bokeh and tornado: KeyError: 'transfer'

**Describe the issue**:
When accessing the dashboard of the scheduler, I am not able to be redirected to the status tab. It always says `500: Internal Server Error`. The rest of the tabs like workers, profile, etc. are working. The interface is also a bit more sluggish.

Accessing the status tab results in the following log:
```log
2022-10-12 14:36:27,136 - bokeh.core.property.validation - ERROR - 'transfer'
Traceback (most recent call last):
File "/opt/conda/lib/python3.9/site-packages/distributed/utils.py", line 748, in wrapper
return func(*args, **kwargs)
File "/opt/conda/lib/python3.9/site-packages/distributed/dashboard/components/scheduler.py", line 713, in update
transfer_incoming_bytes = [
File "/opt/conda/lib/python3.9/site-packages/distributed/dashboard/components/scheduler.py", line 714, in
ws.metrics["transfer"]["incoming_bytes"] for ws in wss
KeyError: 'transfer'
2022-10-12 14:36:27,136 - bokeh.application.handlers.function - ERROR - 'transfer'
Traceback (most recent call last):
File "/opt/conda/lib/python3.9/site-packages/distributed/utils.py", line 748, in wrapper
return func(*args, **kwargs)
File "/opt/conda/lib/python3.9/site-packages/distributed/dashboard/components/scheduler.py", line 4252, in status_doc
workers_transfer_bytes.update()
File "/opt/conda/lib/python3.9/site-packages/bokeh/core/property/validation.py", line 95, in func
return input_function(*args, **kwargs)
File "/opt/conda/lib/python3.9/site-packages/distributed/utils.py", line 748, in wrapper
return func(*args, **kwargs)
File "/opt/conda/lib/python3.9/site-packages/distributed/dashboard/components/scheduler.py", line 713, in update
transfer_incoming_bytes = [
File "/opt/conda/lib/python3.9/site-packages/distributed/dashboard/components/scheduler.py", line 714, in
ws.metrics["transfer"]["incoming_bytes"] for ws in wss
KeyError: 'transfer'
2022-10-12 14:36:27,137 - tornado.application - ERROR - Uncaught exception GET /status (172.18.0.5)
HTTPServerRequest(protocol='http', host='', method='GET', uri='/status', version='HTTP/1.1', remote_ip='172.18.0.5')
Traceback (most recent call last):
File "/opt/conda/lib/python3.9/site-packages/tornado/web.py", line 1704, in _execute
result = await result
File "/opt/conda/lib/python3.9/site-packages/bokeh/server/views/doc_handler.py", line 54, in get
h.modify_document(doc)
File "/opt/conda/lib/python3.9/site-packages/bokeh/application/handlers/function.py", line 143, in modify_document
self._func(doc)
File "/opt/conda/lib/python3.9/site-packages/distributed/utils.py", line 748, in wrapper
return func(*args, **kwargs)
File "/opt/conda/lib/python3.9/site-packages/distributed/dashboard/components/scheduler.py", line 4252, in status_doc
workers_transfer_bytes.update()
File "/opt/conda/lib/python3.9/site-packages/bokeh/core/property/validation.py", line 95, in func
return input_function(*args, **kwargs)
File "/opt/conda/lib/python3.9/site-packages/distributed/utils.py", line 748, in wrapper
return func(*args, **kwargs)
File "/opt/conda/lib/python3.9/site-packages/distributed/dashboard/components/scheduler.py", line 713, in update
transfer_incoming_bytes = [
File "/opt/conda/lib/python3.9/site-packages/distributed/dashboard/components/scheduler.py", line 714, in
ws.metrics["transfer"]["incoming_bytes"] for ws in wss
KeyError: 'transfer'
```

The sluggish performance could be the result of the following error:
```
2022-10-12 14:43:24,068 - tornado.application - ERROR - Uncaught exception GET /workers/ws (172.18.0.5)
HTTPServerRequest(protocol='http', host='', method='GET', uri='/workers/ws', version='HTTP/1.1', remote_ip='172.18.0.5')
Traceback (most recent call last):
File "/opt/conda/lib/python3.9/site-packages/tornado/web.py", line 3173, in wrapper
return method(self, *args, **kwargs)
File "/opt/conda/lib/python3.9/site-packages/bokeh/server/views/ws.py", line 149, in open
raise ProtocolError("Token is expired.")
bokeh.protocol.exceptions.ProtocolError: Token is expired.
```

**Minimal Complete Verifiable Example**:

**Anything else we need to know?**:
I am running rapidsai containers to expose GPUs to the cluster.

**Environment**:

- Dask version: 2022.9.2
- Python version: 3.9
- Operating System: docker
- Install method (conda, pip, source): docker