lib/model: Optimize jobQueue performance and memory use (#8023)

By truncating time.Time to an int64 nanosecond count, we lose the
ability to precisely order timestamps before 1678 or after 2262, but we
gain (linux/amd64, Go 1.17.1):

name                      old time/op    new time/op    delta
JobQueuePushPopDone10k-8    2.85ms ± 5%    2.29ms ± 2%  -19.80%  (p=0.000 n=20+18)
JobQueueBump-8              34.0µs ± 1%    29.8µs ± 1%  -12.35%  (p=0.000 n=19+19)

name                      old alloc/op   new alloc/op   delta
JobQueuePushPopDone10k-8    2.56MB ± 0%    1.76MB ± 0%  -31.31%  (p=0.000 n=18+13)

name                      old allocs/op  new allocs/op  delta
JobQueuePushPopDone10k-8      23.0 ± 0%      23.0 ± 0%     ~     (all equal)

Results for BenchmarkJobQueueBump are with the fixed version, which no
longer depends on b.N for the amount of work performed. rand.Rand.Intn
is cheap at ~10ns per iteration.

lib/model/queue_test.go

@@ -8,6 +8,7 @@ package model
 
 import (
 	"fmt"
+	"math/rand"
 	"testing"
 	"time"
 
@@ -251,16 +252,19 @@ func TestSortByAge(t *testing.T) {
 }
 
 func BenchmarkJobQueueBump(b *testing.B) {
-	files := genFiles(b.N)
+	files := genFiles(10000)
 
 	q := newJobQueue()
 	for _, f := range files {
 		q.Push(f.Name, 0, time.Time{})
 	}
 
+	rng := rand.New(rand.NewSource(int64(b.N)))
+
 	b.ResetTimer()
 	for i := 0; i < b.N; i++ {
-		q.BringToFront(files[i].Name)
+		r := rng.Intn(len(files))
+		q.BringToFront(files[r].Name)
 	}
 }