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* removed experiment-compute and integrated all components into opendc-compute
* updated workflow gradle file
* removed unneeded code
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This change integrates the classes from the old
`opendc-compute-workload` module into the `opendc-experiments-compute`
module. This new module contains helper classes for setting up
experiments with the OpenDC compute service.
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This change removes the OpenTelemetry integration from the OpenDC
Compute modules. Previously, we chose to integrate OpenTelemetry to
provide a unified way to report metrics to the users.
Although this worked as expected, the overhead of the OpenTelemetry when
collecting metrics during simulation was considerable and lacked more
optimization opportunities (other than providing a separate API
implementation). Furthermore, since we were tied to OpenTelemetry's SDK
implementation, we experienced issues with throttling and registering
multiple instruments.
We will instead use another approach, where we expose the core metrics
in OpenDC via specialized interfaces (see the commits before) such that
access is fast and can be done without having to interface with
OpenTelemetry. In addition, we will provide an adapter to that is able
to forward these metrics to OpenTelemetry implementations, so we can
still integrate with the wider ecosystem.
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This change updates the `ParquetDataWriter` class to not use the
`parquet-avro` library for exporting experiment data, but instead to use
the low-level APIs to directly write the data in Parquet format.
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This change updates the compute support library to load the VM
interference model via the OpenDC trace library, which provides a
generic interface for reading interference models associated with
workload traces.
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This change redesigns the virtual machine interference algorithm to have
a fixed memory usage per `VmInterferenceModel` instance. Previously, for
every interference domain, a copy of the model would be created, leading
to OutOfMemory errors when running multiple experiments at the same
time.
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This change creates a new module for doing simulations with virtual
machine workloads. We have found that a lot of code in the Capelin
experiments code is being re-used by non-experiment modules.
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