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AI-based & heuristic workflow scheduling in cloud and fog computing: a systematic review

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Abstract

Fog and cloud computing are emerging paradigms that enable distributed and scalable data processing and analysis. However, these paradigms also pose significant challenges for workflow scheduling and assigning related tasks or jobs to available resources. Resources in fog and cloud environments are heterogeneous, dynamic, and uncertain, requiring efficient scheduling algorithms to optimize costs and latency and to handle faults for better performance. This paper aims to comprehensively survey existing workflow scheduling techniques for fog and cloud environments and their essential challenges. We analyzed 82 related papers published recently in reputable journals. We propose a subjective taxonomy that categorizes the critical difficulties in existing work to achieve this goal. Then, we present a systematic overview of existing workflow scheduling techniques for fog and cloud environments, along with their benefits and drawbacks. We also analyze different workflow scheduling techniques for various criteria, such as performance, costs, reliability, scalability, and security. The outcomes reveal that 25% of the scheduling algorithms use heuristic-based mechanisms, and 75% use different Artificial Intelligence (AI) based and parametric modelling methods. Makespan is the most significant parameter addressed in most articles. This survey article highlights potentials and limitations that can pave the way for further processing or enhancing existing techniques for interested researchers.

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Data availability

The dataset used and analyzed during the current study is available from the corresponding author upon reasonable request.

Abbreviations

AI:

Artificial intelligence

HEFT:

Heterogeneous earliest finish time algorithm

GA:

Genetic algorithm

KHA:

Krill herd algorithm

EC2:

Elastic compute cloud

AHA:

Artificial hummingbird algorithm

ML:

Machine learning

DVFS:

Dynamic voltage and frequency scaling

IoT:

Internet of things

SMO:

Spider monkey optimization

LE:

Low energy

VM:

Virtual machines

MCC:

Mobile cloud computing

VCPU:

Virtual CPU

MEC:

Mobile edge computing

PPR:

Performance-to-power ratio

DAG:

Directed acyclic graph

FOA:

Fruit fly optimization

T :

Set of tasks

FFA:

Farmland fertility algorithm

A :

Set of arcs

MHDA:

Multi-objective hybrid dragonfly algorithm

QoS :

Quality of service

SOS:

Symbiotic organisms search

EPC:

Event-driven process chain

GOA:

Grasshopper optimization algorithm

PSO:

Particle swarm optimization

CA:

Cultural algorithm

FRM:

Fog computing resource management

EPO:

Emperor penguin optimizer

LCS:

Longest common subsequence

HMM:

Hidden Markov model

LOA:

Lion optimization algorithm

HHO:

Harris hawk optimization

NSGA:

Non-dominated sorting genetic algorithm

OSA:

Owl search algorithm

DDQN:

Double deep Q-network

SDN:

Software-defined network

CNN:

Convolutional neural networks

GGCN:

Gated graph convolution network

MLIP:

Mixed integer linear programming

ILP:

Integer linear programming

DEWS:

Deadline energy-aware workflow scheduling

MTGP:

Multi-tree genetic programming

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Authors and Affiliations

  1. Interdisciplinary Centre for Security, Reliability and Trust (SnT), University of Luxembourg, Esch-sur-Alzette, Luxembourg

    Navid Khaledian & Marcus Voelp

  2. Department of Computer Engineering and IT, University of Kurdistan, Sanandaj, Iran

    Sadoon Azizi

  3. Department of Computer Engineering, Sari Branch, Islamic Azad University, Sari, Iran

    Mirsaeid Hosseini Shirvani

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  1. Navid Khaledian
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  2. Marcus Voelp
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  3. Sadoon Azizi
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  4. Mirsaeid Hosseini Shirvani
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Contributions

NK: Conceptualization, methodology, Writing—original draft preparation, validation. MV: Supervising, Data curation, writing—reviewing and editing, validation. SA: Visualization, investigation. MHS: Writing—reviewing and editing, validation.

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Correspondence to Navid Khaledian.

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Khaledian, N., Voelp, M., Azizi, S. et al. AI-based & heuristic workflow scheduling in cloud and fog computing: a systematic review. Cluster Comput (2024). https://doi.org/10.1007/s10586-024-04442-2

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