merge: Implement multi-flow stages in simulator (#110)

This pull request introduces the new `flow2` multi-flow simulator into the OpenDC codebase
and adjust all existing modules to make use of this new simulator.

The new simulator models flow as a network of components, which can each receive flow
from (potentially) multiple other components. In the previous simulator, the framework itself
supported only single flows between components and required re-implementation of many
components to support multiplexing flows.

Initial benchmarks show performance improvements in the range 2x–4x for large scale experiments
such as the Capelin benchmarks.

## Implementation Notes :hammer_and_pick:

* Add support for multi-flow stages
* Support flow transformations
* Add forwarding flow multiplexer
* Expose metrics on FlowMultiplexer
* Re-implement network sim using flow2
* Re-implement power sim using flow2 
* Re-implement compute sim using flow2
* Optimize workload implementation of SimTrace 
* Remove old flow simulator 
* Add log4j-core dependency 

## External Dependencies :four_leaf_clover:

* N/A

## Breaking API Changes :warning:

* Removal of the `org.opendc.simulator.flow` package. You should now use
   the new flow simulator located in `org.opendc.simulator.flow2`.
* `PowerModel` interface is replaced by the `CpuPowerModel` interface.
* `PowerDriver` interface is replaced by the `SimPsu` and `SimPsuFactory` interfaces.
* Removal of `SimTraceWorkload`. Instead, create a workload from a `SimTrace` using 
  `createWorkload(offset)`.
* `ScalingGovernor` has been split in a `ScalingGovernor` and `ScalingGovernorFactory`.
* All modules in `opendc-simulator` are now written in Java. This means that default 
   parameters are not supported anymore for these modules.

2 files changed, 9 insertions, 9 deletions

diff --git a/opendc-experiments/opendc-experiments-tf20/src/test/kotlin/org/opendc/experiments/tf20/TensorFlowTest.kt b/opendc-experiments/opendc-experiments-tf20/src/test/kotlin/org/opendc/experiments/tf20/TensorFlowTest.kt
index c4698058..32f72686 100644
--- a/opendc-experiments/opendc-experiments-tf20/src/test/kotlin/org/opendc/experiments/tf20/TensorFlowTest.kt
+++ b/opendc-experiments/opendc-experiments-tf20/src/test/kotlin/org/opendc/experiments/tf20/TensorFlowTest.kt
@@ -29,7 +29,7 @@ import org.opendc.experiments.tf20.core.SimTFDevice
 import org.opendc.experiments.tf20.distribute.MirroredStrategy
 import org.opendc.experiments.tf20.distribute.OneDeviceStrategy
 import org.opendc.experiments.tf20.util.MLEnvironmentReader
-import org.opendc.simulator.compute.power.LinearPowerModel
+import org.opendc.simulator.compute.power.CpuPowerModels
 import org.opendc.simulator.kotlin.runSimulation
 import java.util.UUID
 
@@ -52,7 +52,7 @@ class TensorFlowTest {
             clock,
             def.model.cpus[0],
             def.model.memory[0],
-            LinearPowerModel(250.0, 60.0)
+            CpuPowerModels.linear(250.0, 60.0)
         )
         val strategy = OneDeviceStrategy(device)
         val batchSize = 32
@@ -87,7 +87,7 @@ class TensorFlowTest {
             clock,
             def.model.cpus[0],
             def.model.memory[0],
-            LinearPowerModel(250.0, 60.0)
+            CpuPowerModels.linear(250.0, 60.0)
         )
         val strategy = OneDeviceStrategy(device)
         val batchSize = 128
@@ -102,8 +102,8 @@ class TensorFlowTest {
 
         val stats = device.getDeviceStats()
         assertAll(
-            { assertEquals(176230322904, clock.millis()) },
-            { assertEquals(4.4057580726E10, stats.energyUsage) }
+            { assertEquals(176230328513, clock.millis()) },
+            { assertEquals(4.405758212825E10, stats.energyUsage) }
         )
     }
 
@@ -122,7 +122,7 @@ class TensorFlowTest {
             clock,
             def.model.cpus[0],
             def.model.memory[0],
-            LinearPowerModel(250.0, 60.0)
+            CpuPowerModels.linear(250.0, 60.0)
         )
 
         val deviceB = SimTFDevice(
@@ -132,7 +132,7 @@ class TensorFlowTest {
             clock,
             def.model.cpus[0],
             def.model.memory[0],
-            LinearPowerModel(250.0, 60.0)
+            CpuPowerModels.linear(250.0, 60.0)
         )
 
         val strategy = MirroredStrategy(listOf(deviceA, deviceB))
diff --git a/opendc-experiments/opendc-experiments-tf20/src/test/kotlin/org/opendc/experiments/tf20/core/SimTFDeviceTest.kt b/opendc-experiments/opendc-experiments-tf20/src/test/kotlin/org/opendc/experiments/tf20/core/SimTFDeviceTest.kt
index 85d63e9b..910cbcc9 100644
--- a/opendc-experiments/opendc-experiments-tf20/src/test/kotlin/org/opendc/experiments/tf20/core/SimTFDeviceTest.kt
+++ b/opendc-experiments/opendc-experiments-tf20/src/test/kotlin/org/opendc/experiments/tf20/core/SimTFDeviceTest.kt
@@ -30,7 +30,7 @@ import org.junit.jupiter.api.Test
 import org.opendc.simulator.compute.model.MemoryUnit
 import org.opendc.simulator.compute.model.ProcessingNode
 import org.opendc.simulator.compute.model.ProcessingUnit
-import org.opendc.simulator.compute.power.LinearPowerModel
+import org.opendc.simulator.compute.power.CpuPowerModels
 import org.opendc.simulator.kotlin.runSimulation
 import java.util.UUID
 
@@ -51,7 +51,7 @@ internal class SimTFDeviceTest {
             clock,
             pu,
             memory,
-            LinearPowerModel(250.0, 100.0)
+            CpuPowerModels.linear(250.0, 100.0)
         )
 
         // Load 1 GiB into GPU memory