private void ReturnResults()
{
System.Diagnostics.Trace.TraceInformation("[ServerRole] ReturnResults() JobID={0}", jobData.JobID);
KMeansJobResult jobResult = new KMeansJobResult(jobData, Centroids.Uri);
jobResult.Points = Points.Uri;
AzureHelper.EnqueueMessage(AzureHelper.ServerResponseQueue, jobResult);
// TODO: Delete this KMeansJob from the list of jobs in ServerRole
}
/// <summary>
/// Enqueues M messages into a queue. Each message is an instruction to a worker to process a partition of the k-means data.
/// </summary>
public void EnqueueTasks(IEnumerable <Worker> workers)
{
AzureHelper.LogPerformance(() =>
{
int workerNumber = 0;
// Loop through the known workers and give them each a chunk of the points.
// Note: This loop must execute in the same order every time, otherwise caching will not work -- the workers will get a different workerNumber each time and therefore a different chunk of the points.
// We use OrderBy on the PartitionKey to guarantee stable ordering.
foreach (Worker worker in workers.OrderBy(worker => worker.PartitionKey))
{
KMeansTaskData taskData = new KMeansTaskData(jobData, Guid.NewGuid(), workerNumber++, workers.Count(), Centroids.Uri, DateTime.UtcNow, IterationCount, worker.BuddyGroupID);
taskData.Points = Points.Uri;
tasks.Add(new KMeansTask(taskData));
AzureHelper.EnqueueMessage(AzureHelper.GetWorkerRequestQueue(worker.PartitionKey), taskData, true);
}
}, jobData.JobID.ToString(), methodName: "EnqueueTasks", iterationCount: IterationCount, points: Points.Uri.ToString(), centroids: Centroids.Uri.ToString(), machineID: MachineID);
}
