survey

Resource Allocation and Task Scheduling in Fog Computing and Internet of Everything Environments: A Taxonomy, Review, and Future Directions

Authors:

Mohammad Shojafar,

Rajkumar BuyyaAuthors Info & Claims

ACM Computing Surveys (CSUR), Volume 54, Issue 11s

Article No.: 233, Pages 1 - 38

https://doi.org/10.1145/3513002

Published: 09 September 2022 Publication History

Abstract

The Internet of Everything paradigm is being rapidly adopted in developing applications for different domains like smart agriculture, smart city, big data streaming, and so on. These IoE applications are leveraging cloud computing resources for execution. Fog computing, which emerged as an extension of cloud computing, supports mobility, heterogeneity, geographical distribution, context awareness, and services such as storage, processing, networking, and analytics on nearby fog nodes. The resource-limited, heterogeneous, dynamic, and uncertain fog environment makes task scheduling a great challenge that needs to be investigated. The article is motivated by this consideration and presents a systematic, comprehensive, and detailed comparative study by discussing the merits and demerits of different scheduling algorithms, focused optimization metrics, and evaluation tools in the fog computing and IoE environment. The goal of this survey article is fivefold. First, we review the fog computing and IoE paradigms. Second, we delineate the optimization metric engaged with fog computing and IoE environment. Third, we review, classify, and compare existing scheduling algorithms dealing with fog computing and IoE environment paradigms by leveraging some examples. Fourth, we rationalize the scheduling algorithms and point out the lesson learned from the survey. Fifth, we discuss the open issues and future research directions to improve scheduling in fog computing and the IoE environment.

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Alsadie D(2024)Advancements in heuristic task scheduling for IoT applications in fog-cloud computing: challenges and prospectsPeerJ Computer Science10.7717/peerj-cs.212810(e2128)Online publication date: 17-Jun-2024
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Sharma NKumar LDwivedi NKaur AKaur G(2024)Resource Allocation and Security Threat in Cloud Computing: A SurveyCGC International Journal of Contemporary Technology and Research10.46860/cgcijctr.2024.06.10.3816:2(381-387)Online publication date: 17-Sep-2024
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Index Terms

Resource Allocation and Task Scheduling in Fog Computing and Internet of Everything Environments: A Taxonomy, Review, and Future Directions
1. Networks
  1. Network algorithms
    1. Control path algorithms
      1. Network resources allocation
  2. Network services
    1. Cloud computing

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cover image ACM Computing Surveys

ACM Computing Surveys Volume 54, Issue 11s

January 2022

785 pages

ISSN:0360-0300

EISSN:1557-7341

DOI:10.1145/3551650

Editor:
Albert Zomaya
University of Sydney, Australia

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Publication History

Published: 09 September 2022

Online AM: 07 February 2022

Accepted: 01 January 2022

Revised: 01 January 2022

Received: 01 June 2021

Published in CSUR Volume 54, Issue 11s

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Cited By

Sun MDing JZhao ZChen JHuang GWang L(2025)Out-of-order execution enabled deep reinforcement learning for dynamic additive manufacturing schedulingRobotics and Computer-Integrated Manufacturing10.1016/j.rcim.2024.10284191(102841)Online publication date: Feb-2025
https://doi.org/10.1016/j.rcim.2024.102841
Alsadie D(2024)Advancements in heuristic task scheduling for IoT applications in fog-cloud computing: challenges and prospectsPeerJ Computer Science10.7717/peerj-cs.212810(e2128)Online publication date: 17-Jun-2024
https://doi.org/10.7717/peerj-cs.2128
Sharma NKumar LDwivedi NKaur AKaur G(2024)Resource Allocation and Security Threat in Cloud Computing: A SurveyCGC International Journal of Contemporary Technology and Research10.46860/cgcijctr.2024.06.10.3816:2(381-387)Online publication date: 17-Sep-2024
https://doi.org/10.46860/cgcijctr.2024.06.10.381
Manikandan NVinod DAnto Arockia Rosaline RNancy PPremalatha G(2024)Green Computing-Based Digital Waste Management and Resource Allocation for Distributed Fog Data CentersComputational Intelligence for Green Cloud Computing and Digital Waste Management10.4018/979-8-3693-1552-1.ch011(209-226)Online publication date: 27-Feb-2024
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Moshawrab MAdda MBouzouane AIbrahim HRaad A(2024)A Maneuver in the Trade-Off Space of Federated Learning Aggregation Frameworks Secured with Polymorphic Encryption: PolyFLAM and PolyFLAP FrameworksElectronics10.3390/electronics1318371613:18(3716)Online publication date: 19-Sep-2024
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Zhu CLiu CZhu HLi J(2024)Cloud–Fog Collaborative Computing Based Task Offloading Strategy in Internet of VehiclesElectronics10.3390/electronics1312235513:12(2355)Online publication date: 16-Jun-2024
https://doi.org/10.3390/electronics13122355
Shareef Fijaz hShojafar MNaeem M(2024)Multi-Class Imbalanced Data Handling with Concept Drift in Fog Computing: A Taxonomy, Review, and Future DirectionsACM Computing Surveys10.1145/3689627Online publication date: 22-Aug-2024
https://doi.org/10.1145/3689627
Kouka NPiuri VSamarati PLi XHandl J(2024)Tasks Scheduling with Load Balancing in Fog Computing: a Bi-level Multi-Objective Optimization ApproachProceedings of the Genetic and Evolutionary Computation Conference10.1145/3638529.3654069(538-546)Online publication date: 14-Jul-2024
https://dl.acm.org/doi/10.1145/3638529.3654069
Proietti Mattia GBeraldi R(2024)Online Decentralized Scheduling in Fog Computing for Smart Cities Based on Reinforcement LearningIEEE Transactions on Cognitive Communications and Networking10.1109/TCCN.2024.337821910:4(1551-1565)Online publication date: Aug-2024
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Sochovsky JKaragiannis VEdinger JKaaser DSchulte S(2024)A Framework for Dynamic Dependency-based Service Placement in the Cloud-Edge Continuum2024 IEEE 44th International Conference on Distributed Computing Systems Workshops (ICDCSW)10.1109/ICDCSW63686.2024.00021(102-112)Online publication date: 23-Jul-2024
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