Efficient Workload Allocation in IoT Fog-Cloud Architectures for Energy Efficiency and Low Latency Using DEABC Algorithm

• Muthubharathi R,
• M. Pandi,
• Vinoth Kumar B,
• A. SenthilKumar,

Vol. 19, No. 2, February 28, 2025
10.3837/tiis.2025.02.002, Download Paper (Free):

• Internet of Things
• Fog Computing
• Cloud computing
• Workload Allocation
• energy consumption
• Employee Bee (EB)
• Onlooker Bee (OLB)
• and Scout Bee (SB)

Abstract

The rapid expansion of IoT applications has resulted in a substantial rise in sensory data generation, necessitating efficient processing methods to handle the growing workload. While cloud computing has traditionally provided scalable resources for data processing, concerns about latency and security have prompted exploration into alternative approaches, such as fog computing. Fog computing enables processing at the network edge, reducing latency but presenting challenges in energy optimization. This research employs the innovative direct efficient Multi-Criteria Sorting in an Artificial Bee Colony Algorithm (DEABC) within the MOP model for WLA in IoT fog and cloud architectures, intending to reduce latency and energy consumption. This approach integrates a new Obscured sorting technique to achieve faster convergence and improved solution quality, eliminating dominated solutions early in the optimization process. The study presents a comprehensive formulation of the problem and demonstrates the effectiveness of DEABC in optimizing workload allocation in fog-cloud environments. Results indicate that the proposed model outperforms existing methods, offering energy-efficient and low-latency IoT systems.

Statistics

Cite this article