4.
Durgabhavani Battu
– Malla Reddy Engineering College for Women, Hyderabad, Telangana, India.
5.
M. Anusha
– Anurag Engineering College, Kodad, Telangana, India.
Received -
Accepted -
Published 20-Jun-2026
Abstract
This research introduces an adaptive task migration approach to achieve a better load balancing in fog computing environment for Internet of Things (IoT) networks. IoT implementations have large numbers of smart devices and sensors that generate vast amounts of data that require very fast processing and therefore traditional systems using cloud computing may have problems processing this huge amount of information due to delays generated by data being sent to the service through the internet and due to high levels of traffic on the internet. Fog computing overcomes these problems by allowing the ability for computer resources to be closer to the devices, allowing for faster processing of data and creating lower communication delays. However, fog nodes cannot process as much data as cloud computer systems and there may be imbalances in workload throughout the fog computing network. Nodes may have overloads or become under-utilised as a result of changing workloads within the IoT model. These types of imbalance would cause a delay in processing, increased use of energy, and therefore reduce the overall performance of the entire fog computing system. To solve this problem, the proposed adaptive task migration model will provide a monitoring function on resource usage of each fog node (CPU utilisation, memory usage, queue lengths of jobs/tasks, network delays) and migrate tasks from overloaded nodes to the nearest under-utilised node(s) in the fog computing network. These dynamic task redistribution procedures will create a balanced workload (i.e., how many tasks are processed on each fog node). Therefore, by utilising the proposed adaptive task migration model, fog networking performance will improve through reducing latency in processing, improving utilisation of resources, increasing energy efficiency and increasing system reliability.
