mirror of
https://github.com/esiur/esiur-dotnet.git
synced 2026-06-13 14:38:43 +00:00
new tests
This commit is contained in:
@@ -0,0 +1,14 @@
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<Project Sdk="Microsoft.NET.Sdk">
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<PropertyGroup>
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<OutputType>Exe</OutputType>
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<TargetFramework>net10.0</TargetFramework>
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<ImplicitUsings>enable</ImplicitUsings>
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<Nullable>enable</Nullable>
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</PropertyGroup>
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<ItemGroup>
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<ProjectReference Include="..\..\..\Libraries\Esiur\Esiur.csproj" OutputItemType="Analyzer"/>
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</ItemGroup>
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</Project>
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@@ -0,0 +1,278 @@
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// ============================================================
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// Scalability Extension: Concurrent Attach Sweep
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// ------------------------------------------------------------
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// Extends Tests/Distribution/ConcurrentAttach with:
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// - Sweep over a wider range of concurrent request counts A.
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// - More rounds per A for confidence-interval reporting.
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// - Auto-stop when timeouts or failures appear (the
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// saturation signal for concurrent attach is different
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// from fan-out: it's *correctness* failure, not slowdown).
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// - Unified CSV output suitable for direct plotting.
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//
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// Server: re-use the existing
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// Tests/Distribution/ConcurrentAttach with --mode server.
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// Or run this binary with --mode both.
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// ------------------------------------------------------------
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// Usage:
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// dotnet run -- --mode both --resources 200 \
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// --a-values 10,25,50,100,250,500,1000,2000 \
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// --rounds 10 --timeout 10000
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// ============================================================
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using Esiur.Protocol;
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using Esiur.Resource;
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using Esiur.Stores;
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using Esiur.Tests.ConcurrentAttachSweep;
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using System.Data.Common;
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using System.Diagnostics;
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using System.Globalization;
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var mode = GetArg(args, "--mode", "both");
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var host = GetArg(args, "--host", "127.0.0.1");
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var port = int.Parse(GetArg(args, "--port", "10902"));
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var resources = int.Parse(GetArg(args, "--resources", "200"));
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var timeoutMs = int.Parse(GetArg(args, "--timeout", "10000"));
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var rounds = int.Parse(GetArg(args, "--rounds", "10"));
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var aValStr = GetArg(args, "--a-values", "10,25,50,100,250,500,1000,2000");
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var outCsv = GetArg(args, "--output", "concurrent_attach_sweep.csv");
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var aValues = aValStr.Split(',').Select(int.Parse).ToArray();
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var serverWh = new Warehouse();
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var clientWh = new Warehouse();
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// ----------------------------------------------------------------
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// SERVER SIDE
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// ----------------------------------------------------------------
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if (mode == "server" || mode == "both")
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{
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await serverWh.Put("sys", new MemoryStore());
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await serverWh.Put("sys/server", new EpServer() { Port = (ushort)port });
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for (int i = 0; i < resources; i++)
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{
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await serverWh.Put($"sys/sensor_{i}", new SensorResource { SensorId = i, Value = i });
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}
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await serverWh.Open();
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Console.WriteLine($"[Server-T3+] Ready: {resources} resources on port {port}");
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if (mode == "server")
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{
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Console.WriteLine("Press ENTER to stop.");
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Console.ReadLine();
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await serverWh.Close();
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return;
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}
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await Task.Delay(500);
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}
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// ----------------------------------------------------------------
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// CLIENT SIDE: sweep over A values
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// ----------------------------------------------------------------
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Console.WriteLine($"[Client-T3+] resources={resources} timeout={timeoutMs}ms rounds={rounds}");
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Console.WriteLine($"[Client-T3+] A values: {string.Join(",", aValues)}");
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var summary = new List<ASummary>();
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bool failureDetected = false;
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foreach (int A in aValues)
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{
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if (failureDetected)
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{
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Console.WriteLine($"\n[Client-T3+] A={A}: SKIPPED (failure at lower A)");
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continue;
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}
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Console.WriteLine($"\n[Client-T3+] === A={A} ===");
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var roundResults = new List<RoundResult>();
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for (int round = 0; round < rounds; round++)
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{
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var rng = new Random(round * 1000 + A);
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var targets = Enumerable.Range(0, A)
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.Select(_ => rng.Next(resources))
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.ToArray();
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long succeeded = 0, failed = 0, timedOut = 0;
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var latencies = new double[A];
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var roundSw = Stopwatch.StartNew();
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// One shared connection per round, matching the existing test methodology
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var connection = await clientWh.Get<EpConnection>($"ep://{host}:{port}");
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var tasks = targets.Select((resourceIdx, taskIdx) => Task.Run(async () =>
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{
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var sw = Stopwatch.StartNew();
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using var cts = new CancellationTokenSource(timeoutMs);
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try
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{
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var proxy = await connection.Get($"sys/sensor_{resourceIdx}");
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sw.Stop();
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latencies[taskIdx] = sw.Elapsed.TotalMilliseconds;
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if (proxy != null) Interlocked.Increment(ref succeeded);
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else Interlocked.Increment(ref failed);
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}
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catch (OperationCanceledException)
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{
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sw.Stop();
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latencies[taskIdx] = timeoutMs;
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Interlocked.Increment(ref timedOut);
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}
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catch
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{
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sw.Stop();
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latencies[taskIdx] = sw.Elapsed.TotalMilliseconds;
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Interlocked.Increment(ref failed);
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}
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})).ToArray();
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await Task.WhenAll(tasks);
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roundSw.Stop();
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var sorted = latencies.OrderBy(x => x).ToArray();
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int n = sorted.Length;
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var rr = new RoundResult
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{
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Round = round + 1,
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A = A,
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Succeeded = succeeded,
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Failed = failed,
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TimedOut = timedOut,
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WallMs = roundSw.Elapsed.TotalMilliseconds,
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P50 = sorted[Math.Min(n - 1, (int)(n * 0.50))],
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P95 = sorted[Math.Min(n - 1, (int)(n * 0.95))],
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P99 = sorted[Math.Min(n - 1, (int)(n * 0.99))],
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Max = sorted[n - 1],
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};
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roundResults.Add(rr);
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Console.WriteLine($" round {round + 1}/{rounds}: ok={succeeded}/{A} fail={failed} "
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+ $"timeout={timedOut} wall={rr.WallMs:F0}ms p50={rr.P50:F0} p99={rr.P99:F0}");
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// Round 1 of each A is conventionally excluded from latency
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// aggregation due to connection-establishment overhead (matches
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// the existing test methodology).
