常需要管理多个 Kubernetes 集群，以实现统一的AI工作负载分发、部署和管理。
+目前，已经有许多用户在多个集群中使用 Volcano，并使用 [Karmada](https://github.com/karmada-io/karmada) 进行管理。为了更好地支持多集群环境中的 AI 任务，支持全局队列管理、任务优先级和公平调度等功能，Volcano 社区孵化了[Volcano Global](https://github.com/volcano-sh/volcano-global)子项目。
+该项目将 Volcano 在单个集群中的强大调度能力扩展到多集群场景，为多集群 AI 任务提供统一的调度平台，支持跨集群任务分发、资源管理和优先级控制。
 
 ## 功能
 

content/zh/docs/v1-12-0/multi_cluster_scheduling.md

@@ -17,9 +17,9 @@ type = "docs"  # Do not modify.
 
 ## 背景
 
-随着企业业务的快速增长，单一Kubernetes集群往往无法满足大规模AI训练和推理任务的需求。用户通常需要管理多个Kubernetes集群，以实现工作负载的统一分发、部署和管理。目前，业界的多集群编排系统（如[Karmada](https://karmada.io/)）主要针对微服务场景，提供了高可用性和容灾部署能力。然而，在AI作业调度方面，Karmada的能力仍然有限，缺乏对**Volcano Job**的支持，也无法满足队列管理、多租户公平调度和作业优先级调度等需求。
-
-为了解决多集群环境下AI作业的调度与管理问题，**Volcano社区**孵化了**[Volcano Global](https://github.com/volcano-sh/volcano-global)**子项目。该项目基于Karmada，扩展了Volcano在单集群中的强大调度能力，为多集群AI作业提供了统一的调度平台，支持跨集群的任务分发、资源管理和优先级控制。
+随着企业业务的快速增长，单个 Kubernetes 集群通常无法满足大规模 AI 训练和推理任务的需求。用户通常需要管理多个 Kubernetes 集群，以实现统一的AI工作负载分发、部署和管理。
+目前，已经有许多用户在多个集群中使用 Volcano，并使用 [Karmada](https://github.com/karmada-io/karmada) 进行管理。为了更好地支持多集群环境中的 AI 任务，支持全局队列管理、任务优先级和公平调度等功能，Volcano 社区孵化了[Volcano Global](https://github.com/volcano-sh/volcano-global)子项目。
+该项目将 Volcano 在单个集群中的强大调度能力扩展到多集群场景，为多集群 AI 任务提供统一的调度平台，支持跨集群任务分发、资源管理和优先级控制。
 
 ## 功能