Quality-aware energy efficient scheduling model for fog computing comprised IoT network

Narayana, Potu; Bhukya, Sreedhar; Jatoth, Chandrashekar; Premchand, P.

doi:10.1016/j.compeleceng.2021.107603

Cited by 16 publications

(3 citation statements)

References 17 publications

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“…The energy consumption ratio versus fluctuating load is another key metric that was evaluated. By making comparisons the metric reported the results for OBSM with said correlating measure obtained values again from recent methods QEESM [27], CFCA [28], as well as (QALS) [29], it can be seen that the effectiveness of the OBSM has increased.…”

Section: 2)mentioning

confidence: 93%

“…The Contemporary models "QEESM (Quality Aware Energy Efficient Scheduling Model) [27]", "CFCA (Container based Fog Computing Architecture) [28]", and "QALS (Quantum approach of load scheduling) [29]" are competent methods to perform data transmission with minimal droop ratio across IoT networks using fog computing. However, none of the aforesaid methods considering the context of the oversized delay sensitive data transmission.…”

Section: Related Researchmentioning

confidence: 99%

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Optimum Batch Scheduling Model for Quality Aware Delay Sensitive Data Transmission over Fog Enabled IOT Network

Potu,

Jatoth,

Parvataneni

2024

SCPE

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The emerging fog networks in the internet of things (IoT) applications provide flexibility and agility for service providers. The combination of fog nodes and edge nodes enable them to deliver a given network service. However, the selection of suitable edge and fog nodes and their scheduling still remain a research challenge. Finding a globally optimal scheduling of oversized data transmission over IoT applications for industrial requirements is crucial. Optimal batch scheduling has been regarded as a viable way to achieve optimal scheduling in other contemporary network models. This manuscript has projected an Optimum Batch Scheduling Model (OBSM) for Quality aware Delay Sensitive Data Transmission over Fog Enabled IoT Networks. A novel clustering technique has been proposed in this manuscript to group the transmission nodes (fog or edge nodes) and data packets, which further pairs each group of data with one of the corresponding node group to achieve delay sensitivity and other quality factors such as energy efficiency. The data scheduling between data and node group is drawn from the previous contribution -"Quality aware Energy Efficient Scheduling Model (QEESM) for Fog Enabled IoT Network". The simulation results have shown that, in terms of average make span rate, average round trip time, and energy consumption, the batch scheduling model OBSM performs noticeably better than the contemporary scheduling models. The OBSM scheduling model's average make-span rate, roundtrip time, as well as energy consumption per make span are 23.3 7.03, 17.8 5.2, and 11.33 6.9 joules, respectively, which conclusively demonstrate that the OBSM model outperforms the existing models. A novel batch scheduling algorithm has been proposed using a unique unsupervised learning approach that suggested to cluster the transmission requests and transmission channels in to multiple clusters.

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Section: 2)mentioning

confidence: 93%

Section: Related Researchmentioning

confidence: 99%

Optimum Batch Scheduling Model for Quality Aware Delay Sensitive Data Transmission over Fog Enabled IOT Network

Potu,

Jatoth,

Parvataneni

2024

SCPE

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“…In [7] the author formulated a task scheduling algorithm to tasks two approaches. QEESM reduce the energy consumption in a novel scheduling strategy that optimize the fog nodes.…”

Section: Related Workmentioning

confidence: 99%

An Effective analysis on various task scheduling algorithms in Fog computing

Choppara,

Mangalampalli

2023

EAI Endorsed Trans IoT

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Fog computing involved as an extension of cloud and distributed systems fog nodes allowing data to be processed closer to the edge device and reduces the latency and bandwidth, storage capacity of IoT tasks. Task scheduling in fog computing involves allocating the tasks in fog nodes based on factors such as node availability, processing power, memory, and network connectivity. In task scheduling we have various scheduling algorithms that are nature inspired and bio-inspired algorithms but still we have latency issues because it is an NP-hard problem. This paper reviews the existing task scheduling algorithms modeled by metaheuristic, nature inspired and machine learning which address the various scheduling parameters like cost, response time, energy consumption, quality of services, execution time, resource utilization, makespan, throughput but still parameters like trust, fault tolerance not addressed by many of the existing authors. Trust and fault tolerance gives an impact and task scheduling trust is necessary to tasks and assign responsibility to systems, while fault tolerance ensures that the system can continue to operate even when failures occur. A balance of trust and fault tolerance gives a quality of service and efficient task scheduling therefore this paper done analysis on parameters like trust, fault tolerance and given research directions.

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HGCSO: Energy Efficient Multi-objective Task Scheduling in Cloud-Fog Environment

Medishetti,

Pillareddy,

Muneeb

et al. 2024

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Quality-aware energy efficient scheduling model for fog computing comprised IoT network

Cited by 16 publications

References 17 publications

Optimum Batch Scheduling Model for Quality Aware Delay Sensitive Data Transmission over Fog Enabled IOT Network

Optimum Batch Scheduling Model for Quality Aware Delay Sensitive Data Transmission over Fog Enabled IOT Network

An Effective analysis on various task scheduling algorithms in Fog computing

HGCSO: Energy Efficient Multi-objective Task Scheduling in Cloud-Fog Environment

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