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Joint Optimization of Computation Offloading and Task Scheduling Using Multi-Objective Arithmetic Optimization Algorithm in Cloud-Fog Computing
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DOI：10.1109/ACCESS.2024.3512191.png)
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Abstract
En 中文
The exponential increase in the Internet of Things (IoT) has affected the cloud computing with increase transmission latency and network overhead for real-time applications. Cloud-fog computing paradigm tackle these limitations by moving computational services closer to the network edge i.e., fog nodes, enhancing the speed of real-time applications. This architecture, with its dynamic computing environment and diverse IoT devices and tasks, demands a reliable and energy-efficient communication network. Joint optimization of computation offloading and task scheduling is a primary challenge, as it involves offloading tasks to optimal computational resources and scheduling them in an efficient order for operational efficacy. While offloading tasks to fog nodes reduces delay but raises energy utilization, offloading them to cloud servers reduces energy usage but raises computational costs and latency. Additionally, inefficient order of task execution (executing lower priority jobs before higher priority tasks) can disrupt system stability and reliability. Therefore, an effective joint optimal computation offloading and task scheduling strategy is essential. To this end, we propose a Multi-objective Arithmetic Optimization-based joint computation offloading and task scheduling algorithm, aiming to minimize energy consumption and transmission latency. Extensive simulations in MATLAB demonstrate the efficacy of the proposed algorithm in terms of designated optimization objectives.
Keywords:
Optimization
Processor scheduling
Cloud computing
Internet of Things
Delays
Servers
Resource management
Heuristic algorithms
Computational efficiency
Energy consumption
Task offloading
computation offloading
optimization algorithms
cloud-fog computing
arithmetic optimization
Journal
IF:
3.6
Papers:
9.7W
Citations:
29.4W
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