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Link to original content: https://doi.org/10.1007/s10586-024-04403-9
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A distributed load balancing method for IoT/Fog/Cloud environments with volatile resource support

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Abstract

In cloud/fog-based environments, resource management is an important and challenging process. The deadline-based workflow scheduling mechanism is a common practice in such systems to overcome the complexities of resource management. However, many proposed approaches suffer from resource overloading/underloading, ignoring volunteer and volatile resources, and acting reactively. This paper presents a load-balancing method for IoT/Fog/Cloud environments integrated with local schedulers based on predicting workload and the presence of volatile mobile nodes (as dynamic resources). The proposed approach, firstly, turns the environment into a grid of equal-sized cells to reduce the system’s complexity. Then, the overall status of intra-cell resources (overloaded, underloaded, or normal) is estimated. This estimation is done according to the workload prediction and available dynamic resources. Finally, an exhaustive search is applied to dispatch extra workflows from an overloaded cell to an underloaded one in such a way as to avoid missing workflow deadlines and improve system performance. The proposed method is intended to be scalable and decentralized by nature, allowing it to be used in large-scale settings such as smart cities. Extensive software simulation is used to evaluate and compare the proposed method to with two recently published works. The simulation results show that the proposed method outperforms others regarding job completion rate, workload variances, and time-related parameters.

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Data availability

As mentioned in reference [52], the datasets generated during and/or analysed during the current study are available online with open-source access.

Abbreviations

LSC:

Local scheduling component

RAC:

Region analysis component

WDC:

Workflow dispatching component

SMC:

System management component

DPC:

Data preparation component

CCM:

Cellular coordinates map

MTC:

Model training component

LBP:

Load balancing plan

RMC:

Region management component

CMC:

Cell management component

DFC:

Data filtering component

BoW:

Bag of workflows

PPC:

Pre-processing component

TM:

Trained model

CLDC:

Cellular locations determination component

OA:

Observations agent

EDC:

Environment division component

FD:

Filtered data

LLBC:

Local load balancing component

PD:

Processed data

ESC:

Exhaustive search component

RD:

Raw data

RCM:

Region cost matrix

CSM:

Cell status message

RSAC:

Region status analysis component

OB:

Observations

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Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or.

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Authors and Affiliations

  1. Department of Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

    Zari Shamsa, Ali Rezaee & Amir Masoud Rahmani

  2. Department of Computer Engineering, North Tehran Branch, Islamic Azad University, Tehran, Iran

    Sahar Adabi

  3. Department of Computer Engineering, Sharif University of Technology, Tehran, Iran

    Ali Movaghar Rahimabadi

Authors
  1. Zari Shamsa
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  2. Ali Rezaee
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  3. Sahar Adabi
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  4. Ali Movaghar Rahimabadi
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  5. Amir Masoud Rahmani
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Contributions

Zari Shamsa: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Resources, Software, Visualization, Writing—original draft, Writing—review & editing. Ali Rezaee: Conceptualization, Investigation, Methodology, Project administration. Sahar Adabi: Supervision, Writing—original draft, Writing—review & editing, Validation, Data Curation. Amir Msoud Rahmani: Validation, Data Curation.

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Correspondence to Ali Rezaee.

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Shamsa, Z., Rezaee, A., Adabi, S. et al. A distributed load balancing method for IoT/Fog/Cloud environments with volatile resource support. Cluster Comput 27, 4281–4320 (2024). https://doi.org/10.1007/s10586-024-04403-9

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