Volume 18, Number 3

A Multi-Objective Optimization Approach to Load Balancing and Task Scheduling in IoT-Fog-Cloud Networks

  Authors

M.Parveen Taj and N.Muthumani, PPG College of Arts and Science, India

  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. This scheme employs a Horse Herd Optimization Algorithm (HOA) for TS. This HOA optimizes the scheduling of users’ task requests to suitable computing resources according to the fitness function calculated using makespan, execution cost, and energy utilization. Though it improves resource utilization, some Physical Machines (PMs) are overloaded, and others are underloaded during uncertain fluctuations in workloads. This causes high energy and resource wastage in the data center. To solve this problem, the HOA is also adopted for Virtual Machine (VM) migration in this article. By assessing the fitness function of PMs such as load, migration cost, energy, and bandwidth use, the HOA chooses the finest VMs to drift to the appropriate PMs. This successfully strikes a balance between load distribution among PMs and energy usage. The simulation findings show that the MOWOTSLB outperforms current LBTS schemes in 0.95 throughput, 30 ms delay,100 ms response time, 175 kj energy consumption, 12.5 mb memory usage, and 900s lifetime.

  Keywords

IoT-fog-cloud systems, Load balancing, Task scheduling, Horse herd optimization & VM migration