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A Method to Compare Scaling Algorithms for Cloud-Based Services

delete2024-01-01
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PRE
AI
D
Danny De Vleeschauwer *
C
Chia‐Yu Chang
P
Paola Soto
Y
Yorick De Bock
M
Miguel Camelo
K
Koen De Schepper
DOI：10.1109/TCC.2024.3500139delete
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Abstract

Abstract

En 中文
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.
Keywords:
Cloud computing
Software algorithms
Software
Approximation algorithms
Symbols
Key performance indicator
Vectors
Synapses
Service level agreements
Servers
Cloud-based services
kubernetes
pods
scaling

Journal

I
IEEE Transactions on Cloud Computing
IF:
5
Papers:
1.9K
Citations:
4.3K

Organization

U
University of Antwerp
Scholars:
2.1W
Papers: 1.9W
Citations: 2.6W
I
interuniversity microelectronics centre
Scholars:
6.3K
Papers: 4.0K
Citations: 0
Cited Papers

Cited Papers

No cited papers available