  Modern data centers suffer from immense power consumption. The erratic
behavior of internet traffic forces data centers to maintain excess capacity in
the form of idle servers in case the workload suddenly increases. As an idle
server still consumes a significant fraction of the peak energy, data center
operators have heavily invested in capacity scaling solutions. In simple terms,
these aim to deactivate servers if the demand is low and to activate them again
when the workload increases. To do so, an algorithm needs to strike a delicate
balance between power consumption, flow-time, and switching costs. Over the
last decade, the research community has developed competitive online algorithms
with worst-case guarantees. In the presence of historic data patterns,
prescription from Machine Learning (ML) predictions typically outperform such
competitive algorithms. This, however, comes at the cost of sacrificing the
robustness of performance, since unpredictable surges in the workload are not
uncommon. The current work builds on the emerging paradigm of augmenting
unreliable ML predictions with online algorithms to develop novel robust
algorithms that enjoy the benefits of both worlds.
  We analyze a continuous-time model for capacity scaling, where the goal is to
minimize the weighted sum of flow-time, switching cost, and power consumption
in an online fashion. We propose a novel algorithm, called Adaptive Balanced
Capacity Scaling (ABCS), that has access to black-box ML predictions, but is
completely oblivious to the accuracy of these predictions. In particular, if
the predictions turn out to be accurate in hindsight, we prove that ABCS is
$(1+\varepsilon)$-competitive. Moreover, even when the predictions are
inaccurate, ABCS guarantees a bounded competitive ratio. The performance of the
ABCS algorithm on a real-world dataset positively support the theoretical
results.
