diff --git a/_benchmark/user-guide/contributing-workloads.md b/_benchmark/user-guide/contributing-workloads.md
new file mode 100644
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+---
+layout: default
+title: Sharing custom workloads
+nav_order: 11
+parent: User guide
+---
+
+# Sharing custom workloads
+
+You can share a custom workload with other OpenSearch users by uploading it to the [workloads repository](https://github.com/opensearch-project/opensearch-benchmark-workloads/) on GitHub. 
+
+Make sure that any data included in the workload's dataset does not contain proprietary data or personally identifiable information (PII). 
+
+To share a custom workload, follow these steps.
+
+## Create a README.md
+
+Provide a detailed `README.MD` file that includes the following:  
+
+- The purpose of the workload. When creating a description for the workload, consider its specific use and how the that use case differs from others in the [workloads repository](https://github.com/opensearch-project/opensearch-benchmark-workloads/).
+- An example document from the dataset that helps users understand the data's structure.
+- The workload parameters that can be used to customize the workload.
+- A list of default test procedures included in the workload as well as other test procedures that the workload can run.
+- An output sample produced by the workload after a test is run.
+- A copy of the open-source license that gives the user and OpenSearch Benchmark permission to use the dataset.
+
+For an example workload README file, go to the `http_logs` [README](https://github.com/opensearch-project/opensearch-benchmark-workloads/blob/main/http_logs/README.md).
+
+## Verify the workload's structure
+
+The workload must include the following files: 
+
+- `workload.json`
+- `index.json`
+- `files.txt`
+- `test_procedures/default.json`
+- `operations/default.json` 
+
+Both `default.json` file names can be customized to have a descriptive name. The workload can include an optional `workload.py` file to add more dynamic functionality. For more information about a file's contents, go to [Anatomy of a workload]({{site.url}}{{site.baseurl}}/benchmark/user-guide/understanding-workloads/anatomy-of-a-workload/).
+
+## Testing the workload
+
+All workloads contributed to OpenSearch Benchmark must fulfill the following testing requirements: 
+
+- All tests run to explore and produce an example from the workload must target an OpenSearch cluster.
+- The workload must pass all integration tests. Follow these steps to ensure that the workload passes the integration tests:
+   1. Add the workload to your forked copy of the [workloads repository](https://github.com/opensearch-project/opensearch-benchmark-workloads/). Make sure that you've forked both the `opensearch-benchmark-workloads` repository and the [OpenSeach Benchmark](https://github.com/opensearch-project/opensearch-benchmark) repository.
+   3. In your forked OpenSearch Benchmark repository, update the `benchmark-os-it.ini` and `benchmark-in-memory.ini` files in the `/osbenchmark/it/resources` directory to point to the forked workloads repository containing your workload.
+   4. After you've modified the `.ini` files, commit your changes to a branch for testing.
+   6. Run your integration tests using GitHub actions by selecting the branch for which you committed your changes. Verify that the tests have run as expected.
+   7. If your integration tests run as expected, go to your forked workloads repository and merge your workload changes into branches `1` and `2`. This allows for your workload to appear in both major versions of OpenSearch Benchmark.
+
+## Create a PR
+
+After testing the workload, create a pull request (PR) from your fork to the `opensearch-project` [workloads repository](https://github.com/opensearch-project/opensearch-benchmark-workloads/). Add a sample output and summary result to the PR description. The OpenSearch Benchmark maintainers will review the PR.
+
+Once the PR is approved, you must share the data corpora of your dataset. The OpenSearch Benchmark team can then add the dataset to a shared S3 bucket. If your data corpora is stored in an S3 bucket, you can use [AWS DataSync](https://docs.aws.amazon.com/datasync/latest/userguide/create-s3-location.html) to share the data corpora. Otherwise, you must inform the maintainers of where the data corpora resides.
diff --git a/_benchmark/user-guide/distributed-load.md b/_benchmark/user-guide/distributed-load.md
index ec46091974..60fc98500f 100644
--- a/_benchmark/user-guide/distributed-load.md
+++ b/_benchmark/user-guide/distributed-load.md
@@ -1,7 +1,7 @@
 ---
 layout: default
 title: Running distributed loads
-nav_order: 10
+nav_order: 15
 parent: User guide
 ---
 
diff --git a/_benchmark/user-guide/telemetry.md b/_benchmark/user-guide/telemetry.md
index 7cc7f6b730..d4c40c790a 100644
--- a/_benchmark/user-guide/telemetry.md
+++ b/_benchmark/user-guide/telemetry.md
@@ -1,7 +1,7 @@
 ---
 layout: default
 title: Enabling telemetry devices
-nav_order: 15
+nav_order: 30
 parent: User guide
 ---