  Distributed computing systems often consist of hundreds of nodes, executing
tasks with different resource requirements. Efficient resource provisioning and
task scheduling in such systems are non-trivial and require close monitoring
and accurate forecasting of the state of the system, specifically resource
utilisation at its constituent machines. Two challenges present themselves
towards these objectives. First, collecting monitoring data entails substantial
communication overhead. This overhead can be prohibitively high, especially in
networks where bandwidth is limited. Second, forecasting models to predict
resource utilisation should be accurate and need to exhibit high inference
speed. Mission critical scheduling and resource allocation algorithms use these
predictions and rely on their immediate availability. To address the first
challenge, we present a communication-efficient data collection mechanism.
Resource utilisation data is collected at the individual machines in the system
and transmitted to a central controller in batches. Each batch is processed by
an adaptive data-reduction algorithm based on Fourier transforms and truncation
in the frequency domain. We show that the proposed mechanism leads to a
significant reduction in communication overhead while incurring only minimal
error and adhering to accuracy guarantees. To address the second challenge, we
propose a deep learning architecture using complex Gated Recurrent Units to
forecast resource utilisation. This architecture is directly integrated with
the above data collection mechanism to improve inference speed of our
forecasting model. Using two real-world datasets, we demonstrate the
effectiveness of our approach, both in terms of forecasting accuracy and
inference speed. Our approach resolves challenges encountered in resource
provisioning frameworks and can be applied to other forecasting problems.
