A comprehensive survey on nature‐inspired algorithms and their applications in edge computing: Challenges and future directions

Adhikari, Mainak; Srirama, Satish Narayana; Amgoth, Tarachand

doi:10.1002/spe.3025

Cited by 7 publications

(3 citation statements)

References 147 publications

(260 reference statements)

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“…Since these multi‐objective strategies are NP‐hard, several heuristics (e.g., constant‐factor approximation 55 ) and meta‐heuristics (e.g., based on genetic algorithm (GA), 56 simulated annealing (SA), 57 particle swarm optimization (PSO) 58 ) were proposed. Nature‐Inspired meta‐heuristics solutions for placement and scheduling of fog applications are summarized in Reference 59.…”

Section: Fog Computing Challenges and Solutionsmentioning

confidence: 99%

A decade of research in fog computing: Relevance, challenges, and future directions

Srirama

2023

Softw Pract Exp

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Recent developments in the Internet of Things (IoT) and real‐time applications, have led to the unprecedented growth in the connected devices and their generated data. Traditionally, this sensor data is transferred and processed at the cloud, and the control signals are sent back to the relevant actuators, as part of the IoT applications. This cloud‐centric IoT model, resulted in increased latencies and network load, and compromised privacy. To address these problems, Fog Computing was coined by Cisco in 2012, a decade ago, which utilizes proximal computational resources for processing the sensor data. Ever since its proposal, fog computing has attracted significant attention and the research fraternity focused at addressing different challenges such as fog frameworks, simulators, resource management, placement strategies, quality of service aspects, fog economics and so forth. However, after a decade of research, we still do not see large‐scale deployments of public/private fog networks, which can be utilized in realizing interesting IoT applications. In the literature, we only see pilot case studies and small‐scale testbeds, and utilization of simulators for demonstrating scale of the specified models addressing the respective technical challenges. There are several reasons for this, and most importantly, fog computing did not present a clear business case for the companies and participating individuals yet. This article summarizes the technical, non‐functional, and economic challenges, which have been posing hurdles in adopting fog computing, by consolidating them across different clusters. The article also summarizes the relevant academic and industrial contributions in addressing these challenges and provides future research directions in realizing real‐time fog computing applications, also considering the emerging trends such as federated learning and quantum computing.

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Section: Fog Computing Challenges and Solutionsmentioning

confidence: 99%

A decade of research in fog computing: Relevance, challenges, and future directions

Srirama

2023

Softw Pract Exp

Self Cite

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“…To more effectively address numerous research difficulties, such as resource placement and scheduling, mobility, communication and edge control, many nature-inspired meta-heuristic (NIMH) methods have been applied in edge computing. Fuzzy logic, edge network systems, and various research issues are all included in the survey conducted by Adhikari et al, which divides the current NIMH into three categories based on the nature of their work [35]. To reduce Service Level Agreement (SLA) violations caused by the limitations of edge computing resources and to handle the computational complexity of edge computing problems, Adyson et al propose a random and heuristic approach to initialize the population for multi-objective genetic algorithm.…”

Section: Related Workmentioning

confidence: 99%

Resource Provisioning Using Meta-Heuristic Methods for IoT Microservices With Mobility Management

2023

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The fog and edge computing paradigm provide a distributed architecture of nodes with processing capability for smart healthcare systems driven by Internet of Thing (IoT) applications. It also provides a method to reduce big data transmissions that cause latency and enhance the system's efficiency. Resource provisioning and scheduling in edge and fog systems is a significant problem due to heterogeneity and dispersion of edge/fog/cloud resources. The goal of scheduling is to map tasks to appropriate resources, which belong to NP-hard problems, and it takes much time to find an optimal solution. Meta-heuristic methods achieve near-optimal solutions within a reasonable time. Current edge/fog resource allocation research does not sufficiently address resource allocation problems in mobility-aware microservice-based IoT applications. This paper proposes a meta-heuristic-based micro-service resource provisioning model with mobility management for smart healthcare systems. The proposed approach has been tested on an experimental set-up with a simulation of a critical real-time smart healthcare application with and without considering the mobility of the devices. It applies meta-heuristic methods such as modified genetic and flower pollination algorithms for resource management. The proposed method outperforms the existing solutions in energy consumption, network usage, cost, execution time, and latency by 17%, 20%, 22%, 17%, and 63%, respectively.INDEX TERMS Edge computing, fog computing, Internet of Things, meta-heuristic, microservices, mobility, smart healthcare, time critical applications.

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“…Besides, the performance of scheduling techniques should be continuously evaluated to offer the best performance. As depicted in Table 1, the existing surveys barely study and provide comprehensive taxonomy for the above-mentioned 3.5 [152] 3 [34] 2.5 [113] 2 [79] 1.5 [118] 1.5 [86] 1.5 [68] 1.5 [60] 1 [123] 0.5 [7] 0.5 This Survey Current : Full Cover, : Partial Cover, : Does Not Cover perspectives. In this work, we identify the main parameters of each perspective and present a taxonomy accordingly.…”

Section: Related Surveysmentioning

confidence: 99%

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

Goudarzi¹,

Palaniswami²,

Buyya³

2022

Preprint

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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. CCS Concepts: • General and reference → General literature; • Computer systems organization → Distributed architectures.

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A comprehensive survey on nature‐inspired algorithms and their applications in edge computing: Challenges and future directions

Cited by 7 publications

References 147 publications

A decade of research in fog computing: Relevance, challenges, and future directions

A decade of research in fog computing: Relevance, challenges, and future directions

Resource Provisioning Using Meta-Heuristic Methods for IoT Microservices With Mobility Management

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

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