TM-MOAOA: a two-stage task scheduling approach using TOPSIS and multi-objective Archimedes optimization in fog-cloud environment
The explosive growth of the Internet of Things (IoT) has introduced significant challenges in real-time data processing, placing substantial pressure on fog and cloud computing infrastructures. Efficient and rapid task processing is essential to manage the massive volume of heterogeneous tasks with...
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| Vydané v: | Computing Ročník 107; číslo 7; s. 155 |
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| Hlavní autori: | , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Vienna
Springer Vienna
01.07.2025
Springer Nature B.V |
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| ISSN: | 0010-485X, 1436-5057 |
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| Abstract | The explosive growth of the Internet of Things (IoT) has introduced significant challenges in real-time data processing, placing substantial pressure on fog and cloud computing infrastructures. Efficient and rapid task processing is essential to manage the massive volume of heterogeneous tasks with varying priority levels. This study proposes a two-stage framework for optimizing load balancing and task scheduling in cloud and fog environments. The framework integrates Multi-Criteria Decision-Making (MCDM) with the Multi-Objective Archimedes Optimization Algorithm (MOAOA). In the first stage, MCDM prioritizes tasks based on key factors such as estimated execution time and deadlines. MOAOA schedules the prioritized tasks within the cloud environment in the second stage. This innovative integration of MCDM and MOAOA enables multi-dimensional optimization for load distribution and resource allocation in distributed IoT systems, thereby enhancing Quality of Service (QoS) and reducing operational costs. Simulation results demonstrate statistically significant improvements in key performance metrics, including makespan, cost, and processing speed. Specifically, compared to baseline algorithms such as Optimal Fuzzy Load Balancing (OFLB), Hybrid Grey Wolf and Improved Particle Swarm Optimization Algorithm (HGWIPSOA), and Multi-objective Cat Swam Optimization Fault tolerant Load Balancing (MCSOFLB), the proposed approach achieves an average reduction of 26.75% in makespan, 22.48% in cost, and a 7.42% improvement in processing speed. |
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| AbstractList | The explosive growth of the Internet of Things (IoT) has introduced significant challenges in real-time data processing, placing substantial pressure on fog and cloud computing infrastructures. Efficient and rapid task processing is essential to manage the massive volume of heterogeneous tasks with varying priority levels. This study proposes a two-stage framework for optimizing load balancing and task scheduling in cloud and fog environments. The framework integrates Multi-Criteria Decision-Making (MCDM) with the Multi-Objective Archimedes Optimization Algorithm (MOAOA). In the first stage, MCDM prioritizes tasks based on key factors such as estimated execution time and deadlines. MOAOA schedules the prioritized tasks within the cloud environment in the second stage. This innovative integration of MCDM and MOAOA enables multi-dimensional optimization for load distribution and resource allocation in distributed IoT systems, thereby enhancing Quality of Service (QoS) and reducing operational costs. Simulation results demonstrate statistically significant improvements in key performance metrics, including makespan, cost, and processing speed. Specifically, compared to baseline algorithms such as Optimal Fuzzy Load Balancing (OFLB), Hybrid Grey Wolf and Improved Particle Swarm Optimization Algorithm (HGWIPSOA), and Multi-objective Cat Swam Optimization Fault tolerant Load Balancing (MCSOFLB), the proposed approach achieves an average reduction of 26.75% in makespan, 22.48% in cost, and a 7.42% improvement in processing speed. |
| ArticleNumber | 155 |
| Author | Moazzami, Setareh Razzaghzadeh, Shiva Khaledian, Navid Kivi, Parisa Norouzi |
| Author_xml | – sequence: 1 givenname: Navid surname: Khaledian fullname: Khaledian, Navid organization: Interdisciplinary Centre for Security, Reliability and Trust (SnT), University of Luxembourg – sequence: 2 givenname: Shiva surname: Razzaghzadeh fullname: Razzaghzadeh, Shiva email: Shiva.razzaghzadeh@srbiau.ac.ir organization: Department of Computer Engineering, Ard.C., Islamic Azad University – sequence: 3 givenname: Setareh surname: Moazzami fullname: Moazzami, Setareh organization: Department of Computer Engineering, Tehran North Branch, Islamic Azad University – sequence: 4 givenname: Parisa Norouzi surname: Kivi fullname: Kivi, Parisa Norouzi organization: Department of Computer Engineering, Ard.C., Islamic Azad University |
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| SubjectTerms | Algorithms Artificial Intelligence Cloud computing Computer Appl. in Administrative Data Processing Computer Communication Networks Computer Science Data processing Deadlines Decision making Deep learning Energy consumption Energy efficiency Fault tolerance Fuzzy logic Genetic algorithms Heuristic Information Systems Applications (incl.Internet) Internet of Things Load balancing Load distribution (forces) Machine learning Methods Multiple criterion Multiple objective analysis Operating costs Optimization algorithms Particle swarm optimization Performance measurement Real time Regular Paper Resource allocation Scheduling Software Engineering Task scheduling Workloads |
| Title | TM-MOAOA: a two-stage task scheduling approach using TOPSIS and multi-objective Archimedes optimization in fog-cloud environment |
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