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GC.Collect();
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await Task.Delay(500);
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}
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var steady = roundResults.Skip(1).ToList(); // exclude round 1
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if (steady.Count == 0) steady = roundResults;
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var anyFailure = roundResults.Any(r => r.Failed > 0 || r.TimedOut > 0);
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var s = new ASummary
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{
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A = A,
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Rounds = roundResults.Count,
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AnyFailures = anyFailure,
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TotalSucceeded = roundResults.Sum(r => r.Succeeded),
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TotalFailed = roundResults.Sum(r => r.Failed),
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TotalTimedOut = roundResults.Sum(r => r.TimedOut),
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MeanP50 = steady.Average(r => r.P50),
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Ci95P50 = ConfidenceIntervalHalfWidth95(steady.Select(r => r.P50).ToArray()),
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MeanP99 = steady.Average(r => r.P99),
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Ci95P99 = ConfidenceIntervalHalfWidth95(steady.Select(r => r.P99).ToArray()),
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MeanWall = steady.Average(r => r.WallMs),
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Ci95Wall = ConfidenceIntervalHalfWidth95(steady.Select(r => r.WallMs).ToArray()),
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};
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summary.Add(s);
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Console.WriteLine($" [A={A}] SUMMARY: "
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+ $"p50={s.MeanP50:F1}±{s.Ci95P50:F1} "
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+ $"p99={s.MeanP99:F1}±{s.Ci95P99:F1} "
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+ $"wall={s.MeanWall:F0}±{s.Ci95Wall:F0}ms "
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+ $"failures={s.TotalFailed + s.TotalTimedOut}/{s.TotalSucceeded + s.TotalFailed + s.TotalTimedOut}");
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if (anyFailure)
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{
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Console.WriteLine($" [A={A}] *** FAILURE DETECTED: stopping sweep ***");
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failureDetected = true;
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}
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}
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// ----------------------------------------------------------------
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// CSV output
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// ----------------------------------------------------------------
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var sb = new System.Text.StringBuilder();
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sb.AppendLine("a,rounds,any_failures,total_succeeded,total_failed,total_timed_out," +
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"mean_p50_ms,ci95_p50,mean_p99_ms,ci95_p99,mean_wall_ms,ci95_wall");
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foreach (var r in summary)
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{
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sb.AppendLine(string.Create(CultureInfo.InvariantCulture,
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$"{r.A},{r.Rounds},{r.AnyFailures},{r.TotalSucceeded},{r.TotalFailed},{r.TotalTimedOut}," +
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$"{r.MeanP50:F2},{r.Ci95P50:F2},{r.MeanP99:F2},{r.Ci95P99:F2}," +
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$"{r.MeanWall:F2},{r.Ci95Wall:F2}"));
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}
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await File.WriteAllTextAsync(outCsv, sb.ToString());
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Console.WriteLine($"\n[Client-T3+] Results written to {outCsv}");
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if (mode == "server" || mode == "both") await serverWh.Close();
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if (mode == "client" || mode == "both") await clientWh.Close();
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// ----------------------------------------------------------------
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// Helpers
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// ----------------------------------------------------------------
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static double ConfidenceIntervalHalfWidth95(double[] xs)
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{
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int n = xs.Length;
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if (n < 2) return 0;
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double mean = xs.Average();
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double sumSq = xs.Sum(x => (x - mean) * (x - mean));
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double std = Math.Sqrt(sumSq / (n - 1));
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double sem = std / Math.Sqrt(n);
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double t = (n - 1) switch
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{
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1 => 12.706,
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2 => 4.303,
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3 => 3.182,
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4 => 2.776,
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5 => 2.571,
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6 => 2.447,
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7 => 2.365,
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8 => 2.306,
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9 => 2.262,
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_ => 1.960
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};
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return t * sem;
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}
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static string GetArg(string[] args, string key, string def)
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{
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int i = Array.IndexOf(args, key);
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return (i >= 0 && i + 1 < args.Length) ? args[i + 1] : def;
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}
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record RoundResult
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{
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public int Round;
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public int A;
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public long Succeeded;
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public long Failed;
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public long TimedOut;
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public double WallMs;
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public double P50;
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public double P95;
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public double P99;
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public double Max;
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}
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record ASummary
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{
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public int A;
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public int Rounds;
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public bool AnyFailures;
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public long TotalSucceeded;
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public long TotalFailed;
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public long TotalTimedOut;
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public double MeanP50;
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public double Ci95P50;
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public double MeanP99;
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public double Ci95P99;
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public double MeanWall;
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public double Ci95Wall;
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}
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@@ -0,0 +1,16 @@
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using System;
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using System.Collections.Generic;
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using System.Text;
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namespace Esiur.Tests.ConcurrentAttachSweep;
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using Esiur.Resource;
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[Resource]
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public partial class SensorResource : Resource
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{
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public int SensorId { get; set; }
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[Export]
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public double value;
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}
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@@ -0,0 +1,10 @@
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<Project Sdk="Microsoft.NET.Sdk">
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<PropertyGroup>
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<OutputType>Exe</OutputType>
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<TargetFramework>net10.0</TargetFramework>
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<ImplicitUsings>enable</ImplicitUsings>
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<Nullable>enable</Nullable>
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</PropertyGroup>
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</Project>
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@@ -0,0 +1 @@
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Console.WriteLine("Hello, World!");
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@@ -0,0 +1,14 @@
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<Project Sdk="Microsoft.NET.Sdk">
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|
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<PropertyGroup>
|
||||
<OutputType>Exe</OutputType>
|
||||
<TargetFramework>net10.0</TargetFramework>
|
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<ImplicitUsings>enable</ImplicitUsings>
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<Nullable>enable</Nullable>
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</PropertyGroup>
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<ItemGroup>
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<ProjectReference Include="..\..\..\..\..\Libraries\Esiur\Esiur.csproj" />
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</ItemGroup>
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</Project>
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@@ -0,0 +1,390 @@
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// ============================================================
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// Scalability Extension: Fan-Out — ORCHESTRATOR CLIENT
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// ------------------------------------------------------------
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// Drives a full sweep of subscriber counts N against a single
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// server instance. For each N value:
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// 1. Spawns N in-process subscriber tasks, each opening its
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// own EpConnection to the server.
