func startNextJob(event *models.Event, node *models.Node,
job *models.Job, scheduler scheduling.Scheduler,
eventQueue *models.EventQueue) {
node.CurJob = job
node.EstCompletion = event.Time + scheduler.GetAllocation(job)
completeTime := event.Time + job.RealExec
if job.AbsoluteDeadline() < node.EstCompletion && job.AbsoluteDeadline() < completeTime {
heap.Push(eventQueue, &models.Event{
Job: job,
Time: job.AbsoluteDeadline(),
Node: node,
Type: models.Miss,
})
} else if node.EstCompletion < completeTime {
heap.Push(eventQueue, &models.Event{
Job: job,
Time: node.EstCompletion,
Node: node,
Type: models.Stretch,
})
} else {
heap.Push(eventQueue, &models.Event{
Job: event.Job,
Time: completeTime,
Node: node,
Type: models.Complete,
})
}
}
// GenJobs generates and returns a slice of Jobs with timings
// derived from exponentially distributed random variables.
func GenJobs(sysLoad float64, simTime int, clusterSize int, outPath string, stats chan string,
precision chan float64, threadCap *models.Semaphore, jobPool *sync.Pool) {
threadCap.Lock()
var totalComputation = 0
var numJobs = round(
(sysLoad * float64(simTime) * float64(clusterSize)) / realExecAvg)
var numSubmitters = round(math.Sqrt(float64(numJobs)))
submitterEstAvgs := make([]float64, numSubmitters)
for i := 0; i < numSubmitters; i++ {
for {
submitterEstAvgs[i] = rand.ExpFloat64() * estExecAvg
if submitterEstAvgs[i] > estExecAvg/15.0 && submitterEstAvgs[i] < estExecAvg*15.0 {
break
}
}
}
var burstSize int
var burstAvg = sysLoad * float64(clusterSize) * burstMult
var interval int
var intervalLambda = float64(simTime) / (float64(numJobs) / burstAvg)
var jobs []models.Job
var arrival = 0
var estExec, realExec, deadline, submitter int
for i := 0; i < numJobs; {
submitter = rand.Intn(numSubmitters)
if i != 0 {
interval = round(rand.ExpFloat64() * float64(intervalLambda))
arrival += interval
}
burstSize = round(rand.ExpFloat64() * burstAvg)
for j := 0; j < burstSize; j++ {
realExec = round(rand.ExpFloat64() * realExecAvg)
for { // estExec must always be > realExec
estExec = round(rand.ExpFloat64() * submitterEstAvgs[submitter])
if estExec > realExec {
break
}
}
for { // deadline must always be > estExec (n.b. this implicitly makes it > realExec)
deadline = round(rand.ExpFloat64() * deadlineAvg)
if deadline > estExec {
break
}
}
var newJob *models.Job
poolFetch := jobPool.Get()
if poolFetch == nil {
newJob = &models.Job{}
} else {
newJob = poolFetch.(*models.Job)
}
newJob.ID = i
newJob.SubmitterID = submitter
newJob.Arrival = arrival
newJob.EstExec = estExec
newJob.RealExec = realExec
newJob.Deadline = deadline
jobs = append(jobs, *newJob)
jobPool.Put(newJob)
totalComputation += realExec
i++
}
}
WriteJobsToCSV(jobs, outPath)
var trueLoad = (float64(totalComputation) / (float64(simTime) * float64(clusterSize)))
precision <- (trueLoad / sysLoad)
var statsMsg = "Generated jobs for:\n" +
" Cluster Size: %v\n" +
" System Load: %v\n" +
" Time Span: %v\n" +
" Actual computation load: %v\n"
statsMsg = fmt.Sprintf(statsMsg, clusterSize, sysLoad, simTime, trueLoad)
stats <- statsMsg
threadCap.Free()
}