%0 Thesis %1 loesch1985langzeitstudie %A Taj, M.Parveen %A N.Muthumani, %D 2026 %E Meghanathan, Natarajan %K networks %R 10.5121/ijcnc.2026.18301 %T A Multi-Objective Optimization Approach to Load Balancing and Task Scheduling in IoT-Fog-Cloud Networks %U https://aircconline.com/ijcnc/V18N3/18326cnc01.pdf %X Although distributed systems can exist across multiple data centers, they require fog and cloud computing paradigms for data management in an Internet of Things (IoT) era. The integrated IoT, fog, and cloud (IoT-fog-cloud) method enables the processing of large amounts of IoT data in real-time. Alternatively, a lack of Load Balancing (LB) and improper handling of network resources can reduce the Quality of Service (QoS) under such circumstances. In real-time applications, increasing traffic to fog nodes causes delays and increases energy consumption. This problem was resolved by an effective LB algorithm. However, good resource utilization can be achieved whenever an effective LB is incorporated with Task Scheduling (TS). Hence, this article proposes a Multi-Objective Weight Optimized Task Scheduling and Load Balancing (MOWOTSLB) for IoT-fog-cloud systems. This research aims to effectively schedule workloads in a balanced manner, which conserves energy, enhances QoS, and reduces task execution time.

@phdthesis{loesch1985langzeitstudie, abstract = {Although distributed systems can exist across multiple data centers, they require fog and cloud computing paradigms for data management in an Internet of Things (IoT) era. The integrated IoT, fog, and cloud (IoT-fog-cloud) method enables the processing of large amounts of IoT data in real-time. Alternatively, a lack of Load Balancing (LB) and improper handling of network resources can reduce the Quality of Service (QoS) under such circumstances. In real-time applications, increasing traffic to fog nodes causes delays and increases energy consumption. This problem was resolved by an effective LB algorithm. However, good resource utilization can be achieved whenever an effective LB is incorporated with Task Scheduling (TS). Hence, this article proposes a Multi-Objective Weight Optimized Task Scheduling and Load Balancing (MOWOTSLB) for IoT-fog-cloud systems. This research aims to effectively schedule workloads in a balanced manner, which conserves energy, enhances QoS, and reduces task execution time. }, added-at = {2026-06-03T14:52:08.000+0200}, author = {Taj, M.Parveen and N.Muthumani}, biburl = {https://www.bibsonomy.org/bibtex/28f5652ec8e6c0d1727b71e7f68e4ed64/laimbee}, description = {Although distributed systems can exist across multiple data centers, they require fog and cloud computing paradigms for data management in an Internet of Things (IoT) era. The integrated IoT, fog, and cloud (IoT-fog-cloud) method enables the processing of large amounts of IoT data in real-time. Alternatively, a lack of Load Balancing (LB) and improper handling of network resources can reduce the Quality of Service (QoS) under such circumstances. In real-time applications, increasing traffic to fog nodes causes delays and increases energy consumption. This problem was resolved by an effective LB algorithm. However, good resource utilization can be achieved whenever an effective LB is incorporated with Task Scheduling (TS). Hence, this article proposes a Multi-Objective Weight Optimized Task Scheduling and Load Balancing (MOWOTSLB) for IoT-fog-cloud systems. This research aims to effectively schedule workloads in a balanced manner, which conserves energy, enhances QoS, and reduces task execution time. }, dnbtitleid = {860485714}, doi = {10.5121/ijcnc.2026.18301}, editor = {Meghanathan, Natarajan}, interhash = {63ba4403309554e4f9b22d46beea4034}, intrahash = {8f5652ec8e6c0d1727b71e7f68e4ed64}, keywords = {networks}, month = may, timestamp = {2026-06-03T14:52:08.000+0200}, title = {A Multi-Objective Optimization Approach to Load Balancing and Task Scheduling in IoT-Fog-Cloud Networks }, url = {https://aircconline.com/ijcnc/V18N3/18326cnc01.pdf}, year = 2026 }