Optimal Distribution of Workloads in Cloud-Fog Architecture in Intelligent Vehicular Networks

Abbasi, Mahdi; Yaghoobikia, Mina; Rafiee, Milad; Khosravi, Mohammad R.; Menon, Varun G.

doi:10.1109/tits.2021.3071328

Cited by 17 publications

(5 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Validating the proposed algorithm by performing 30 independent runs, for each run, the best obtained delay and energy consumption were recorded to get average result [31]. The main parameters for validating the performance of the algorithms are summarized in Table II referring to [20], and NPSO parameters referring to [32] that indicate and the represents the suitable settings to provide the best convergence rate. From the simulation experiments, it is evident that the MLLF outperforms the compared algorithms with respect to reducing the delay to the minimum value, which can guarantee a reduction in transmission delay.…”

Section: Simulation Set-up and Resultsmentioning

confidence: 99%

“…Figure 6 compares the proposed algorithm, NPSO, non-linear optimization [20], MOPSO-DC, and NSGA-II regarding the energy consumption when D_max= 100. Recall the average delay threshold of the MLLF in Figure 5; that is, the normal delay threshold is (-∞, 100).…”

Section: Simulation Set-up and Resultsmentioning

confidence: 99%

“…However, GA consumes more energy during computation than other meta-heuristic approaches because it adopts more parameters and does not consider other state-of-the-art comparative algorithms for evaluation. Referring to [20] proposed the idea of distributing workloads in an IoT edgecloud computing system while guaranteeing energy efficiency and minimal delay via a delay-based workload allocation algorithm (DBWA). The algorithm uses the Lyapunov drift-plus-penalty theory to reduce the system's energy consumption with a granular delay for each job.…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Workload Allocation Toward Energy Consumption-Delay Trade-Off in Cloud-Fog Computing Using Multi-Objective NPSO Algorithm

et al. 2023

View full text Add to dashboard Cite

The Internet of Things (IoT) generates massive data from smart devices that demand responses from cloud servers. However, sending tasks to the cloud reduces the power consumed by the users' devices, but increases the transmission delay of the tasks. In contrast, sending tasks to the fog server reduces the transmission delay due to the shorter distance between the user and the server. However, this occurs at the user end's expense of higher energy consumption. Thus, this study proposes a mathematical framework for workload allocation to model the power consumption and delay functions for both fog and clouds. After that, a Modified Least Laxity First (MLLF) algorithm was proposed to reduce the maximum delay threshold. Furthermore, a new multi-objective approach, namely the Non-dominated Particle Swarm Optimization (NPSO), is proposed to reduce energy consumption and delay compared to the state-of-theart algorithms. The simulation results show that NPSO outperforms the state-of-the-art algorithm in reducing energy consumption, while NGSA-II proves its effectiveness in reducing transmission delay compared to the other algorithms in the experimental simulation. In addition, the MLLF algorithm reduces the maximum delay threshold by approximately 11% compared with other related algorithms. Moreover, the results prove that metaheuristics are more appropriate for distributed computing.

show abstract

Section: Simulation Set-up and Resultsmentioning

confidence: 99%

Section: Simulation Set-up and Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Workload Allocation Toward Energy Consumption-Delay Trade-Off in Cloud-Fog Computing Using Multi-Objective NPSO Algorithm

et al. 2023

View full text Add to dashboard Cite

show abstract

“…3 To address the above-mentioned challenges, the edge computing paradigm has emerged, which provides the cloud services at the edge of the network with minimum latency and energy consumption. 4,5 The main advance of edge computing over centralized cloud servers is to distribute the real-time applications on the local edge devices that minimize the communication overhead and congestion of the network. The distributed edge devices are useful for processing delay-sensitive applications with better services, which increases the attention of the academicians and developers while improving the performance of different stakeholders (i.e., real-time end-users, local edge devices, and centralized cloud servers), and meeting multiple Quality-of-Service (QoS) constraints.…”

Section: Introductionmentioning

confidence: 99%

“…To address the above‐mentioned challenges, the edge computing paradigm has emerged, which provides the cloud services at the edge of the network with minimum latency and energy consumption 4,5 . The main advance of edge computing over centralized cloud servers is to distribute the real‐time applications on the local edge devices that minimize the communication overhead and congestion of the network.…”

Section: Introductionmentioning

confidence: 99%

A comprehensive survey on nature‐inspired algorithms and their applications in edge computing: Challenges and future directions

2021

View full text Add to dashboard Cite

Driven by the vision of real-time applications and smart communication, recent years have witnessed a paradigm shift from centralized cloud computing toward distributed edge computing. The main features of edge computing are to drag the cloud services toward the network edge with dramatic reductions of latency while increasing the resource utilization of the network and computing devices. Being the natural extension of cloud computing, edge computing inherits a variety of research challenges and brings forth different new issues to solve.These challenges are dealing with solving complex optimization problems including scheduling and processing real-time applications. Nature-inspired meta-heuristic (NIMH) algorithm is an overarching term in the field of an optimization problem that provides robust solutions to the NP-complete problems, from computationally tractable approximate solutions to real-time optimization strategies. Nowadays, different NIMH algorithms have been applied in the field of edge computing for solving various research challenges including resource placement and scheduling, communication, mobility, and edge controlling with higher efficiency. In this survey, we classify the existing NIMH into three categories based on their nature of works and included fuzzy logic and systems in the field of edge networks along with different research challenges. Further, we introduce different challenges and future directions to identify promising research works in edge computing.

show abstract

Service delay and optimization of the energy efficiency of a system in fog-enabled smart cities

Wang,

Shafik,

Seong

et al. 2023

Alexandria Engineering Journal

View full text Add to dashboard Cite

Optimal Distribution of Workloads in Cloud-Fog Architecture in Intelligent Vehicular Networks

Cited by 17 publications

References 24 publications

Workload Allocation Toward Energy Consumption-Delay Trade-Off in Cloud-Fog Computing Using Multi-Objective NPSO Algorithm

Workload Allocation Toward Energy Consumption-Delay Trade-Off in Cloud-Fog Computing Using Multi-Objective NPSO Algorithm

A comprehensive survey on nature‐inspired algorithms and their applications in edge computing: Challenges and future directions

Service delay and optimization of the energy efficiency of a system in fog-enabled smart cities

Contact Info

Product

Resources

About