首页|A Method to Compare Scaling Algorithms for Cloud-Based Services

A Method to Compare Scaling Algorithms for Cloud-Based Services

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Nowadays, many services are offered via the cloud, i.e., they rely on interacting software components that can run on a set of connected Commercial Off-The-Shelf (COTS) servers sitting in data centers. As the demand for any particular service evolves over time, the computational resources associated with the service must be scaled accordingly while keeping the Key Performance Indicators (KPIs) associated with the service under control. Consequently, scaling always involves a delicate trade-off between using the cloud resources and complying with the KPIs. In this paper, we show that a (workload-dependent) Pareto front embodies this trade-off’s limits. We identify this Pareto front for various workloads and assess the ability of several scaling algorithms to approach that Pareto front.

Cloud computingSoftware algorithmsSoftwareApproximation algorithmsSymbolsKey performance indicatorVectorsSynapsesService level agreementsServers

Danny De Vleeschauwer、Chia-Yu Chang、Paola Soto、Yorick De Bock、Miguel Camelo、Koen De Schepper

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Network Automation Department, Nokia Bell Labs, Antwerp, Belgium

IDLab, University of Antwerp - IMEC, Antwerp, Belgium

2025

IEEE transactions on cloud computing

IEEE transactions on cloud computing

ISSN:
年,卷(期):2025.13(1)
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