Context-aware scheduling in Fog computing: A survey, taxonomy, challenges and future directions

Fog computing extends Cloud-based facilities and stays in the vicinity of the end-users to provide an attractive solution to a diverse range of latency-sensitive applications. The applications are becoming more sophisticated, context-aware, and computation-intensive due to varying situational and en...

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Bibliographic Details
Published inJournal of network and computer applications Vol. 180; p. 103008
Main Authors Islam, Mir Salim Ul, Kumar, Ashok, Hu, Yu-Chen
Format Journal Article
LanguageEnglish
Published Elsevier Ltd 15.04.2021
Subjects
Context-awareness
Contextual information
Fog computing
Resource estimation
Resource management
Resource provisioning
Scheduling
Fog computing
Resource provisioning
Resource estimation
Contextual information
Context-awareness
Scheduling
Resource management
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Summary:Fog computing extends Cloud-based facilities and stays in the vicinity of the end-users to provide an attractive solution to a diverse range of latency-sensitive applications. The applications are becoming more sophisticated, context-aware, and computation-intensive due to varying situational and environmental conditions in order to meet the ever-increasing users’ demands. Further, resource heterogeneity, dynamic nature, resource limitations, and unpredictability of the Fog environment make scheduling of application tasks while satisfying Quality of Service (QoS) requirements a challenging job. To overcome these issues various scheduling strategies have been proposed considering contextual information of different entities involved in Fog computing. This survey represents a comprehensive literature analysis pertaining to context-aware scheduling in Fog computing. It provides detailed comparison of existing scheduling approaches based on important factors such as context-aware parameters, case studies, performance metrics, and evaluation tools along with advantages and limitations. It also presents detailed taxonomy, performance metrics, and context-aware parameter analysis. Further, it list several issues and challenges. This study will aid the research community in exploring future research directions and essential aspects of scheduling approaches using different types of contextual information.
ISSN:1084-8045
1095-8592
DOI:10.1016/j.jnca.2021.103008