| dc.description.abstract | Fog Computing has emerged as a promising paradigm to handle the growing demands of computing and networking in edge environments. However, ensuring efficient resource utilization and maintaining QoS in an FC environment remains a significant challenge due to the dynamic and heterogeneous nature of FNs. Load-balancing, which involves distributing the workload among FNs, is a critical technique to optimize resource utilization and enhance QoS.
The increasing proliferation of smart devices on the IoT has led to a growing demand for efficient storage techniques. While CC has been widely used for storing and processing large amounts of data, it is anticipated that bandwidth issues may limit its ability to handle the scale of IoT devices in the future. FC, a novel paradigm, has emerged as a potential solution to address the challenges faced by large-scale IoT networks. The increasing number of sensing devices on the IoT has led to a surge in traffic on cloud servers, necessitating efficient job scheduling to reduce data delays and improve QoS in fog-based cloud systems. Various strategies have been proposed to maintain QoS, but the increased service delay caused by burst traffic can lead to an unbalanced load on the fog environment, impacting job scheduling (Kong). To address this challenge, this thesis proposes a novel hybrid model that combines the features and working style of SNNC and an optimization algorithm with load-balancing scheduling on Fog Nodes.
The proposed hybrid model is compared with well-known algorithms, including Round Robin (RR), Hybrid RR, Hybrid Threshold-based, and Hybrid Predictive-Based models, using fundamental benchmark optimization test functions. The performance of the proposed model demonstrates superior results in sustaining the task scheduling process compared to existing algorithms, indicating its efficacy in improving QoS in the fog environment. (Sadoon Azizi, 2022) | en_US |