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// 2. Each subscriber attaches to all M resources and counts
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// property-change notifications it receives over a fixed
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// measurement window.
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// 3. The orchestrator polls the server's sys/control resource
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// to capture server-side CPU during the window.
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// 4. Tears down all N subscribers and waits a settle interval
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// before the next sweep point.
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// 5. Repeats for `replications` rounds so the per-N mean and
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// 95% confidence interval can be computed.
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// 6. Auto-stops the sweep if either:
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// - mean per-subscriber rate drops below 10% of theoretical,
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// - or server CPU stays at >180% (>90% of 2 cores) for the
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// entire measurement window.
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//
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// Note on in-process vs separate processes: subscribers are
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// tasks within a single client process to keep the test self-
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// contained and avoid spawning N OS processes. Each task uses
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// its own EpConnection (TCP connection) to the server, so from
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// the server's perspective the load looks identical to N
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// distinct subscriber nodes for the property-propagation path.
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// The single-client-process design does mean that the client
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// host's CPU is shared across all subscribers; the orchestrator
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// records this too so degradation can be attributed correctly.
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// ------------------------------------------------------------
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// Usage:
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// dotnet run -- --host 127.0.0.1 --port 10900 --resources 100 \
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// --emit-interval-ms 50 --window-sec 60 \
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// --warmup-sec 5 --replications 3 \
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// --n-values 2,5,10,20,50,100,200,500
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// ============================================================
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using Esiur.Protocol;
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using Esiur.Resource;
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using System.Data.Common;
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using System.Diagnostics;
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using System.Globalization;
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var host = GetArg(args, "--host", "127.0.0.1");
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var port = int.Parse(GetArg(args, "--port", "10900"));
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var resources = int.Parse(GetArg(args, "--resources", "100"));
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var emitIntervalMs = int.Parse(GetArg(args, "--emit-interval-ms", "50"));
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var windowSec = int.Parse(GetArg(args, "--window-sec", "60"));
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var warmupSec = int.Parse(GetArg(args, "--warmup-sec", "5"));
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var settleSec = int.Parse(GetArg(args, "--settle-sec", "5"));
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var replications = int.Parse(GetArg(args, "--replications", "3"));
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var nValuesStr = GetArg(args, "--n-values", "2,5,10,20,50,100,200,500");
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var outputCsv = GetArg(args, "--output", "fanout_sweep_results.csv");
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var nValues = nValuesStr.Split(',').Select(int.Parse).ToArray();
|
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double theoreticalMaxRate = 1000.0 / emitIntervalMs * resources;
|
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double minAcceptableRate = theoreticalMaxRate * 0.10;
|
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|
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Console.WriteLine($"[Orchestrator] resources={resources} interval={emitIntervalMs}ms "
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+ $"window={windowSec}s replications={replications}");
|
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Console.WriteLine($"[Orchestrator] theoretical_max_per_subscriber_rate={theoreticalMaxRate:F0} notif/s");
|
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Console.WriteLine($"[Orchestrator] saturation_threshold={minAcceptableRate:F0} notif/s");
|
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Console.WriteLine($"[Orchestrator] N values: {string.Join(",", nValues)}");
|
||||
|
||||
// ----------------------------------------------------------------
|
||||
// Attach to the server's control resource once.
|
||||
// ----------------------------------------------------------------
|
||||
var controlWh = new Warehouse();
|
||||
dynamic? control = null;
|
||||
try
|
||||
{
|
||||
