Add model auto redeploy tutorial

docs/tutorials/model_auto_redeploy.md

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+# Topic
+This doc explains how to use model auto redeploy feature in ml-commons(This doc works for OpenSearch 2.8+).
+
+# Background
+After ml-commons support serving model inside OpenSearch cluster, we need to take care the node failure case for the 
+deployed models, this can save user's effort to maintain the model's availability. For example, once a node is down
+caused by arbitrary reason and then restarted either by script or manually, if there isn't the model auto redeploy 
+feature, the model runs on a smaller cluster(expected nodes - 1) which could cause more nodes failure since each working
+node is handling more traffic than it expected. To address this we introduced model auto redeploy feature and enabling this
+feature is pretty simple.
+
+# Enable model auto redeploy
+There are several configurations to control the model auto redeploy feature behavior including: 
+### plugins.ml_commons.model_auto_redeploy.enable
+The default value of this configuration is false, value range is: [true, false], once it's set to true, 
+it means the model auto redeploy feature is enabled.
+### plugins.ml_commons.model_auto_redeploy.lifetime_retry_times
+This configuration means how many auto redeploy failure we can tolerate, value range is: [0, Integer.MAX_VALUE],
+default value is 3 which means once 3 times of `failure auto redeploy` reached, the system will not retry auto 
+redeploy anymore. But once auto redeploy is successful, the retry time will be reset to 0. 
+### plugins.ml_commons.model_auto_redeploy_success_ratio
+This configuration means how to determine if an auto redeployment is success or not. Since node failure is random, we can't
+make sure model auto redeploy can be successful at any time in a cluster, so if most of the expected working nodes have
+been successfully redeployed the model, the retry is considered successful. The value range is: [0, 1], and the default 
+value of this is 0.8 which means if 80% greater or equals 80% nodes successfully redeployed a model, that model's auto 
+redeployment is success.
+
+# Limitation
+The auto redeployment of models is designed to handle all cases involving node failures, but it does have its limitations. 
+Under the hood, ml-commons uses a cron job to sync up the status of all models in a cluster. 
+This cron job checks all the failed nodes and removes them from the internal model routing 
+table (a mapping between model ID and operational node IDs) to ensure that requests won't be dispatched to 
+crashed nodes.
+
+However, there's one scenario that model auto redeployment cannot handle, which is a complete cluster restart. 
+In this situation, if all nodes are shut down, the last live node's cron job will detect that all the models 
+are not functioning correctly and update the models' status to DEPLOY_FAILED. 
+The model auto redeploy won't check this status because it's not a valid redeployment status. 
+In this case, the user will have to invoke the model deploy/load API.
+
+For cases where only some nodes crash, once the plugins.ml_commons.model_auto_redeploy.enable configuration 
+is set to true, the models will automatically redeploy on those crashed nodes.
+
+# Example
+An example to enable the model auto redeploy feature is via changing the configuration like below:
+```
+PUT /_cluster/settings
+{
+    "persistent" : {
+        "plugins.ml_commons.model_auto_redeploy.enable" : true 
+  }
+}
+```
+One can also change other two configuration to get desire behavior like below:
+```
+Changes the life-time retry times to 10:
+PUT /_cluster/settings
+{
+    "persistent" : {
+        "plugins.ml_commons.model_auto_redeploy.lifetime_retry_times" : 10 
+  }
+}
+
+Change the determination of success to 70% expected work nodes in the cluster:
+PUT /_cluster/settings
+{
+    "persistent" : {
+        "plugins.ml_commons.model_auto_redeploy.lifetime_retry_times" : 10 
+  }
+}
+```
+