Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning-Assisted Competitive Algorithms for Peak-Aware Energy
  Scheduling   In this paper, we study the peak-aware energy scheduling problem using the
competitive framework with machine learning prediction. With the uncertainty of
energy demand as the fundamental challenge, the goal is to schedule the energy
output of local generation units such that the electricity bill is minimized.
While this problem has been tackled using classic competitive design with
worst-case guarantee, the goal of this paper is to develop learning-assisted
competitive algorithms to improve the performance in a provable manner. We
develop two deterministic and randomized algorithms that are provably robust
against the poor performance of learning prediction, however, achieve the
optimal performance as the error of prediction goes to zero. Extensive
experiments using real data traces verify our theoretical observations and show
15.13% improved performance against pure online algorithms.

33
The categories of this paper are: plasm-ph
23
The categories of this paper are: solv-int
0
The categories of this paper are: hep-ph
22
The categories of this paper are: chao-dyn
10
The categories of this paper are: q-bio
14
The categories of this paper are: math-ph
8
The categories of this paper are: hep-ex
17
The categories of this paper are: q-fin
29
The categories of this paper are: funct-an
28
The categories of this paper are: alg-geom
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The categories of this paper are: comp-gas
12
The categories of this paper are: nucl-th
30
The categories of this paper are: q-alg
13
The categories of this paper are: quant-ph
18
The categories of this paper are: econ
15
The categories of this paper are: nucl-ex