Tight Bounds for Online Balanced Partitioning in the Generalized Learning Model

Resource allocation in distributed and networked systems such as the Cloud is becoming increasingly flexible, allowing these systems to dynamically adjust toward the workloads they serve, in a demand-aware manner.

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Published
2025-07-16
DOI
https://doi.org/10.1145/3694906.3743327
Primary Topic
Optimization and Search Problems
Type
article
Field-Weighted Citation Impact
0.00

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article

Tight Bounds for Online Balanced Partitioning in the Generalized Learning Model

Harald Räcke, Stefan Schmid, Ruslan Zabrodin
Optimization and Search Problems
article

Tight Bounds for Online Balanced Partitioning in the Generalized Learning Model

Harald Räcke, Stefan Schmid, Ruslan Zabrodin
article en

Abstract

Resource allocation in distributed and networked systems such as the Cloud is becoming increasingly flexible, allowing these systems to dynamically adjust toward the workloads they serve, in a demand-aware manner.

Technische Universität Berlin (DE), Technical University of Munich (DE)
Deutsche Forschungsgemeinschaft
Openalex Percentile: Top 81%
Optimization and Search Problems
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