Design of Resource-Aware Load Allocation for Heterogeneous Fog Computing Environments

Hassan, Syed Rizwan; Ahmad, İshtiaq; Rehman, Ateeq Ur; Hussen, Seada; Hamam, Habib

doi:10.1155/2022/3543640

Cited by 4 publications

(2 citation statements)

References 29 publications

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“…The proposed policy provides a significant reduction in network consumption by assigning appropriate fog resources to clusters of cameras. The size of the information sensed by the edge nodes is calculated using the sensing rate of the sensors ( Hassan et al, 2022a ). The proposed algorithm assigns a cluster of edge devices to a suitable fog node to get the optimum performance of the paradigm.…”

Section: Resultsmentioning

confidence: 99%

Design of load-aware resource allocation for heterogeneous fog computing systems

Hassan,

Rehman,

Alsharabi

et al. 2024

PeerJ Computer Science

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The execution of delay-aware applications can be effectively handled by various computing paradigms, including the fog computing, edge computing, and cloudlets. Cloud computing offers services in a centralized way through a cloud server. On the contrary, the fog computing paradigm offers services in a dispersed manner providing services and computational facilities near the end devices. Due to the distributed provision of resources by the fog paradigm, this architecture is suitable for large-scale implementation of applications. Furthermore, fog computing offers a reduction in delay and network load as compared to cloud architecture. Resource distribution and load balancing are always important tasks in deploying efficient systems. In this research, we have proposed heuristic-based approach that achieves a reduction in network consumption and delays by efficiently utilizing fog resources according to the load generated by the clusters of edge nodes. The proposed algorithm considers the magnitude of data produced at the edge clusters while allocating the fog resources. The results of the evaluations performed on different scales confirm the efficacy of the proposed approach in achieving optimal performance.

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Section: Resultsmentioning

confidence: 99%

Design of load-aware resource allocation for heterogeneous fog computing systems

Hassan,

Rehman,

Alsharabi

et al. 2024

PeerJ Computer Science

Self Cite

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“…Another approach was designed for mapping sensor nodes to its parent fog node effectively, so that overall system performance could be improved. Authors designed this approach to handle varied computing capabilities of fog nodes and look to reduce delay and improve network utilization [ 19 ].…”

Section: Related Workmentioning

confidence: 99%

A Novel Framework for Fog-Assisted Smart Healthcare System with Workload Optimization

Abdellatif

Singh

Aldribi

et al. 2022

Computational Intelligence and Neuroscience

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Fog-assisted and IoT-enabled smart healthcare system with rapid response rates is the major area of concern now a days. Dynamic and heterogeneous fog networks are difficult to manage and a considerable amount of overhead could be realized while managing ever increasing load on foglets. Fog computing plays a vital role in managing ever increasing processing demands from diverse IoT-based applications. Smart healthcare systems work with the assistance of sensor-based devices and automatic data collection and processing can speed up overall system functionality. In the proposed work, a novel framework for smart health care is presented where a series of activities are performed with prime objective of reducing latency and execution time. Principal component analysis is used for feature reduction and support vector machines with radial basis function kernel is used for classification purpose. Workload optimization on the fog nodes is implemented using genetic algorithm. Data collection process also involves preprocessing as a leading step for generating cleaner data. Amalgamation of intelligent and optimization techniques in the presented framework certainly improves the efficiency of the overall system. Experimental results reveal that proposed work outperforms the existing fog-assisted smart healthcare systems in terms of latency, execution time, overall system accuracy, and system stability.

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

Hossam,

Abdel-Galil,

Belal

2024

Cluster Comput

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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.

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Design of Resource-Aware Load Allocation for Heterogeneous Fog Computing Environments

Cited by 4 publications

References 29 publications

Design of load-aware resource allocation for heterogeneous fog computing systems

Design of load-aware resource allocation for heterogeneous fog computing systems

A Novel Framework for Fog-Assisted Smart Healthcare System with Workload Optimization

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

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