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Data replica placement approaches in fog computing: a review

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Abstract

Recently, we are witnessing an enormous burst of data due to the ever-increasing number of Internet of Things (IoT) devices. The traditional cloud computing paradigm has failed to scale; to be specific, its latency and bandwidth utilization are remarkably increased and consequently, Quality of Service (QoS) is decreased. On the other hand, the data management scope in fog computing require much more considerations in terms of performance and scalability. This is because of deploying IoT applications over fog nodes considering their resource-limited and heterogeneity. However, to the best of our knowledge, there is not any literature review that systematically categorizes these issues. In this paper, we have presented a classification of data replica placement approaches considering four main categories: framework-based, graph-based, heuristic-based, and meta-heuristic-based algorithms. To sum up, the primary contribution of this study is as follows: studying articles on data replica placement in fog computing, as well as presenting their strengths and weaknesses, providing a comprehensive systematic review of current approaches and categorizing them comprehensively, discussing research challenges, and future works to improve computing and evaluation mechanisms in the fog computing environment. This paper generally provides a classification, briefly explains the reviewed techniques, and then compares these methods in the end.

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Data availability

Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.

Abbreviations

ACO:

Ant colony algorithm

ACOv:

Optimized ant colony algorithm

CCWN:

Centrality complex weighted networks

DDS:

Distributed data stores

DDoS:

Distributed denial of services

DHT:

Distributed hash table

DNS:

Domain name system

D-Rep:

Distributed—replica placement

DSEGA:

Data-intensive service edge genetic algorithm

DRCA:

Dynamic replica creation algorithm

DRC-AH:

Data replica creation based on access heat

DRC-GM:

Dynamic replica creation algorithm granular and comprehensive

DRSA:

Data replica scheduling algorithm

DRS-NSC:

Data replica selection based on node service capability

DRC-DS:

Dynamic replica creation based on domain structure

FDA:

Fog data analytics

FLP:

Facility location problem

GA:

Genetic algorithm

GAP:

Generalized assignment problem

HDFS:

Hadoop distributed file system

IoT:

Internet of things

IP:

Integer programming

IoV:

Internet of vehicles

MDP:

Markov decision-making process

MILP:

Mixed-integer linear programming

NFV:

Network function virtualization

PoP:

Point of presence

QoS:

Quality of service

FNSG:

Fast non-dominated sorting genetic

RP-FNSG:

Fast non-dominated sorting genetic replica placement

SA:

Simulated annealing

SDN:

Software-defined networking

SLR:

Systematic literature review

TQ:

Technical questions

UAV:

Unmanned aerial vehicles

VM:

Virtual machine

W2H:

Web to home

WIEBRS:

Wireless IoT edge-enabled block replica strategy

YCSB:

Yahoo cloud serving benchmark

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This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

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Authors and Affiliations

  1. Department of Computer Engineering, Qom Branch, Islamic Azad University, Qom, Iran

    Esmaeil Torabi, Mostafa Ghobaei-Arani & Ali Shahidinejad

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  1. Esmaeil Torabi
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  2. Mostafa Ghobaei-Arani
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  3. Ali Shahidinejad
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Contributions

ET, AS, MGA conducted this research. ET: Methodology, Software, Validation, Writing original draft. AS: Conceptualization, Supervision, Writing review & editing, Formal analysis, Project administration. MGA: Investigation, Resources, Data curation, Visualization.

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Correspondence to Mostafa Ghobaei-Arani.

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Torabi, E., Ghobaei-Arani, M. & Shahidinejad, A. Data replica placement approaches in fog computing: a review. Cluster Comput 25, 3561–3589 (2022). https://doi.org/10.1007/s10586-022-03575-6

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