Task Offloading in Fog Computing for Using Smart Ant Colony Optimization

Kishor, Amit; Chakarbarty, Chinmay

doi:10.1007/s11277-021-08714-7

Cited by 98 publications

(72 citation statements)

References 43 publications

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“…In this category, the data should be processed by the servers as soon as it was generated (i.e., real-time), and hence, the data usually require relatively simple transformation or computation. Several works such as [20,61,76] discuss stream workload for IoT applications.…”

Section: Workload Modelmentioning

confidence: 99%

Scheduling IoT Applications in Edge and Fog Computing Environments: A Taxonomy and Future Directions

Goudarzi¹,

Palaniswami²,

Buyya

2022

ACM Comput. Surv.

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Fog computing, as a distributed paradigm, offers cloud-like services at the edge of the network with low latency and high-access bandwidth to support a diverse range of IoT application scenarios. To fully utilize the potential of this computing paradigm, scalable, adaptive, and accurate scheduling mechanisms and algorithms are required to efficiently capture the dynamics and requirements of users, IoT applications, environmental properties, and optimization targets. This paper presents a taxonomy of recent literature on scheduling IoT applications in Fog computing. Based on our new classification schemes, current works in the literature are analyzed, research gaps of each category are identified, and respective future directions are described.

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Section: Workload Modelmentioning

confidence: 99%

Scheduling IoT Applications in Edge and Fog Computing Environments: A Taxonomy and Future Directions

Goudarzi¹,

Palaniswami²,

Buyya

2022

ACM Comput. Surv.

View full text Add to dashboard Cite

show abstract

“…In this category, the data should be processed by the servers as soon as it was generated (i.e., real-time), and hence, the data usually require relatively simple transformation or computation. Several works such as [22,65,82] discuss stream workload for IoT applications.…”

Section: Workload Modelmentioning

confidence: 99%

“…Compared to two-tier model, this model also uses CSs at the highest-most layer to support edge resources (i.e., Fog computing). Several works considered three-tier model in the literature such as [59,65,108,116].…”

Section: Tiering Modelmentioning

confidence: 99%

“…The current literature based on the CSs' number can be divided into 1) single and 2) multiple categories. Majority of works only consider one CDC as resource provider (either as a central entity with aggregated resources or different number of VMs) to support FSs such as [14,50,59,65]. In real-world environment, different CDCs are available which can provide services with different QoS for multiple applications.…”

Section: Cloud Servers (Css)mentioning

confidence: 99%

“…Hence, cooperative Fog computing, associated protocols, and constraints require further investigation for different IoT application scenarios. Many-Tier Multiple General Hetero Multiple General Hetero ND Single [117] Two-Tier Multiple General Hetero Multiple General Hetero ND ND [90] Three-Tier Multiple General Hetero Multiple General Hetero ND Multiple [14] Three-Tier Multiple General Hetero Multiple General Hetero Single [58] Three-Tier Multiple General Hetero Multiple General Hetero Single [114] Two-Tier Multiple Vehicle Hetero Multiple General, Vehicle Hetero Intra [6] Three-Tier Multiple General Hetero Multiple General Hetero Single [16] Three-Tier Multiple General ND Multiple General Hetero Intra Single [22] Two-Tier Multiple General Homo Multiple General Hetero Intra [4] Three-Tier Multiple General Hetero Multiple General Hetero Single [50] Three-Tier Multiple General ND Multiple General Hetero Single [59] Three-Tier Multiple General Hetero Multiple General Hetero Single [61] Two-Tier Multiple General ND Multiple General Hetero Intra [65] Three-Tier Multiple General Hetero Multiple General Hetero Single [69] Two-Tier Multiple General Hetero Multiple General Hetero Intra [82] Three-Tier Multiple General Hetero Multiple BS Hetero Single [95] Three-…”

mentioning

confidence: 99%

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Scheduling IoT Applications in Edge and Fog Computing Environments: A Taxonomy and Future Directions

Goudarzi¹,

Palaniswami²,

Buyya³

2022

Preprint

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e‐TOALB: An efficient task offloading in IoT‐fog networks

Lone,

Sofi

2023

Concurrency and Computation

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SummarySmart devices are concerned about the processing and computation of tasks due to their tiny nature. They prefer to offload their tasks to the cloud for processing and computation. Due to the huge amount of data being generated by smart devices, the cloud becomes inefficient in terms of huge delay. Thus, Processing tasks in the cloud can add latency and finally needs to be addressed. Thus, fog computing is an alternative to the latency issue. The tasks are offloaded to fog instead of the cloud. In this paper, e‐TOALB (enhanced task offloading and load balancing), a modified and enhanced nature‐inspired and meta‐heuristic ant colony optimization is used to offload tasks in a fog environment. The results obtained by the proposed method are compared with Particle swarm optimization (PSO), round robin (RR), and ant colony optimization. The numerical results clearly show an improvement in average response time and load sharing among all fog nodes. The results of the proposed model produce low response time, low average service time, and low standard deviation. The proposed scheme aims to find the best possible decision for offloading tasks to nearby fog devices and to find an optimal route for offloading with the least communication cost and average service time.

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Task Offloading in Fog Computing for Using Smart Ant Colony Optimization

Cited by 98 publications

References 43 publications

Scheduling IoT Applications in Edge and Fog Computing Environments: A Taxonomy and Future Directions

Scheduling IoT Applications in Edge and Fog Computing Environments: A Taxonomy and Future Directions

Scheduling IoT Applications in Edge and Fog Computing Environments: A Taxonomy and Future Directions

e‐TOALB: An efficient task offloading in IoT‐fog networks

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