I have 1 node K8 cluster on digitalocean with 1cpu/2gbRAM
and 3 node cluster on google cloud with 1cpu/2gbRAM I ran two jobs separatley on each cloud platform with auto-scaling enabled.

First job had memory request of 200Mi

apiVersion: batch/v1
kind: Job
metadata:
  name: scaling-test
spec:
  parallelism: 16
  template:
    metadata:
      name: scaling-test
    spec:
      containers:
        - name: debian
          image: debian
          command: ["/bin/sh","-c"]
          args: ["sleep 300"]
          resources:
            requests:
              cpu: "100m"
              memory: "200Mi"
      restartPolicy: Never

More nodes of (1cpu/2gbRAM) were added to cluster automatically and after job completion extra node were deleted automatically.

After that, i ran second job with memory request 4500Mi

apiVersion: batch/v1
kind: Job
metadata:
  name: scaling-test2
spec:
  parallelism: 3
  template:
    metadata:
      name: scaling-test2
    spec:
      containers:
        - name: debian
          image: debian
          command: ["/bin/sh","-c"]
          args: ["sleep 5"]
          resources:
            requests:
              cpu: "100m"
              memory: "4500Mi"
      restartPolicy: Never

After checking later job remained in pending state . I checked pods Events log and i'm seeing following error.

0/5 nodes are available: 5 Insufficient memory    **source: default-scheduler**
pod didn't trigger scale-up (it wouldn't fit if a new node is added): 1 Insufficient memory **source:cluster-autoscaler**

cluster did not auto-scaled for required requested resource for job. Is this possible using kubernetes?