public PrognosingService(PrognosingParams @params, ISeriesStorage seriesStorage)
{
Params = @params;
rForecast = new RForecast(@params);
this.seriesStorage = seriesStorage;
}
static void Main(string[] args)
{
string rPackagesPath = "rPackages";
if (!Directory.Exists(rPackagesPath))
{
Directory.CreateDirectory(rPackagesPath);
}
var settings = new Prognosing.Models.PrognosingParams()
{
PrognoseDepth = 1,
MinTraceWindow = 20,
MaxTraceWindow = 25
};
var steps = 5000;
string inputFolder = args.Any() ? args[0] : "Input";
string vm1TracePath = Path.Combine(inputFolder, "Traces", "1.csv");
var trace = ReadTrace(new FileInfo(vm1TracePath)).Take(steps).ToArray();
var traceSerieses = Enumerable.Range(0, trace.Count() - settings.MinTraceWindow - 1)
.Select(i => trace.Skip(i).Take(settings.MaxTraceWindow))
.SelectMany(series =>
new VmResourceTrace[]
{
new VmResourceTrace(1, Resource.Cpu, series.Select(r => r.CPU)),
new VmResourceTrace(1, Resource.Network, series.Select(r => r.Network)),
new VmResourceTrace(1, Resource.Memory, series.Select(r => r.Memmory)),
new VmResourceTrace(1, Resource.Iops, series.Select(r => r.IOPS))
});
var reportService = new ReportService("vmPrognose.xlsx", settings.PrognoseDepth);
reportService.InitVmPrognoseSheets(1, "ARIMA", "SES", "HOLT", "DHOLT", "CROST");
var vm = new VM()
{
Id = 1
};
var previousForecast = new Dictionary <Resource, Dictionary <string, IEnumerable <ForecastResult> > >()
{
{ Resource.Cpu, null },
{ Resource.Network, null },
{ Resource.Memory, null },
{ Resource.Iops, null }
};
var stat = new Dictionary <Resource, Dictionary <string, float[]> >()
{
{ Resource.Cpu, new Dictionary <string, float[]>() },
{ Resource.Network, new Dictionary <string, float[]>() },
{ Resource.Memory, new Dictionary <string, float[]>() },
{ Resource.Iops, new Dictionary <string, float[]>() }
};
try
{
RGlobalEnvironment.InitREngineWithForecasing(rPackagesPath);
RForecast rForecast = new RForecast(settings);
for (int i = 0; i < steps; i++)
{
Console.WriteLine($"step {i}");
vm.Resources = trace[i];
if (i >= settings.MinTraceWindow - 1)
{
var series = trace.Skip(Math.Max(i - settings.MaxTraceWindow + 1, 0)).Take(settings.MaxTraceWindow).ToArray();
var results = rForecast.RunAlgorythms(
new VmResourceTrace[]
{
new VmResourceTrace(1, Resource.Cpu, series.Select(r => r.CPU)),
new VmResourceTrace(1, Resource.Network, series.Select(r => r.Network)),
new VmResourceTrace(1, Resource.Memory, series.Select(r => r.Memmory)),
new VmResourceTrace(1, Resource.Iops, series.Select(r => r.IOPS))
}).ToDictionary(f => f.Resource, f => f.Forecast);
stat[Resource.Cpu] = results[Resource.Cpu].ToDictionary(p => p.Key,
p => p.Value.First(
f => f.WindowOffset == GetBest(p.Key, previousForecast[Resource.Cpu], trace[i].CPU)).Result);
stat[Resource.Network] = results[Resource.Network].ToDictionary(p => p.Key,
p => p.Value.First(
f => f.WindowOffset == GetBest(p.Key, previousForecast[Resource.Network], trace[i].Network)).Result);
stat[Resource.Memory] = results[Resource.Memory].ToDictionary(p => p.Key,
p => p.Value.First(
f => f.WindowOffset == GetBest(p.Key, previousForecast[Resource.Memory], trace[i].Memmory)).Result);
stat[Resource.Iops] = results[Resource.Iops].ToDictionary(p => p.Key,
p => p.Value.First(
f => f.WindowOffset == GetBest(p.Key, previousForecast[Resource.Iops], trace[i].IOPS)).Result);
previousForecast = results;
}
reportService.WriteVmPrognoseStatistics(i + 1, vm, stat);
}
reportService.DrawVmForecastCharts();
reportService.Save();
}
finally
{
RGlobalEnvironment.R.Dispose();
reportService.Dispose();
}
}