A Multi-Classifiers Based Algorithm for Energy Efficient Tasks Offloading in Fog Computing

Alasmari, Moteb K.; Alwakeel, Sami S.; Alohali, Yousef A.

doi:10.3390/s23167209

Cited by 10 publications

(1 citation statement)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The proposed solution is compared with the performance of deep Q-networks (DQN) and deep convolutional Q-networks (DCQN) to evaluate the effectiveness of different neural network approaches. In the work to [3], the primary aim is to improve the energy efficiency of task offloading within Fog Computing environments. Fog Computing entails the utilization of decentralized computing resources situated closer to the network's edge, and optimizing energy consumption is deemed critical for the efficiency of these systems.…”

Section: Ai-based Methodsmentioning

confidence: 99%

A Multi-Objective Approach for Optimizing IoT Applications Offloading in Fog-Cloud Environments with NSGA-II

mokni,

Yassa

2024

Preprint

View full text Add to dashboard Cite

Internet of Things (IoT) applications have gained widespread adoption across diverse sectors such as healthcare, smart agriculture, smart cities, transportation, and water management, leading to a substantial generation of Big Data. To efficiently process this voluminous data, there is a pressing need for a platform capable of handling large quantities. However, real-time applications face challenges in cloud processing due to high latency. Fog computing, serving as a complementary infrastructure to the cloud, emerges as a viable solution by facilitating task processing, networking, and data storage in cloud data centers accessible to mobile users. Task offloading emerges as a promising fog computing solution to overcome resource constraints in IoT applications. This involves the execution of part or all of mobile applications in the cloud, aiming to enhance execution time and reduce energy consumption. Our research concentrates on optimizing the IoT task offloading problem within heterogeneous environments, considering conflicting constraints. This optimization challenge is formulated as a multi-objective problem, emphasizing energy consumption and latency Quality of Service (QoS) metrics. Our proposed solution, named Tof-NSGAII, is tailored to respect the finite resources of fog computing by adeptly balancing workloads and meeting the latency requirements of IoT tasks. We have adapted the widely employed meta-heuristic, the non-dominant sorting genetic algorithm (NSGA-II), to generate a set of non-dominated multi-objective task offloading optimization solutions, considering both energy consumption and latency. Experimental results showcase the efficacy of Tof-NSGAII in generating task offloading solutions that judiciously distribute executed tasks between fog and cloud computing environments based on their specific requirements. Additionally, the generated non-dominated solutions demonstrate optimality in terms of energy consumption, boasting an average energy reduction of 12.18\% compared to alternative approaches. Notably, our approach introduces only a marginal increase in latency, amounting to 0.38\%, a difference that can be considered negligible.

show abstract

Section: Ai-based Methodsmentioning

confidence: 99%

A Multi-Objective Approach for Optimizing IoT Applications Offloading in Fog-Cloud Environments with NSGA-II

mokni,

Yassa

2024

Preprint

View full text Add to dashboard Cite

show abstract

An energy-aware module placement strategy in fog-based healthcare monitoring systems

Hossam,

Abdel-Galil,

Belal

2024

Cluster Comput

View full text Add to dashboard Cite

Fog computing and the Internet of Things (IoT) have revolutionized healthcare monitoring systems, enabling real-time health data collection and transmission while overcoming cloud computing limitations. However, efficiently selecting fog nodes for application modules with varying deadline requirements and ensuring adherence to quality of service (QoS) criteria pose significant challenges due to resource constraints and device limitations. In this paper, we present a novel two-layered hierarchical design for fog devices, leveraging cluster aggregation to optimize the selection of fog nodes for healthcare applications. We introduce three efficient algorithms to minimize system latency and reduce energy consumption in fog computing environments. Our proposed model is rigorously evaluated using the iFogSim toolkit and compared with cloud-based and latency-aware model [Mahmud R, Ramamohanarao K, Buyya R in ACM Transactions on Internet Technology.19, 2018, 10.1145/3186592]. In four distinct network topologies, our model exhibits an average latency reduction of at least 87% and energy consumption reduction of at least 76% when compared to the Cloud-based model. Similarly, when compared to the Latency-aware model proposed in [Mahmud R, Ramamohanarao K, Buyya R in ACM Transactions on Internet Technology. 19, 2018, 10.1145/3186592], our model showcases a minimum reduction of 43% in average latency and 27% in energy consumption. Our contribution lies in addressing the complexity of selecting fog nodes for application modules with diverse deadline requirements, while ensuring QoS. This work advances the field of real-time healthcare monitoring systems, promising substantial improvements in efficiency and effectiveness.

show abstract

A multi-objective approach for optimizing IoT applications offloading in fog–cloud environments with NSGA-II

Mokni,

Yassa

2024

J Supercomput

View full text Add to dashboard Cite

A Multi-Classifiers Based Algorithm for Energy Efficient Tasks Offloading in Fog Computing

Cited by 10 publications

References 31 publications

A Multi-Objective Approach for Optimizing IoT Applications Offloading in Fog-Cloud Environments with NSGA-II

A Multi-Objective Approach for Optimizing IoT Applications Offloading in Fog-Cloud Environments with NSGA-II

An energy-aware module placement strategy in fog-based healthcare monitoring systems

A multi-objective approach for optimizing IoT applications offloading in fog–cloud environments with NSGA-II

Contact Info

Product

Resources

About