var controlConn = await controlWh.Get<EpConnection>($"ep://{host}:{port}");
|
||||
control = await controlConn.Get("sys/control");
|
||||
}
|
||||
catch (Exception ex)
|
||||
{
|
||||
Console.WriteLine($"[Orchestrator] WARNING: could not attach to sys/control: {ex.Message}");
|
||||
Console.WriteLine("[Orchestrator] Server CPU will be reported as N/A.");
|
||||
}
|
||||
|
||||
// ----------------------------------------------------------------
|
||||
// All sweep points x replications, with per-N early-stop logic.
|
||||
// ----------------------------------------------------------------
|
||||
var allResults = new List<SweepResult>();
|
||||
bool saturatedDetected = false;
|
||||
|
||||
foreach (int n in nValues)
|
||||
{
|
||||
if (saturatedDetected)
|
||||
{
|
||||
Console.WriteLine($"\n[Orchestrator] N={n}: SKIPPED (saturation reached at lower N)");
|
||||
continue;
|
||||
}
|
||||
|
||||
var perRepResults = new List<RepResult>();
|
||||
|
||||
for (int rep = 0; rep < replications; rep++)
|
||||
{
|
||||
Console.WriteLine($"\n[Orchestrator] === N={n} rep={rep + 1}/{replications} ===");
|
||||
|
||||
var subscribers = new SubscriberTask[n];
|
||||
var subscriberWhs = new Warehouse[n];
|
||||
|
||||
// ---------- spawn N subscribers ----------
|
||||
Console.WriteLine($"[Orchestrator] Spawning {n} subscribers...");
|
||||
var spawnSw = Stopwatch.StartNew();
|
||||
var spawnTasks = new Task<SubscriberTask?>[n];
|
||||
for (int i = 0; i < n; i++)
|
||||
{
|
||||
int captured = i;
|
||||
subscriberWhs[i] = new Warehouse();
|
||||
spawnTasks[i] = SpawnSubscriber(subscriberWhs[i], host, port, resources, captured);
|
||||
}
|
||||
|
||||
await Task.WhenAll(spawnTasks);
|
||||
|
||||
bool spawnFailed = false;
|
||||
for (int i = 0; i < n; i++)
|
||||
{
|
||||
if (spawnTasks[i].Result == null) { spawnFailed = true; break; }
|
||||
subscribers[i] = spawnTasks[i].Result!;
|
||||
}
|
||||
spawnSw.Stop();
|
||||
|
||||
if (spawnFailed)
|
||||
{
|
||||
Console.WriteLine($"[Orchestrator] N={n}: spawn failed; treating as saturation.");
|
||||
saturatedDetected = true;
|
||||
await TeardownAll(subscriberWhs);
|
||||
break;
|
||||
}
|
||||
Console.WriteLine($"[Orchestrator] All {n} subscribers attached in {spawnSw.Elapsed.TotalSeconds:F2}s");
|
||||
|
||||
// ---------- warmup ----------
|
||||
Console.WriteLine($"[Orchestrator] Warmup {warmupSec}s...");
|
||||
await Task.Delay(warmupSec * 1000);
|
||||
foreach (var s in subscribers) s.ResetCounters();
|
||||
|
||||
// ---------- measurement window with CPU sampling ----------
|
||||
Console.WriteLine($"[Orchestrator] Measurement window {windowSec}s...");
|
||||
var cpuSamples = new List<double>();
|
||||
var connSamples = new List<int>();
|
||||
var winSw = Stopwatch.StartNew();
|
||||
while (winSw.Elapsed.TotalSeconds < windowSec)
|
||||
{
|
||||
await Task.Delay(1000);
|
||||
if (control != null)
|
||||
{
|
||||
try
|
||||
{
|
||||
cpuSamples.Add((double)control.CpuPercent);
|
||||
connSamples.Add((int)control.ConnectedClients);
|
||||
}
|
||||
catch { /* control resource may not have current value yet */ }
|
||||
}
|
||||
}
|
||||
|
||||
double elapsedSec = winSw.Elapsed.TotalSeconds;
|
||||
|
||||
// ---------- collect per-subscriber counts ----------
|
||||
var perSubRates = new double[n];
|
||||
long totalReceived = 0;
|
||||
long totalLate = 0;
|
||||
for (int i = 0; i < n; i++)
|
||||
{
|
||||
perSubRates[i] = subscribers[i].Received / elapsedSec;
|
||||
totalReceived += subscribers[i].Received;
|
||||
totalLate += subscribers[i].LateDeliveries;
|
||||
}
|
||||
|
||||
double meanPerSub = perSubRates.Average();
|
||||
double stdPerSub = StdDev(perSubRates);
|
||||
double minPerSub = perSubRates.Min();
|
||||
double maxPerSub = perSubRates.Max();
|
||||
double aggregate = perSubRates.Sum();
|
||||
double avgServerCpu = cpuSamples.Count > 0 ? cpuSamples.Average() : double.NaN;
|
||||
double peakServerCpu = cpuSamples.Count > 0 ? cpuSamples.Max() : double.NaN;
|
||||
|
||||
Console.WriteLine($"[Orchestrator] N={n} rep={rep + 1}: "
|
||||
+ $"mean_per_sub={meanPerSub:F1}/s "
|
||||
+ $"aggregate={aggregate:F0}/s "
|
||||
+ $"late={totalLate} "
|
||||
+ $"server_cpu_avg={avgServerCpu:F1}% peak={peakServerCpu:F1}%");
|
||||
|
||||
perRepResults.Add(new RepResult
|
||||
{
|
||||
N = n,
|
||||
Rep = rep + 1,
|
||||
MeanPerSub = meanPerSub,
|
||||
StdPerSub = stdPerSub,
|
||||
MinPerSub = minPerSub,
|
||||
MaxPerSub = maxPerSub,
|
||||
Aggregate = aggregate,
|
||||
LateDeliveries = totalLate,
|
||||
ServerCpuAvg = avgServerCpu,
|
||||
ServerCpuPeak = peakServerCpu,
|
||||
});
|
||||
|
||||
// ---------- teardown ----------
|
||||
Console.WriteLine($"[Orchestrator] Tearing down {n} subscribers...");
|
||||
await TeardownAll(subscriberWhs);
|
||||
await Task.Delay(settleSec * 1000);
|
||||
}
|
||||
|
||||
// ---------- per-N aggregation ----------
|
||||
if (perRepResults.Count > 0)
|
||||
{
|
||||
double meanOfMeans = perRepResults.Average(r => r.MeanPerSub);
|
||||
double ciHalfWidth = ConfidenceIntervalHalfWidth95(
|
||||
perRepResults.Select(r => r.MeanPerSub).ToArray());
|
||||
|
||||
Console.WriteLine($"\n[Orchestrator] N={n} SUMMARY: "
|
||||
+ $"mean_per_sub={meanOfMeans:F1} ± {ciHalfWidth:F1} notif/s (95% CI)");
|
||||
|
||||
// Saturation detection: stop sweep if per-sub rate falls below
|
||||
// 10% of theoretical OR server CPU peaked above 180% (>90% of 2 cores)
|
||||
if (meanOfMeans < minAcceptableRate)
|
||||
{
|
||||
Console.WriteLine($"[Orchestrator] *** SATURATION DETECTED: rate {meanOfMeans:F0} < {minAcceptableRate:F0} ***");
|
||||
saturatedDetected = true;
|
||||
}
|
||||
else if (perRepResults.Average(r => r.ServerCpuPeak) > 180.0)
|
||||
{
|
||||
Console.WriteLine($"[Orchestrator] *** SATURATION DETECTED: server CPU peaked > 180% ***");
|
||||
saturatedDetected = true;
|
||||
}
|
||||
|
||||
// Aggregate row for CSV
|
||||
allResults.Add(new SweepResult
|
||||
{
|
||||
N = n,
|
||||
Replications = perRepResults.Count,
|
||||
MeanPerSubRate = meanOfMeans,
|
||||
Ci95HalfWidth = ciHalfWidth,
|
||||
