  The operational cost of a cloud computing platform is one of the most
significant Quality of Service (QoS) criteria for schedulers, crucial to keep
up with the growing computational demands. Several data-driven deep neural
network (DNN)-based schedulers have been proposed in recent years that
outperform alternative approaches by providing scalable and effective resource
management for dynamic workloads. However, state-of-the-art schedulers rely on
advanced DNNs with high computational requirements, implying high scheduling
costs. In non-stationary contexts, the most sophisticated schedulers may not
always be required, and it may be sufficient to rely on low-cost schedulers to
temporarily save operational costs. In this work, we propose MetaNet, a
surrogate model that predicts the operational costs and scheduling overheads of
a large number of DNN-based schedulers and chooses one on-the-fly to jointly
optimize job scheduling and execution costs. This facilitates improvements in
execution costs, energy usage and service level agreement violations of up to
11%, 43% and 13% compared to the state-of-the-art methods.
