update application-failover.md

Signed-off-by: zhzhuang-zju <[email protected]>

docs/userguide/failover/application-failover.md

@@ -190,12 +190,12 @@ spec:
 
 You can edit `suppressDeletion` to false in `gracefulEvictionTasks` to evict the application in the failed cluster after you confirm the failure.
 
-## Stateful Application Failover Support
-
-Starting from v1.12, the application-level failover feature adds support for stateful application failover, it provides a generalized way for users to define application state preservation in the context of cluster-to-cluster failovers.
+## Application State Preservation
 
 In releases prior to v1.12, Karmada’s scheduling logic runs on the assumption that resources that are scheduled and rescheduled are stateless. In some cases, users may desire to conserve a certain state so that applications can resume from where they left off in the previous cluster. For CRDs dealing with data-processing (such as Flink or Spark), it can be particularly useful to restart applications from a previous checkpoint. That way applications can seamlessly resume processing data while avoiding double processing.
 
+To meet the aforementioned requirements, starting from v1.12, the application-level failover has introduced a mechanism for preserving application state, providing users with a general way to define application state preservation in the context of of cluster-to-cluster failovers.
+
 ### Defining StatePreservation
 
 `StatePreservation` is a field under `.spec.failover.application`, it defines the policy for preserving and restoring state data during failover events for stateful applications. When an application fails over from one cluster to another, this policy enables the extraction of critical data from the original resource configuration.

i18n/zh/docusaurus-plugin-content-docs/current/userguide/failover/application-failover.md

@@ -192,11 +192,9 @@ spec:
 
 您可以在 gracefulEvictionTasks 中将 suppressDeletion 修改为 false，确认故障后驱逐故障集群中的应用。
 
-## 无状态应用 Failover 支持
+## 应用状态保留
 
-从 v1.12 开始，应用故障转移特性增加了对有状态应用故障转移的支持，它为用户提供了一种通用的方式来定义在集群间故障转移情境下的应用状态保留。
-
-在 v1.12 之前的版本中，Karmada 的调度逻辑基于这样的假设运行：被调度和重新调度的资源是无状态的。在某些情况下，用户可能希望保留某种状态，以便应用可以从之前集群中停止的地方恢复。对于处理数据加工的 CRD（例如 Flink 或 Spark），从之前的检查点重新启动应用可能特别有用。这样应用可以无缝地恢复数据处理，同时避免重复处理。
+在 v1.12 之前的版本中，Karmada 的调度逻辑基于这样的假设运行：被调度和重新调度的资源是无状态的。在某些情况下，用户可能希望保留某种状态，以便应用可以从之前集群中停止的地方恢复。对于处理数据加工的 CRD（例如 Flink 或 Spark），从之前的检查点重新启动应用可能特别有用。这样应用可以无缝地恢复数据处理，同时避免重复处理。为满足以上诉求，从 v1.12 开始，应用级故障迁移新增应用状态保留机制，为用户提供了一种通用的方式来定义在集群间故障转移情境下的应用状态保留。
 
 ### 定义 StatePreservation