Fragmented Task Scheduling for Load-Balanced Fog Computing Based on Q-Learning
Fragmented Task Scheduling for Load-Balanced Fog Computing Based on Q-Learning
About this item
Full title
Author / Creator
Publisher
Oxford: Hindawi
Journal title
Language
English
Formats
Publication information
Publisher
Oxford: Hindawi
Subjects
More information
Scope and Contents
Contents
5G and beyond (B5G) applications generate tremendous computing-intensive, latency-sensitive, and privacy-sensitive tasks, which differ from the legacy cloud computing tasks, requiring more sophisticated scheduling strategies. We must satisfy the stringent service requirements, particularly privacy preservation that has not been sufficiently conside...
Alternative Titles
Full title
Fragmented Task Scheduling for Load-Balanced Fog Computing Based on Q-Learning
Authors, Artists and Contributors
Author / Creator
Identifiers
Primary Identifiers
Record Identifier
TN_cdi_proquest_journals_2640852792
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_2640852792
Other Identifiers
ISSN
1530-8669
E-ISSN
1530-8677
DOI
10.1155/2022/4218696
