merge: Migrate to flow-based simulation for low-level models

This pull request converts the `opendc-simulator-resources` module into a flow
simulator and adapts the existing low-level models (e.g., CPU, network, disk)
to this new flow simulator.

The flow simulator works differently from the uniform resource consumption
model, in that it models flow through a system of connections, as opposed to
resource consumptions. Concretely, this means that while in the uniform
resource consumption model, consumptions with the same usage are propagated to
the resources, in the flow simulator, only changes to the flow in the system
are propagated. 

Overall, this leads to less updates in the system and therefore higher
performance. The benchmarks shows that the new implementation obtains more than
double the performance of the old implementation. We have focused in the new
implementation on reducing the amount of work and memory
allocations/loads/stores per updates.

* Migrate from kotlinx-benchmark to jmh-gradle (for better profiling support)
* Use longer traces for benchmarks (to prevent measuring the benchmark
overhead)
* Use direct field access for perf-sensitive code
* Combine work and deadline to duration
* Add support for pushing flow from context (to eliminate the allocation for
every `SimResourceCommand`)
* Reduce memory allocations in SimResourceInterpreter, by revamping the way
timers are allocated.
* Simplify max-min aggregator implementation (by utilizing the new push
mechanism) 
* Invoke consumer callback on every invalidation (in order to propagate changes
downstream)
* Lazily push changes to resource context (by not updating the flow rate
immediately after a push, but only after an update)
* Remove onUpdate callback 
* Merge distributor and aggregator into switch 
* Separate push and pull flags 
* Remove failure callback from FlowSource 
* Create separate callbacks for remaining events 
* Make convergence callback optional
* Reduce field accesses in FlowConsumerContextImpl
* Optimize hot path in SimTraceWorkload
* Expose CPU time counters directly on hypervisor 
* Optimize telemetry collection

**Breaking API Changes**

* The entire `opendc-simulator-resources` module has been replaced by the
`opendc-simulator-flow` module.
* `SimHypervisor.Listener` has been removed in favour of a new interface that
exposes the performance counters of the hypervisor directly. To listen for
convergence, use `FlowConvergenceListener`.

1 files changed, 140 insertions, 0 deletions

diff --git a/opendc-simulator/opendc-simulator-flow/src/jmh/kotlin/org/opendc/simulator/flow/FlowBenchmarks.kt b/opendc-simulator/opendc-simulator-flow/src/jmh/kotlin/org/opendc/simulator/flow/FlowBenchmarks.kt
new file mode 100644
index 00000000..e927f81d
--- /dev/null
+++ b/opendc-simulator/opendc-simulator-flow/src/jmh/kotlin/org/opendc/simulator/flow/FlowBenchmarks.kt
@@ -0,0 +1,140 @@
+/*
+ * Copyright (c) 2021 AtLarge Research
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to deal
+ * in the Software without restriction, including without limitation the rights
+ * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+ * copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+
+package org.opendc.simulator.flow
+
+import kotlinx.coroutines.ExperimentalCoroutinesApi
+import kotlinx.coroutines.launch
+import org.opendc.simulator.core.SimulationCoroutineScope
+import org.opendc.simulator.core.runBlockingSimulation
+import org.opendc.simulator.flow.mux.ForwardingFlowMultiplexer
+import org.opendc.simulator.flow.mux.MaxMinFlowMultiplexer
+import org.opendc.simulator.flow.source.TraceFlowSource
+import org.openjdk.jmh.annotations.*
+import java.util.concurrent.ThreadLocalRandom
+import java.util.concurrent.TimeUnit
+
+@State(Scope.Thread)
+@Fork(1)
+@Warmup(iterations = 2, time = 1, timeUnit = TimeUnit.SECONDS)
+@Measurement(iterations = 5, time = 3, timeUnit = TimeUnit.SECONDS)
+@OptIn(ExperimentalCoroutinesApi::class)
+class FlowBenchmarks {
+    private lateinit var scope: SimulationCoroutineScope
+    private lateinit var engine: FlowEngine
+
+    @Setup
+    fun setUp() {
+        scope = SimulationCoroutineScope()
+        engine = FlowEngine(scope.coroutineContext, scope.clock)
+    }
+
+    @State(Scope.Thread)
+    class Workload {
+        lateinit var trace: Sequence<TraceFlowSource.Fragment>
+
+        @Setup
+        fun setUp() {
+            val random = ThreadLocalRandom.current()
+            val entries = List(10000) { TraceFlowSource.Fragment(1000, random.nextDouble(0.0, 4500.0)) }
+            trace = entries.asSequence()
+        }
+    }
+
+    @Benchmark
+    fun benchmarkSink(state: Workload) {
+        return scope.runBlockingSimulation {
+            val provider = FlowSink(engine, 4200.0)
+            return@runBlockingSimulation provider.consume(TraceFlowSource(state.trace))
+        }
+    }
+
+    @Benchmark
+    fun benchmarkForward(state: Workload) {
+        return scope.runBlockingSimulation {
+            val provider = FlowSink(engine, 4200.0)
+            val forwarder = FlowForwarder(engine)
+            provider.startConsumer(forwarder)
+            return@runBlockingSimulation forwarder.consume(TraceFlowSource(state.trace))
+        }
+    }
+
+    @Benchmark
+    fun benchmarkMuxMaxMinSingleSource(state: Workload) {
+        return scope.runBlockingSimulation {
+            val switch = MaxMinFlowMultiplexer(engine)
+
+            FlowSink(engine, 3000.0).startConsumer(switch.newOutput())
+            FlowSink(engine, 3000.0).startConsumer(switch.newOutput())
+
+            val provider = switch.newInput()
+            return@runBlockingSimulation provider.consume(TraceFlowSource(state.trace))
+        }
+    }
+
+    @Benchmark
+    fun benchmarkMuxMaxMinTripleSource(state: Workload) {
+        return scope.runBlockingSimulation {
+            val switch = MaxMinFlowMultiplexer(engine)
+
+            FlowSink(engine, 3000.0).startConsumer(switch.newOutput())
+            FlowSink(engine, 3000.0).startConsumer(switch.newOutput())
+
+            repeat(3) {
+                launch {
+                    val provider = switch.newInput()
+                    provider.consume(TraceFlowSource(state.trace))
+                }
+            }
+        }
+    }
+
+    @Benchmark
+    fun benchmarkMuxExclusiveSingleSource(state: Workload) {
+        return scope.runBlockingSimulation {
+            val switch = ForwardingFlowMultiplexer(engine)
+
+            FlowSink(engine, 3000.0).startConsumer(switch.newOutput())
+            FlowSink(engine, 3000.0).startConsumer(switch.newOutput())
+
+            val provider = switch.newInput()
+            return@runBlockingSimulation provider.consume(TraceFlowSource(state.trace))
+        }
+    }
+
+    @Benchmark
+    fun benchmarkMuxExclusiveTripleSource(state: Workload) {
+        return scope.runBlockingSimulation {
+            val switch = ForwardingFlowMultiplexer(engine)
+
+            FlowSink(engine, 3000.0).startConsumer(switch.newOutput())
+            FlowSink(engine, 3000.0).startConsumer(switch.newOutput())
+
+            repeat(2) {
+                launch {
+                    val provider = switch.newInput()
+                    provider.consume(TraceFlowSource(state.trace))
+                }
+            }
+        }
+    }
+}