O2O-PLB: A One-to-One-Based Optimizer With Priority and Load Balancing Mechanism for Resource Allocation in Fog-Cloud Environments
In the era of the Internet of Things (IoT), Fog and Cloud computing have become critical frameworks for managing large-scale, distributed systems. However, the challenge of optimizing resource allocation remains significant, especially in dynamic and diverse environments. This paper presents O2O-PLB...
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2025-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/10857275/ |
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| author | V. C. Bharathi S. Syed Abuthahir Monelli Ayyavaraiah G. Arunkumar Usama Abdurrahman Sardar Asad Ali Biabani |
| author_facet | V. C. Bharathi S. Syed Abuthahir Monelli Ayyavaraiah G. Arunkumar Usama Abdurrahman Sardar Asad Ali Biabani |
| author_sort | V. C. Bharathi |
| collection | DOAJ |
| description | In the era of the Internet of Things (IoT), Fog and Cloud computing have become critical frameworks for managing large-scale, distributed systems. However, the challenge of optimizing resource allocation remains significant, especially in dynamic and diverse environments. This paper presents O2O-PLB, a new One-to-One-Based Optimizer with Priority and Load Balancing mechanism aimed at improving resource allocation in Fog-Cloud settings. O2O-PLB adopts a priority-based approach, assigning tasks according to urgency, system limitations, and available resources, while its load balancing feature ensures an even distribution of tasks to prevent congestion and inefficiency. The method integrates Fog and Cloud resources effectively, boosting system performance and reducing latency. Simulation results show that O2O-PLB outperforms traditional resource allocation methods in resource usage, response times, and latency reduction. Based on the experimental results, the O2O-PLB algorithm significantly outperforms the benchmark algorithms across essential performance metrics at varying task loads. In terms of response time, O2O-PLB achieves an average reduction of 30% over Greedy-LC, 40% over GA, 45% over MOPSO, and 55% compared to EALB. For latency, O2O-PLB achieves an average decrease of 25% relative to Greedy-LC, 35% over GA, 40% compared to MOPSO, and 50% over EALB. When it comes to load imbalance, O2O-PLB consistently improves by approximately 60% over both MOPSO and EALB, 50% over GA, and 40% over Greedy-LC, indicating strong task distribution capabilities. In terms of task failure rate, O2O-PLB reduces failures by 65% compared to EALB, 50% over GA, 40% over MOPSO, and 35% over Greedy-LC. The findings suggest that O2O-PLB provides an effective solution for optimizing Fog-Cloud resource management, making it a promising tool for future IoT applications. |
| format | Article |
| id | doaj-art-b7c18057233b4b2ca9c49fb45b083494 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-b7c18057233b4b2ca9c49fb45b0834942025-02-06T00:00:46ZengIEEEIEEE Access2169-35362025-01-0113221462215510.1109/ACCESS.2025.353621010857275O2O-PLB: A One-to-One-Based Optimizer With Priority and Load Balancing Mechanism for Resource Allocation in Fog-Cloud EnvironmentsV. C. Bharathi0https://orcid.org/0000-0003-2097-4577S. Syed Abuthahir1Monelli Ayyavaraiah2https://orcid.org/0000-0002-4141-4774G. Arunkumar3https://orcid.org/0000-0003-2243-8620Usama Abdurrahman4https://orcid.org/0000-0002-1586-8211Sardar Asad Ali Biabani5https://orcid.org/0000-0002-7564-5909Department of CSE, VIT-AP University, Amaravati, IndiaDepartment of CSE, Madanapalle Institute of Technology and Sciences, Madanapalle, Andhra Pradesh, IndiaDepartment of CSE, Rajeev Gandhi Memorial College of Engineering and Technology, Nandyala, Andhra Pradesh, IndiaDepartment of CSE, Madanapalle Institute of Technology and Sciences, Madanapalle, Andhra Pradesh, IndiaDepartment of CSE, Sathyabama Institute of Science and Technology, Chennai, IndiaDeanship of Postgraduate Studies and Research, Umm Al-Qura University, Makkah, Saudi ArabiaIn the era of the Internet of Things (IoT), Fog and Cloud computing have become critical frameworks for managing large-scale, distributed systems. However, the challenge of optimizing resource allocation remains significant, especially in dynamic and diverse environments. This paper presents O2O-PLB, a new One-to-One-Based Optimizer with Priority and Load Balancing mechanism aimed at improving resource allocation in Fog-Cloud settings. O2O-PLB adopts a priority-based approach, assigning tasks according to urgency, system limitations, and available resources, while its load balancing feature ensures an even distribution of tasks to prevent congestion and inefficiency. The method integrates Fog and Cloud resources effectively, boosting system performance and reducing latency. Simulation results show that O2O-PLB outperforms traditional resource allocation methods in resource usage, response times, and latency reduction. Based on the experimental results, the O2O-PLB algorithm significantly outperforms the benchmark algorithms across essential performance metrics at varying task loads. In terms of response time, O2O-PLB achieves an average reduction of 30% over Greedy-LC, 40% over GA, 45% over MOPSO, and 55% compared to EALB. For latency, O2O-PLB achieves an average decrease of 25% relative to Greedy-LC, 35% over GA, 40% compared to MOPSO, and 50% over EALB. When it comes to load imbalance, O2O-PLB consistently improves by approximately 60% over both MOPSO and EALB, 50% over GA, and 40% over Greedy-LC, indicating strong task distribution capabilities. In terms of task failure rate, O2O-PLB reduces failures by 65% compared to EALB, 50% over GA, 40% over MOPSO, and 35% over Greedy-LC. The findings suggest that O2O-PLB provides an effective solution for optimizing Fog-Cloud resource management, making it a promising tool for future IoT applications.https://ieeexplore.ieee.org/document/10857275/Cloud computingfog computingresource allocationload balancingresource optimizationO2O-PLB |
| spellingShingle | V. C. Bharathi S. Syed Abuthahir Monelli Ayyavaraiah G. Arunkumar Usama Abdurrahman Sardar Asad Ali Biabani O2O-PLB: A One-to-One-Based Optimizer With Priority and Load Balancing Mechanism for Resource Allocation in Fog-Cloud Environments IEEE Access Cloud computing fog computing resource allocation load balancing resource optimization O2O-PLB |
| title | O2O-PLB: A One-to-One-Based Optimizer With Priority and Load Balancing Mechanism for Resource Allocation in Fog-Cloud Environments |
| title_full | O2O-PLB: A One-to-One-Based Optimizer With Priority and Load Balancing Mechanism for Resource Allocation in Fog-Cloud Environments |
| title_fullStr | O2O-PLB: A One-to-One-Based Optimizer With Priority and Load Balancing Mechanism for Resource Allocation in Fog-Cloud Environments |
| title_full_unstemmed | O2O-PLB: A One-to-One-Based Optimizer With Priority and Load Balancing Mechanism for Resource Allocation in Fog-Cloud Environments |
| title_short | O2O-PLB: A One-to-One-Based Optimizer With Priority and Load Balancing Mechanism for Resource Allocation in Fog-Cloud Environments |
| title_sort | o2o plb a one to one based optimizer with priority and load balancing mechanism for resource allocation in fog cloud environments |
| topic | Cloud computing fog computing resource allocation load balancing resource optimization O2O-PLB |
| url | https://ieeexplore.ieee.org/document/10857275/ |
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