Scheduling Algorithms in Fog Computing: A Survey

Matrouk, Khaled; Alatoun, Kholoud

doi:10.2991/ijndc.k.210111.001

Cited by 55 publications

(20 citation statements)

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“…Matrouk and Alatoun present a survey on scheduling algorithms in fog computing [81] in which they discuss the algorithms for resource allocation and scheduling issues. They classify the related literature according to a focused scheduling problem like task scheduling, resource scheduling, resource allocation, job scheduling, and worklow scheduling.…”

Section: Comparison With Existing Surveysmentioning

confidence: 99%

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

et al. 2022

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The Internet of Everything paradigm is being rapidly adopted in developing applications for different domains like smart agriculture, smart city, big data streaming, etc. 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 like 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 paper 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 paper 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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Section: Comparison With Existing Surveysmentioning

confidence: 99%

“…Many learning-based dynamic task allocation studies are not considered [81] Discussion on algorithms for ive major scheduling issues Recent dynamic machine learning scheduling algorithms are not included [56] Review of context-aware scheduling techniques in fog computing…”

Section: Reference Objectives Limitationsmentioning

confidence: 99%

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

et al. 2022

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“…Numerical results show that using this strategy can achieve higher cost-effective performance. Related scholars introduced a fog cloud allocation mechanism based on Gaussian process regression, which is used for 2 Complexity resource allocation of infrastructure composed of collaborative fog and cloud [20]. e fog cloud allocation mechanism uses Gaussian process regression to predict future demand to avoid blocking requests, especially delay-sensitive requests.…”

Section: Related Workmentioning

confidence: 99%

“…In the simulation experiment, the input is a task graph based on a directed acyclic graph, and a random network topology graph is used. e average value of the node degree is 3, and the fog nodes are connected with a probability of 0.5. e experimental parameters are set as follows: the range of the number of tasks is [20,200], the range of task calculations is [5,100] MB, the communication volume range of the task is [5,100] MB, and the data volume range of nonforward nodes required by the task is [5,50] MB. e range of the number of fog nodes is [6,10], the processing rate range of fog nodes is [5,20] MB/sec, the transmission rate range between fog nodes is [50,200] Mbps, and the calculation unit price range of fog node resources is [2,2] cent/sec.…”

Section: Simulation Parameter Settingmentioning

confidence: 99%

Use Linear Weighted Genetic Algorithm to Optimize the Scheduling of Fog Computing Resources

Rui-sheng

2021

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This paper establishes a mathematical model for the resource management and scheduling of the fog node cluster and establishes the optimization goals of delay, communication load, and service cost. According to the idea of genetic algorithm for single-objective optimization, this paper proposes a linear weighted genetic algorithm based on linear weighting. The optimization weight is established according to the user’s preference for the target. We normalize the optimization objective function and merge it into one target, and then we proceed with genetic manipulation to get a better solution. The experimental results show that when the user specifies the preference weight, the optimal solution can be obtained by the genetic algorithm based on linear weighting, and the algorithm execution efficiency is high. With the increase of the single-objective weight, the optimization effect of this objective is better. When the preference weight tends to be average, its overall optimization effect is not ideal. When the user does not specify the preference weight, a set of optimal solutions can be obtained through the improved nondominated sorting genetic algorithm with elite strategy. Compared with the traditional algorithm, in addition to the overall optimization effect of the target being better, the algorithm itself also has higher efficiency.

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“…Khalid et al [59] surveyed on privacy and access control schemes in FC. Matrouk et al [60] aimed to comprehensively review and analyze many scheduling algorithms in FC. Singh et al [61] resented a systematic literature review of FC.…”

Section: A Comprehensive Surveysmentioning

confidence: 99%

Energy Coherent Fog Networks Using Multi-Sink Wireless Sensor Networks

et al. 2021

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Fog computing (FC) models the cloud computing paradigm expedient by bridging the breach between centralized data servers and diverse terrestrially distributed applications. It wields various wireless sensor networks (WSNs) that sprawl in the core of any IoT applications. Consequently, the operation of fog network turns on the efficiency of WSNs operation, while the all-inclusive network energy consumption depends on both FC and WSNs operation. This paper addresses how dissimilar organizations of a fog network can influence its effectiveness and energy savings. Chiefly, it appraises whether deploying multisink nodes in close enough vicinity to the fog nodes can give in energy savings and foster coherent data communication between WSNs and fog networks. To assess the multi-sink assignment problem the following four criteria are used: (i) Distance from the fog network nodes; (ii) Nodes degree; (iii) Sink nodes energy; and (iv) Sink nodes processing capabilities. This paper suggests four novel solutions to the multisink connectivity for some challenges of fog networks deeming: (i) Window Nondominant Set (WNS); (ii) Evaluation Based Approach (EBA); (iii) Harris Hawks Optimizer (HHO); and (iv) Modified HHO (MHHO). Distinct sets of experiments are conducted to check out algorithmic performance. The performance of all algorithms is measured and then compared to each other in terms of power consumption, runtime, packet loss, and localization error. One of the key supremacies of our approaches is the utilization of fog network for sensor networks data processing, principally with the large-scale networks. Yet, the communication challenges could need further study due to the limited communication range of the sensors.

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Scheduling Algorithms in Fog Computing: A Survey

Cited by 55 publications

References 0 publications

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

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

Use Linear Weighted Genetic Algorithm to Optimize the Scheduling of Fog Computing Resources

Energy Coherent Fog Networks Using Multi-Sink Wireless Sensor Networks

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