  Oversubscription is a prevalent practice in cloud services where the system
offers more virtual resources, such as virtual cores in virtual machines, to
users or applications than its available physical capacity for reducing revenue
loss due to unused/redundant capacity. While oversubscription can potentially
lead to significant enhancement in efficient resource utilization, the caveat
is that it comes with the risks of overloading and introducing jitter at the
level of physical nodes if all the co-located virtual machines have high
utilization. Thus suitable oversubscription policies which maximize utilization
while mitigating risks are paramount for cost-effective seamless cloud
experiences. Most cloud platforms presently rely on static heuristics-driven
decisions about oversubscription activation and limits, which either leads to
overloading or stranded resources. Designing an intelligent oversubscription
policy that can adapt to resource utilization patterns and jointly optimizes
benefits and risks is, largely, an unsolved problem. We address this challenge
with our proposed novel HuMan-in-the-loop Protoypical Imitation Learning
(ProtoHAIL) framework that exploits approximate symmetries in utilization
patterns to learn suitable policies. Also, our human-in-the-loop
(knowledge-infused) training allows for learning safer policies that are robust
to noise and sparsity. Our empirical investigations on real data show orders of
magnitude reduction in risk and significant increase in benefits (saving
stranded cores) in Microsoft cloud platform for 1st party (internal services).