MeanAggregate = perRepResults.Average(r => r.Aggregate),
|
||||
TotalLate = perRepResults.Sum(r => r.LateDeliveries),
|
||||
MeanServerCpuAvg = perRepResults.Average(r => r.ServerCpuAvg),
|
||||
MeanServerCpuPeak = perRepResults.Average(r => r.ServerCpuPeak),
|
||||
});
|
||||
}
|
||||
}
|
||||
|
||||
// ----------------------------------------------------------------
|
||||
// Output
|
||||
// ----------------------------------------------------------------
|
||||
var sb = new System.Text.StringBuilder();
|
||||
sb.AppendLine("n,replications,mean_per_sub_rate,ci95_halfwidth,mean_aggregate," +
|
||||
"total_late,mean_server_cpu_avg,mean_server_cpu_peak");
|
||||
foreach (var r in allResults)
|
||||
{
|
||||
sb.AppendLine(string.Create(CultureInfo.InvariantCulture,
|
||||
$"{r.N},{r.Replications},{r.MeanPerSubRate:F2},{r.Ci95HalfWidth:F2}," +
|
||||
$"{r.MeanAggregate:F1},{r.TotalLate},{r.MeanServerCpuAvg:F2},{r.MeanServerCpuPeak:F2}"));
|
||||
}
|
||||
await File.WriteAllTextAsync(outputCsv, sb.ToString());
|
||||
Console.WriteLine($"\n[Orchestrator] Results written to {outputCsv}");
|
||||
|
||||
// ----------------------------------------------------------------
|
||||
// Subscriber spawn / teardown
|
||||
// ----------------------------------------------------------------
|
||||
static async Task<SubscriberTask?> SpawnSubscriber(
|
||||
Warehouse wh, string host, int port, int resources, int subId)
|
||||
{
|
||||
try
|
||||
{
|
||||
var conn = await wh.Get<EpConnection>($"ep://{host}:{port}");
|
||||
var sub = new SubscriberTask { SubscriberId = subId };
|
||||
|
||||
for (int i = 0; i < resources; i++)
|
||||
{
|
||||
var proxy = await conn.Get($"sys/sensor_{i}");
|
||||
long lastTick = Stopwatch.GetTimestamp();
|
||||
|
||||
proxy.Instance.PropertyModified += (PropertyModificationInfo data) =>
|
||||
{
|
||||
if (data.Name != "Value") return;
|
||||
long now = Stopwatch.GetTimestamp();
|
||||
double elapsedMs = (now - lastTick) * 1000.0 / Stopwatch.Frequency;
|
||||
lastTick = now;
|
||||
Interlocked.Increment(ref sub._received);
|
||||
if (elapsedMs > 400) Interlocked.Increment(ref sub._lateDeliveries);
|
||||
};
|
||||
}
|
||||
|
||||
return sub;
|
||||
}
|
||||
catch (Exception ex)
|
||||
{
|
||||
Console.WriteLine($" [Spawn-{subId}] FAILED: {ex.Message}");
|
||||
return null;
|
||||
}
|
||||
}
|
||||
|
||||
static async Task TeardownAll(Warehouse[] whs)
|
||||
{
|
||||
foreach (var wh in whs)
|
||||
{
|
||||
try { await wh.Close(); }
|
||||
catch { /* ignore */ }
|
||||
}
|
||||
}
|
||||
|
||||
// ----------------------------------------------------------------
|
||||
// Stats helpers
|
||||
// ----------------------------------------------------------------
|
||||
static double StdDev(double[] xs)
|
||||
{
|
||||
if (xs.Length < 2) return 0;
|
||||
double mean = xs.Average();
|
||||
double sumSq = xs.Sum(x => (x - mean) * (x - mean));
|
||||
return Math.Sqrt(sumSq / (xs.Length - 1));
|
||||
}
|
||||
|
||||
/// <summary>
|
||||
/// 95% confidence interval half-width using Student's t-distribution.
|
||||
/// For very small samples (n < 3) returns 0 (not enough data).
|
||||
/// t values for 95% two-sided are hard-coded; see standard tables.
|
||||
/// </summary>
|
||||
static double ConfidenceIntervalHalfWidth95(double[] xs)
|
||||
{
|
||||
int n = xs.Length;
|
||||
if (n < 2) return 0;
|
||||
double std = StdDev(xs);
|
||||
double sem = std / Math.Sqrt(n);
|
||||
// t for df=n-1, two-sided 95%
|
||||
double t = (n - 1) switch
|
||||
{
|
||||
1 => 12.706,
|
||||
2 => 4.303,
|
||||
3 => 3.182,
|
||||
4 => 2.776,
|
||||
5 => 2.571,
|
||||
6 => 2.447,
|
||||
7 => 2.365,
|
||||
8 => 2.306,
|
||||
9 => 2.262,
|
||||
_ => 1.960 // normal approximation
|
||||
};
|
||||
return t * sem;
|
||||
}
|
||||
|
||||
static string GetArg(string[] args, string key, string def)
|
||||
{
|
||||
int i = Array.IndexOf(args, key);
|
||||
return (i >= 0 && i + 1 < args.Length) ? args[i + 1] : def;
|
||||
}
|
||||
|
||||
// ----------------------------------------------------------------
|
||||
// Records
|
||||
// ----------------------------------------------------------------
|
||||
class SubscriberTask
|
||||
{
|
||||
public int SubscriberId;
|
||||
internal long _received;
|
||||
internal long _lateDeliveries;
|
||||
public long Received => Interlocked.Read(ref _received);
|
||||
public long LateDeliveries => Interlocked.Read(ref _lateDeliveries);
|
||||
public void ResetCounters()
|
||||
{
|
||||
Interlocked.Exchange(ref _received, 0);
|
||||
Interlocked.Exchange(ref _lateDeliveries, 0);
|
||||
}
|
||||
}
|
||||
|
||||
record RepResult
|
||||
{
|
||||
public int N;
|
||||
public int Rep;
|
||||
public double MeanPerSub;
|
||||
public double StdPerSub;
|
||||
public double MinPerSub;
|
||||
public double MaxPerSub;
|
||||
public double Aggregate;
|
||||
public long LateDeliveries;
|
||||
public double ServerCpuAvg;
|
||||
public double ServerCpuPeak;
|
||||
}
|
||||
|
||||
record SweepResult
|
||||
{
|
||||
public int N;
|
||||
public int Replications;
|
||||
public double MeanPerSubRate;
|
||||
public double Ci95HalfWidth;
|
||||
public double MeanAggregate;
|
||||
public long TotalLate;
|
||||
public double MeanServerCpuAvg;
|
||||
public double MeanServerCpuPeak;
|
||||
}
|
||||
@@ -7,6 +7,14 @@
|
||||
<Nullable>enable</Nullable>
|
||||
</PropertyGroup>
|
||||
|
||||
<ItemGroup>
|
||||
<Compile Remove="Program.cs" />
|
||||
</ItemGroup>
|
||||
|
||||
<ItemGroup>
|
||||
<None Include="Program.cs" />
|
||||
</ItemGroup>
|
||||
|
||||
<ItemGroup>
|
||||
<ProjectReference Include="..\..\..\..\Libraries\Esiur\Esiur.csproj" />
|
||||
</ItemGroup>
|
||||
|
||||
@@ -0,0 +1,176 @@
|
||||
// ============================================================
|
||||
// Test 4: Fork-Join Queueing Test — CLIENT NODE (REPLICATED)
|
||||
//
|
||||
// Extends the original single-shot client to run K independent
|
||||
// replications of each (delay, α) configuration so that 95%
|
||||
// confidence intervals can be reported for the metrics in
|
||||
// Table III (λ, μ, R̄, δ̄, D̄, P99(D), queue length, batch B).
