Siva Kesava’s research while affiliated with Microsoft and other places

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Publications (1)

Figure 1: Example heuristics and their encoding in MetaOpt (sub-figures (b) and (c)). Heuristic in sub-figure (b) forces the demands less than a threshold to be pinned and then solves a flow maximization problem, heuristic in sub-figure (c) assigns the first bin that can fit the ball.
Figure 5: The adversarial subspace generator: (a) finds a rough subspace and separates bad samples ( ) from good ones ( ); (b) it trains a regression tree on these samples and uses it to refine the subspace and produces (c). We show the first subspace (í µí°· 0 ) for our FF example in (c). Here, í µí° ¶ í µí±– í µí±— encodes the rough subspace and í µí±‡ í µí±– and í µí±‰ í µí±– the path in the regression tree.
Towards Safer Heuristics With XPlain
  • Conference Paper
  • Full-text available

November 2024

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10 Reads

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Solal Pirelli

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Siva Kesava

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[...]

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Many problems that cloud operators solve are computationally expensive, and operators often use heuristic algorithms (that are faster and scale better than optimal) to solve them more efficiently. Heuristic analyzers enable operators to find when and by how much their heuristics underperform. However, these tools do not provide enough detail for operators to mitigate the heuristic's impact in practice: they only discover a single input instance that causes the heuristic to underperform (and not the full set), and they do not explain why. We propose XPlain, a tool that extends these analyzers and helps operators understand when and why their heuristics underperform. We present promising initial results that show such an extension is viable.

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