  Task scheduling is a well-studied problem in the context of optimizing the
Quality of Service (QoS) of cloud computing environments. In order to sustain
the rapid growth of computational demands, one of the most important QoS
metrics for cloud schedulers is the execution cost. In this regard, several
data-driven deep neural networks (DNNs) based schedulers have been proposed in
recent years to allow scalable and efficient resource management in dynamic
workload settings. However, optimal scheduling frequently relies on
sophisticated DNNs with high computational needs implying higher execution
costs. Further, even in non-stationary environments, sophisticated schedulers
might not always be required and we could briefly rely on low-cost schedulers
in the interest of cost-efficiency. Therefore, this work aims to solve the
non-trivial meta problem of online dynamic selection of a scheduling policy
using a surrogate model called MetaNet. Unlike traditional solutions with a
fixed scheduling policy, MetaNet on-the-fly chooses a scheduler from a large
set of DNN based methods to optimize task scheduling and execution costs in
tandem. Compared to state-of-the-art DNN schedulers, this allows for
improvement in execution costs, energy consumption, response time and service
level agreement violations by up to 11, 43, 8 and 13 percent, respectively.
