Energy and delay efficient fog computing using caching mechanism

Shahid, Muzammil Hussain; Hameed, Ahmad Raza; Islam, Saif ul; Khattak, Hasan Ali; Din, Ikram Ud; Rodrigues, Joel J. P. C.

doi:10.1016/j.comcom.2020.03.001

Cited by 67 publications

(22 citation statements)

References 15 publications

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“…Several energy-efficient techniques for fog computing have been identified for the purpose of energy-aware task offloading, energy-aware fog node placement, energy-aware device control, and energy-aware energy harvesting [13]. A number of studies have focused on energy-aware load balancing in fog environments [14,15]. In [14], the authors proposed an energy-aware load-balancing and scheduling method for equipment in a smart factory using fog nodes.…”

Section: Energy-efficient Fog Computingmentioning

confidence: 99%

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Green Demand Aware Fog Computing: A Prediction-Based Dynamic Resource Provisioning Approach

et al. 2022

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Fog computing could potentially cause the next paradigm shift by extending cloud services to the edge of the network, bringing resources closer to the end-user. With its close proximity to end-users and its distributed nature, fog computing can significantly reduce latency. With the appearance of more and more latency-stringent applications, in the near future, we will witness an unprecedented amount of demand for fog computing. Undoubtedly, this will lead to an increase in the energy footprint of the network edge and access segments. To reduce energy consumption in fog computing without compromising performance, in this paper we propose the Green-Demand-Aware Fog Computing (GDAFC) solution. Our solution uses a prediction technique to identify the working fog nodes (nodes serve when request arrives), standby fog nodes (nodes take over when the computational capacity of the working fog nodes is no longer sufficient), and idle fog nodes in a fog computing infrastructure. Additionally, it assigns an appropriate sleep interval for the fog nodes, taking into account the delay requirement of the applications. Results obtained based on the mathematical formulation show that our solution can save energy up to 65% without deteriorating the delay requirement performance.

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Section: Energy-efficient Fog Computingmentioning

confidence: 99%

“…In [14], the authors proposed an energy-aware load-balancing and scheduling method for equipment in a smart factory using fog nodes. Meanwhile, the load-balancing solution in [15] selects a fog node which has a high energy and high computation availability in order to process a request.…”

Section: Energy-efficient Fog Computingmentioning

confidence: 99%

Green Demand Aware Fog Computing: A Prediction-Based Dynamic Resource Provisioning Approach

et al. 2022

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“…There are many techniques that could be used to save energy in a fog network. Some of the used techniques are based on data caching [41]- [44]. Such techniques depend on retrieving the required data from the cached data [45] to save time and energy.…”

Section: Related Workmentioning

confidence: 99%

Energy Coherent Fog Networks Using Multi-Sink Wireless Sensor Networks

et al. 2021

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Fog computing (FC) models the cloud computing paradigm expedient by bridging the breach between centralized data servers and diverse terrestrially distributed applications. It wields various wireless sensor networks (WSNs) that sprawl in the core of any IoT applications. Consequently, the operation of fog network turns on the efficiency of WSNs operation, while the all-inclusive network energy consumption depends on both FC and WSNs operation. This paper addresses how dissimilar organizations of a fog network can influence its effectiveness and energy savings. Chiefly, it appraises whether deploying multisink nodes in close enough vicinity to the fog nodes can give in energy savings and foster coherent data communication between WSNs and fog networks. To assess the multi-sink assignment problem the following four criteria are used: (i) Distance from the fog network nodes; (ii) Nodes degree; (iii) Sink nodes energy; and (iv) Sink nodes processing capabilities. This paper suggests four novel solutions to the multisink connectivity for some challenges of fog networks deeming: (i) Window Nondominant Set (WNS); (ii) Evaluation Based Approach (EBA); (iii) Harris Hawks Optimizer (HHO); and (iv) Modified HHO (MHHO). Distinct sets of experiments are conducted to check out algorithmic performance. The performance of all algorithms is measured and then compared to each other in terms of power consumption, runtime, packet loss, and localization error. One of the key supremacies of our approaches is the utilization of fog network for sensor networks data processing, principally with the large-scale networks. Yet, the communication challenges could need further study due to the limited communication range of the sensors.

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“…As IoT continues to grow rapidly, it is becoming a massive network that links almost every element of our activities [11]. IoT devices are usually powered using batteries.…”

Section: Introductionmentioning

confidence: 99%