protected virtual IEnumerable <IDeploymentChromosome> Cross(IEnumerable <IDeploymentChromosome> parents)
{
var crossoverParents = new IDeploymentChromosome[CrossoverOperator.ParentsNumber];
var i = 0;
List <IDeploymentChromosome> offspring = new List <IDeploymentChromosome>();
foreach (var parent in parents)
{
crossoverParents[i] = parent;
i++;
if (i == CrossoverOperator.ParentsNumber)
{
if (RandomProvider.GetRandom() <= CrossoverProbability)
{
offspring.AddRange(CrossoverOperator.Cross(crossoverParents));
}
else
{
offspring.AddRange(crossoverParents);
}
i = 0;
}
}
if (i != 0)
{
offspring.AddRange(crossoverParents.Take(i));
}
return(offspring);
}
public IDeploymentChromosome Mutate(IDeploymentChromosome deployment)
{
var genes = deployment.Genes;
var microserviceIds = genes.Select(g => new MicroserviceIdentifier(g.FeatureId.ToString())).ToArray();
var newGenes = new List <IDeploymentGene>();
foreach (var gene in genes)
{
var clonedGene = new DeploymentGene(gene.FeatureId, microserviceIds[_randomProvider.GetRandom(0, microserviceIds.Length)]);
newGenes.Add(clonedGene);
}
return(deployment.UpdateGenes(newGenes));
}
public double Evaluate(IDeploymentChromosome deployment, SampleWorkload workload)
{
var deploymentModel = deployment.ToDeploymentModel();
var microserviceQueueingTheoryInfo = new List <QueueingTheoryInfo>();
foreach (var microservice in deploymentModel.Microservices)
{
// Get the queueing theory representation for every type of event that this microservice listens to
var customerTypes = microservice.Select(workload.GetQueueingTheoryInfo);
// Calculate the summation of this these customer classes
microserviceQueueingTheoryInfo.Add(customerTypes.Aggregate((result, current) => result + current));
}
// In this case we want to minimize the mean sojourn time
// For real usage you would want more complex calculations here
return(-microserviceQueueingTheoryInfo.Select(q => q.SojournTime).Average());
}
internal IDeploymentChromosome Cross(IDeploymentChromosome firstParent, IDeploymentChromosome secondParent, int firstGeneIndex, int secondGeneIndex)
{
var firstParentGenes = firstParent.Genes.ToArray();
var secondParentGenes = secondParent.Genes.ToArray();
if (firstParentGenes.Length != secondParentGenes.Length)
{
throw new ArgumentException("Both Chromosomes should have the same length");
}
var firstMicroserviceId = firstParentGenes[firstGeneIndex].MicroserviceId;
var secondMicroserviceId = secondParentGenes[secondGeneIndex].MicroserviceId;
var FeatureIdsToBeMovedFromFirstParent = firstParentGenes.Where(g => g.MicroserviceId == firstMicroserviceId).Select(g => g.FeatureId);
var FeatureIdsToBeMovedFromSecondParent = secondParentGenes.Where(g => g.MicroserviceId == secondMicroserviceId).Select(g => g.FeatureId);
var featuresToBeMoved = FeatureIdsToBeMovedFromFirstParent.Union(FeatureIdsToBeMovedFromSecondParent).Distinct();
// Which microserviceId we use here does not matter, since UpdateGenes will fix them to the correct one
var newDeploymentGenes = featuresToBeMoved.Select(f => new DeploymentGene(f, firstMicroserviceId)).ToArray();
return(firstParent.UpdateGenes(newDeploymentGenes));
}
public IPopulation CreateInitialPopulation(IDeploymentChromosome initialDeployment, int minimalSize, int maximalSize)
{
return(new Population(Enumerable.Repeat(initialDeployment, minimalSize), minimalSize, maximalSize));
}
public IDeploymentChromosome Mutate(IDeploymentChromosome deployment)
{
return(new DeploymentChromosome(deployment.FeatureModel, deployment.Genes.ToArray()));
}
