Learning for Smart Edge: Cognitive Learning-Based Computation Offloading

Hao, Yixue; Jiang, Yinging; Hossain, M. Shamim; Alhamid, Mohammed F.; Amin, Syed Umar

doi:10.1007/s11036-018-1119-7

Cited by 10 publications

(2 citation statements)

References 31 publications

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“…The authors in [ 8 ] have proposed a dynamic task allocation scheme that finds an optimal trade-off between service delay and quality loss constraints in VFC. The event-triggered task allocation problem is solved using a binary PSO algorithm and simulated in real-world vehicular mobility traces for video streaming and real-time object recognition tasks [ 41 ]. Considering the distributed capacity, the range and types of user applications and the mobility of IoT devices, Bittencourt et al [ 42 ] analyzed the significance of concurrent, first-come first-served (FCFS), and delay-priority scheduling policies to improve the execution time in fog environments.…”

Section: Related Workmentioning

confidence: 99%

SONG: A Multi-Objective Evolutionary Algorithm for Delay and Energy Aware Facility Location in Vehicular Fog Networks

Hussain

Azar

Ahmed

et al. 2023

Sensors

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With the emergence of delay- and energy-critical vehicular applications, forwarding sense-actuate data from vehicles to the cloud became practically infeasible. Therefore, a new computational model called Vehicular Fog Computing (VFC) was proposed. It offloads the computation workload from passenger devices (PDs) to transportation infrastructures such as roadside units (RSUs) and base stations (BSs), called static fog nodes. It can also exploit the underutilized computation resources of nearby vehicles that can act as vehicular fog nodes (VFNs) and provide delay- and energy-aware computing services. However, the capacity planning and dimensioning of VFC, which come under a class of facility location problems (FLPs), is a challenging issue. The complexity arises from the spatio-temporal dynamics of vehicular traffic, varying resource demand from PD applications, and the mobility of VFNs. This paper proposes a multi-objective optimization model to investigate the facility location in VFC networks. The solutions to this model generate optimal VFC topologies pertaining to an optimized trade-off (Pareto front) between the service delay and energy consumption. Thus, to solve this model, we propose a hybrid Evolutionary Multi-Objective (EMO) algorithm called Swarm Optimized Non-dominated sorting Genetic algorithm (SONG). It combines the convergence and search efficiency of two popular EMO algorithms: the Non-dominated Sorting Genetic Algorithm (NSGA-II) and Speed-constrained Particle Swarm Optimization (SMPSO). First, we solve an example problem using the SONG algorithm to illustrate the delay–energy solution frontiers and plotted the corresponding layout topology. Subsequently, we evaluate the evolutionary performance of the SONG algorithm on real-world vehicular traces against three quality indicators: Hyper-Volume (HV), Inverted Generational Distance (IGD) and CPU delay gap. The empirical results show that SONG exhibits improved solution quality over the NSGA-II and SMPSO algorithms and hence can be utilized as a potential tool by the service providers for the planning and design of VFC networks.

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Section: Related Workmentioning

confidence: 99%

SONG: A Multi-Objective Evolutionary Algorithm for Delay and Energy Aware Facility Location in Vehicular Fog Networks

Hussain

Azar

Ahmed

et al. 2023

Sensors

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“…In [75], the problem of the computational offloading scheme is investigated, in which a cognitive learning-based computation offloading (CLCO) scheme is proposed. In addition, an architecture is designed for the computational offloading task on the basis of the learning mechanism.…”

Section: ) Clcomentioning

confidence: 99%

Towards Mobile Edge Computing: Taxonomy, Challenges, Applications and Future Realms

et al. 2020

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The realm of cloud computing has revolutionized access to cloud resources and their utilization and applications over the Internet. However, deploying cloud computing for delay critical applications and reducing the delay in access to the resources are challenging. The Mobile Edge Computing (MEC) paradigm is one of the effective solutions, which brings the cloud computing services to the proximity of the edge network and leverages the available resources. This paper presents a survey of the latest and state-of-the-art algorithms, techniques, and concepts of MEC. The proposed work is unique in that the most novel algorithms are considered, which are not considered by the existing surveys. Moreover, the chosen novel literature of the existing researchers is classified in terms of performance metrics by describing the realms of promising performance and the regions where the margin of improvement exists for future investigation for the future researchers. This also eases the choice of a particular algorithm for a particular application. As compared to the existing surveys, the bibliometric overview is provided, which is further helpful for the researchers, engineers, and scientists for a thorough insight, application selection, and future consideration for improvement. In addition, applications related to the MEC platform are presented. Open research challenges, future directions, and lessons learned in area of the MEC are provided for further future investigation.

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Edge resource slicing approaches for latency optimization in AI-edge orchestration

Chandrika¹,

Mekala

Srivastava

2022

Cluster Comput

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Learning for Smart Edge: Cognitive Learning-Based Computation Offloading

Cited by 10 publications

References 31 publications

SONG: A Multi-Objective Evolutionary Algorithm for Delay and Energy Aware Facility Location in Vehicular Fog Networks

SONG: A Multi-Objective Evolutionary Algorithm for Delay and Energy Aware Facility Location in Vehicular Fog Networks

Towards Mobile Edge Computing: Taxonomy, Challenges, Applications and Future Realms

Edge resource slicing approaches for latency optimization in AI-edge orchestration

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