|
||||
//
|
||||
// Each replication uses an identical configuration; the server
|
||||
// runs StartUpdatesLocal back-to-back, and the client snapshots
|
||||
// the cumulative finished-queue length between replications so
|
||||
// that each replication's evaluation sees only its own items.
|
||||
//
|
||||
// Usage:
|
||||
// dotnet run -- --host 127.0.0.1 --port 10901 \
|
||||
// --trials 1000 \
|
||||
// --delays 5:10:20:30:50:100 \
|
||||
// --alphas 0.0:0.25:0.5:0.75:1.0 \
|
||||
// --replications 5 \
|
||||
// --output forkjoin_replicated.csv
|
||||
// ============================================================
|
||||
|
||||
using Esiur.Data;
|
||||
using Esiur.Protocol;
|
||||
using Esiur.Resource;
|
||||
using Esiur.Tests.Queueing.Client;
|
||||
using System.ComponentModel;
|
||||
using System.Globalization;
|
||||
using System.IO;
|
||||
|
||||
// ---------- arguments ----------
|
||||
var host = GetArg(args, "--host", "127.0.0.1");
|
||||
var port = int.Parse(GetArg(args, "--port", "10901"));
|
||||
var trials = int.Parse(GetArg(args, "--trials", "1000"));
|
||||
var replications = int.Parse(GetArg(args, "--replications", "5"));
|
||||
var settleMs = int.Parse(GetArg(args, "--settle-ms", "1000"));
|
||||
var outputCsv = GetArg(args, "--output", "forkjoin_replicated.csv");
|
||||
var delays = GetArg(args, "--delays", "5:10:20:30:50:100")
|
||||
.Split(':').Select(int.Parse).ToArray();
|
||||
var alphas = GetArg(args, "--alphas", "0.0:0.25:0.5:0.75:1.0")
|
||||
.Split(':').Select(s => double.Parse(s, CultureInfo.InvariantCulture)).ToArray();
|
||||
|
||||
Console.WriteLine($"[Client-T4-R] Connecting to {host}:{port}");
|
||||
Console.WriteLine($"[Client-T4-R] trials/rep={trials} replications={replications} " +
|
||||
$"settle={settleMs}ms");
|
||||
Console.WriteLine($"[Client-T4-R] delays={string.Join(",", delays)}");
|
||||
Console.WriteLine($"[Client-T4-R] alphas={string.Join(",", alphas.Select(a => a.ToString("F2", CultureInfo.InvariantCulture)))}");
|
||||
Console.WriteLine($"[Client-T4-R] {delays.Length * alphas.Length} configurations × {replications} reps " +
|
||||
$"= {delays.Length * alphas.Length * replications} trial runs");
|
||||
|
||||
// ---------- connect ----------
|
||||
var wh = new Warehouse();
|
||||
var serviceResource = await wh.Get<EpResource>($"ep://{host}:{port}/sys/queueing");
|
||||
var service = (dynamic)serviceResource;
|
||||
|
||||
// ---------- replication coordinator state ----------
|
||||
//
|
||||
// The server's StartUpdatesLocal fires `trials` PropertyChanged events
|
||||
// across a single call. We count incoming events; when `trials` arrive,
|
||||
// the current replication is complete. We then slice off this rep's
|
||||
// portion of the cumulative finished-queue and hand it to QueueEval.
|
||||
//
|
||||
// `repDone` is signaled once per replication so the orchestrator coroutine
|
||||
// can drive the next call.
|
||||
|
||||
int eventsThisRep = 0;
|
||||
TaskCompletionSource<bool> repDone = new(TaskCreationOptions.RunContinuationsAsynchronously);
|
||||
int finishedQueueBaseline = 0; // cumulative length BEFORE current rep started
|
||||
|
||||
serviceResource.PropertyChanged += (object? sender, PropertyChangedEventArgs e) =>
|
||||
{
|
||||
int n = Interlocked.Increment(ref eventsThisRep);
|
||||
if (n == trials)
|
||||
{
|
||||
repDone.TrySetResult(true);
|
||||
}
|
||||
};
|
||||
|
||||
// ---------- main sweep ----------
|
||||
var rows = new List<ReplicatedResult>();
|
||||
|
||||
using var writer = new StreamWriter(outputCsv);
|
||||
writer.WriteLine(ReplicatedEvalAggregator.CsvHeader);
|
||||
writer.Flush();
|
||||
|
||||
foreach (var delay in delays)
|
||||
{
|
||||
foreach (var alpha in alphas)
|
||||
{
|
||||
Console.WriteLine();
|
||||
Console.WriteLine($"[Client-T4-R] >>> delay={delay} ms α={alpha:F2} " +
|
||||
$"(running {replications} replications) <<<");
|
||||
|
||||
var reps = new List<EsiurQueueEval.EvalResult>(replications);
|
||||
|
||||
for (int rep = 0; rep < replications; rep++)
|
||||
{
|
||||
// Reset per-rep state
|
||||
Interlocked.Exchange(ref eventsThisRep, 0);
|
||||
repDone = new TaskCompletionSource<bool>(
|
||||
TaskCreationOptions.RunContinuationsAsynchronously);
|
||||
|
||||
// Snapshot the cumulative finished-queue length right before this rep
|
||||
// so we can slice off only this rep's portion afterwards.
|
||||
var preQueue = service.DistributedResourceConnection.GetFinishedQueue();
|
||||
finishedQueueBaseline = preQueue.Count;
|
||||
|
||||
// Kick off the server-driven trial sequence (fire-and-forget;
|
||||
// completion is signalled via PropertyChanged → repDone).
|
||||
service.StartUpdatesLocal(delay, trials, alpha);
|
||||
|
||||
// Wait until `trials` PropertyChanged events have been received.
|
||||
await repDone.Task;
|
||||
|
||||
// The server completed `trials` events; slice off this rep's
|
||||
// portion of the cumulative finished-queue. GetFinishedQueue()
|
||||
// returns IReadOnlyList<AsyncQueueItem<T>>; we forward the
|
||||
// typed sliced subset directly to Evaluate which is generic
|
||||
// on T (the property's runtime payload type).
