ConfidenceQ: a Novel Confidence-Weighted Q-Learning Approach for Optimal Resource Allocation in a Multi-Broker Collaborated Cloud Systems

Inderscience is a global company, a dynamic leading independent journal publisher disseminates the latest research across the broad fields of science, engineering and technology; management, public and business administration; environment, ecological economics and sustainable development; computing, ICT and internet/web services, and related areas.

Authors

Publication Details

Journal
International Journal of Cloud Computing
Published
2026-05-21
DOI
https://doi.org/10.1504/ijcc.2027.10078603
Primary Topic
IoT and Edge/Fog Computing
Type
article
Field-Weighted Citation Impact
0.00
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article

ConfidenceQ: a Novel Confidence-Weighted Q-Learning Approach for Optimal Resource Allocation in a Multi-Broker Collaborated Cloud Systems

M Chaitra, Sumana Maradithaya, Koushik S
International Journal of Cloud Computing
IoT and Edge/Fog Computing
article

ConfidenceQ: a Novel Confidence-Weighted Q-Learning Approach for Optimal Resource Allocation in a Multi-Broker Collaborated Cloud Systems

M Chaitra, Sumana Maradithaya, Koushik S
article en

Abstract

Inderscience is a global company, a dynamic leading independent journal publisher disseminates the latest research across the broad fields of science, engineering and technology; management, public and business administration; environment, ecological economics and sustainable development; computing, ICT and internet/web services, and related areas.

International Journal of Cloud ComputingVol. 16(3)
Openalex Percentile: Top 4%
IoT and Edge/Fog Computing
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