|
||||
var fullQueue = service.DistributedResourceConnection.GetFinishedQueue();
|
||||
var typedQueue = SliceQueue(fullQueue, finishedQueueBaseline);
|
||||
|
||||
var repResult = EsiurQueueEval.Evaluate(typedQueue);
|
||||
reps.Add(repResult);
|
||||
|
||||
Console.WriteLine($" rep {rep + 1}/{replications}: " +
|
||||
$"λ={repResult.LambdaEventsPerSecond:F1}/s " +
|
||||
$"R̄={repResult.Latency.ReadinessMs.Mean:F1}ms " +
|
||||
$"δ̄={repResult.Latency.HolMs.Mean:F1}ms " +
|
||||
$"D̄={repResult.Latency.EndToEndMs.Mean:F1}ms");
|
||||
|
||||
// Settle period between reps to let any straggler notifications drain
|
||||
// and to keep the per-rep arrivals statistically independent of any
|
||||
// residual server state from the previous rep.
|
||||
await Task.Delay(settleMs);
|
||||
}
|
||||
|
||||
var agg = ReplicatedEvalAggregator.Aggregate(delay, alpha, reps);
|
||||
rows.Add(agg);
|
||||
|
||||
ReplicatedEvalAggregator.PrintSummary(agg);
|
||||
|
||||
// Append to CSV immediately so partial progress is preserved
|
||||
// if the process is killed mid-sweep.
|
||||
writer.WriteLine(ReplicatedEvalAggregator.ToCsvRow(agg));
|
||||
writer.Flush();
|
||||
}
|
||||
}
|
||||
|
||||
Console.WriteLine();
|
||||
Console.WriteLine($"[Client-T4-R] Done. {rows.Count} configurations written to {outputCsv}");
|
||||
Environment.Exit(0);
|
||||
|
||||
|
||||
// ----------------------------------------------------------------
|
||||
static string GetArg(string[] args, string key, string def)
|
||||
{
|
||||
int i = Array.IndexOf(args, key);
|
||||
return (i >= 0 && i + 1 < args.Length) ? args[i + 1] : def;
|
||||
}
|
||||
|
||||
// ----------------------------------------------------------------
|
||||
// Slice the cumulative finished-queue down to only the items added
|
||||
// during the current replication.
|
||||
//
|
||||
// The queue is dynamically typed (returned from a dynamic-dispatched
|
||||
// member) and its element type is AsyncQueueItem<T> where T is the
|
||||
// runtime payload type of the observed property. We rely on the DLR
|
||||
// to bind the LINQ Skip<T>/ToList<T> generic methods at runtime, just
|
||||
// as the original code does with the Evaluate<T> call below it.
|
||||
// ----------------------------------------------------------------
|
||||
static dynamic SliceQueue(dynamic fullQueue, int skipCount)
|
||||
{
|
||||
return System.Linq.Enumerable.ToList(
|
||||
System.Linq.Enumerable.Skip(fullQueue, skipCount));
|
||||
}
|
||||
@@ -0,0 +1,183 @@
|
||||
using System;
|
||||
using System.Collections.Generic;
|
||||
using System.Globalization;
|
||||
using System.Linq;
|
||||
|
||||
namespace Esiur.Tests.Queueing.Client
|
||||
{
|
||||
/// <summary>
|
||||
/// Point estimate accompanied by a 95% confidence-interval half-width
|
||||
/// (computed with Student's t for small samples). Use ToString() to
|
||||
/// render as "mean ± half" in print output.
|
||||
/// </summary>
|
||||
public readonly record struct MeanCi(double Mean, double Ci95HalfWidth, int N)
|
||||
{
|
||||
public static MeanCi From(IEnumerable<double> xs)
|
||||
{
|
||||
var arr = xs.ToArray();
|
||||
int n = arr.Length;
|
||||
if (n == 0) return new MeanCi(0, 0, 0);
|
||||
if (n == 1) return new MeanCi(arr[0], 0, 1);
|
||||
|
||||
double mean = arr.Average();
|
||||
double sumSq = 0;
|
||||
for (int i = 0; i < n; i++)
|
||||
{
|
||||
double d = arr[i] - mean;
|
||||
sumSq += d * d;
|
||||
}
|
||||
double std = Math.Sqrt(sumSq / (n - 1));
|
||||
double sem = std / Math.Sqrt(n);
|
||||
|
||||
// Student's t two-sided 95% for small df. df = n - 1.
|
||||
// Values from standard tables; ≥10 falls back to normal (1.960).
|
||||
double t = (n - 1) switch
|
||||
{
|
||||
1 => 12.706,
|
||||
2 => 4.303,
|
||||
3 => 3.182,
|
||||
4 => 2.776,
|
||||
5 => 2.571,
|
||||
6 => 2.447,
|
||||
7 => 2.365,
|
||||
8 => 2.306,
|
||||
9 => 2.262,
|
||||
10 => 2.228,
|
||||
11 => 2.201,
|
||||
12 => 2.179,
|
||||
13 => 2.160,
|
||||
14 => 2.145,
|
||||
15 => 2.131,
|
||||
16 => 2.120,
|
||||
17 => 2.110,
|
||||
18 => 2.101,
|
||||
19 => 2.093,
|
||||
20 => 2.086,
|
||||
_ => 1.960 // normal approximation for df > 20
|
||||
};
|
||||
return new MeanCi(mean, t * sem, n);
|
||||
}
|
||||
|
||||
public override string ToString() =>
|
||||
N <= 1
|
||||
? Mean.ToString("F2", CultureInfo.InvariantCulture)
|
||||
: string.Create(CultureInfo.InvariantCulture,
|
||||
$"{Mean:F2}±{Ci95HalfWidth:F2}");
|
||||
}
|
||||
|
||||
/// <summary>
|
||||
/// Aggregated result over K replications of the same (delay, alpha)
|
||||
/// configuration. Carries point estimates plus per-metric 95% CI
|
||||
/// half-widths for the headline metrics reported in the paper:
|
||||
/// arrival rate λ, service rate μ, mean readiness R̄, mean HOL δ̄,
|
||||
/// and mean end-to-end latency D̄.
|
||||
///
|
||||
/// The companion <see cref="EsiurQueueEval.EvalResult"/> field
|
||||
/// (PerRepMean) holds the existing-style averaged point estimates
|
||||
/// so downstream code that already consumed EvalResult continues
|
||||
/// to work unchanged.
|
||||
/// </summary>
|
||||
public sealed record ReplicatedResult(
|
||||
int Delay,
|
||||
double Alpha,
|
||||
int Replications,
|
||||
MeanCi Lambda,
|
||||
MeanCi Mu,
|
||||
MeanCi ReadinessMeanMs,
|
||||
MeanCi HolMeanMs,
|
||||
MeanCi EndToEndMeanMs,
|
||||
MeanCi EndToEndP99Ms,
|
||||
MeanCi QueueLengthMean,
|
||||
MeanCi BatchSizeMean,
|
||||
EsiurQueueEval.EvalResult PerRepMean);
|
||||
|
||||
public static class ReplicatedEvalAggregator
|
||||
{
|
||||
/// <summary>
|
||||
/// Combine K per-replication EvalResult objects into a single
|
||||
/// ReplicatedResult, computing point estimates and 95% CIs.
|
||||
/// </summary>
|
||||
public static ReplicatedResult Aggregate(
|
||||
int delay,
|
||||
double alpha,
|
||||
IReadOnlyList<EsiurQueueEval.EvalResult> reps)
|
||||
{
|
||||
if (reps == null) throw new ArgumentNullException(nameof(reps));
|
||||
if (reps.Count == 0) throw new ArgumentException("reps is empty.", nameof(reps));
|
||||
|
||||
var lambda = MeanCi.From(reps.Select(r => r.LambdaEventsPerSecond));
|
||||
var mu = MeanCi.From(reps.Select(r => r.MuEventsPerSecond));
|
||||
var readiness = MeanCi.From(reps.Select(r => r.Latency.ReadinessMs.Mean));
|
||||
var hol = MeanCi.From(reps.Select(r => r.Latency.HolMs.Mean));
|
||||
var e2eMean = MeanCi.From(reps.Select(r => r.Latency.EndToEndMs.Mean));
|
||||
var e2eP99 = MeanCi.From(reps.Select(r => r.Latency.EndToEndMs.P99));
|
||||
var qLen = MeanCi.From(reps.Select(r => r.QueueLength.Mean));
|
||||
var batch = MeanCi.From(reps.Select(
|
||||
r => r.FlushSizeStats?.Mean ?? double.NaN)
|
||||
.Where(v => !double.IsNaN(v)));
|
||||
|
||||
// Use the existing Average helper for the carry-along point estimates.
|
||||
var perRepMean = EsiurQueueEval.Average(reps);
|
||||
|
||||
return new ReplicatedResult(
|
||||
Delay: delay,
|
||||
Alpha: alpha,
|
||||
Replications: reps.Count,
|
||||
Lambda: lambda,
|
||||
Mu: mu,
|
||||
ReadinessMeanMs: readiness,
|
||||
HolMeanMs: hol,
|
||||
EndToEndMeanMs: e2eMean,
|
||||
EndToEndP99Ms: e2eP99,
|
||||
QueueLengthMean: qLen,
|
||||
BatchSizeMean: batch,
|
||||
PerRepMean: perRepMean);
|
||||
}
|
||||
|
||||
public static string CsvHeader =>
|
||||
"delay_ms,alpha,replications," +
|
||||
"lambda_mean,lambda_ci95," +
|
||||
"mu_mean,mu_ci95," +
|
||||
"readiness_mean_ms,readiness_ci95," +
|
||||
"hol_mean_ms,hol_ci95," +
|
||||
"e2e_mean_ms,e2e_ci95," +
|
||||
"e2e_p99_ms,e2e_p99_ci95," +
|
||||
"queue_len_mean,queue_len_ci95," +
|
||||
"batch_mean,batch_ci95";
|
||||
|
||||
public static string ToCsvRow(ReplicatedResult r)
|
||||
{
|
||||
var inv = CultureInfo.InvariantCulture;
|
||||
return string.Create(inv,
|
||||
$"{r.Delay},{r.Alpha:F3},{r.Replications}," +
|
||||
$"{r.Lambda.Mean:F3},{r.Lambda.Ci95HalfWidth:F3}," +
|
||||
$"{r.Mu.Mean:F3},{r.Mu.Ci95HalfWidth:F3}," +
|
||||
$"{r.ReadinessMeanMs.Mean:F3},{r.ReadinessMeanMs.Ci95HalfWidth:F3}," +
|
||||
$"{r.HolMeanMs.Mean:F3},{r.HolMeanMs.Ci95HalfWidth:F3}," +
|
||||
$"{r.EndToEndMeanMs.Mean:F3},{r.EndToEndMeanMs.Ci95HalfWidth:F3}," +
|
||||
$"{r.EndToEndP99Ms.Mean:F3},{r.EndToEndP99Ms.Ci95HalfWidth:F3}," +
|
||||
$"{r.QueueLengthMean.Mean:F3},{r.QueueLengthMean.Ci95HalfWidth:F3}," +
|
||||
$"{r.BatchSizeMean.Mean:F3},{r.BatchSizeMean.Ci95HalfWidth:F3}");
|
||||
}
|
||||
|
||||
/// <summary>
|
||||
/// Console-friendly compact summary, one configuration per call.
|
||||
/// </summary>
|
||||
public static void PrintSummary(ReplicatedResult r)
|
||||
{
|
||||
Console.WriteLine();
|
||||
Console.WriteLine($"=== Configuration: delay={r.Delay} ms, α={r.Alpha:F2}, " +
|
||||
$"replications={r.Replications} ===");
|
||||
Console.WriteLine("Metric | Mean ± 95% CI half-width");
|
||||
Console.WriteLine("----------------+----------------------------------------");
|
||||
Console.WriteLine($"λ (/s) | {r.Lambda}");
|
||||
Console.WriteLine($"μ (/s) | {r.Mu}");
|
||||
Console.WriteLine($"R̄ (ms) | {r.ReadinessMeanMs}");
|
||||
Console.WriteLine($"δ̄ (ms) | {r.HolMeanMs}");
|
||||
Console.WriteLine($"D̄ (ms) | {r.EndToEndMeanMs}");
|
||||
Console.WriteLine($"P99(D) (ms) | {r.EndToEndP99Ms}");
|
||||
Console.WriteLine($"Queue length | {r.QueueLengthMean}");
|
||||
Console.WriteLine($"Batch size B | {r.BatchSizeMean}");
|
||||
}
|
||||
}
|
||||
}
|
||||
Reference in New Issue
Block a user