Effective data placement for scientific workflows in mobile edge computing using genetic particle swarm optimization

Chen, Zheyi; Hu, Jia; Min, Geyong; Chen, Xing

doi:10.1002/cpe.5413

Cited by 22 publications

(15 citation statements)

References 26 publications

(50 reference statements)

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“…A computing algorithm based on genetic algorithm to allocate computing resources was presented, and the results showed that the algorithm could minimize the energy consumption of user equipment [30]. In [31], Chen et al studied the data placement strategy of the workflow in MEC, and adopted a method based on genetic algorithm particle swarm optimization, and the final result shows that this method can effectively reduce the data placement cost of MEC.…”

Section: Resource Allocationmentioning

confidence: 99%

Efficient task offloading using particle swarm optimization algorithm in edge computing for industrial internet of things

You

Tang

2021

J Cloud Comp

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As a new form of computing based on the core technology of cloud computing and built on edge infrastructure, edge computing can handle computing-intensive and delay-sensitive tasks. In mobile edge computing (MEC) assisted by 5G technology, offloading computing tasks of edge devices to the edge servers in edge network can effectively reduce delay. Designing a reasonable task offloading strategy in a resource-constrained multi-user and multi-MEC system to meet users’ needs is a challenge issue. In industrial internet of things (IIoT) environment, considering the rapid increase of industrial edge devices and the heterogenous edge servers, a particle swarm optimization (PSO)-based task offloading strategy is proposed to offload tasks from resource-constrained edge devices to edge servers with energy efficiency and low delay style. A multi-objective optimization problem that considers time delay, energy consumption and task execution cost is proposed. The fitness function of the particle represents the total cost of offloading all tasks to different MEC servers. The offloading strategy based on PSO is compared with the genetic algorithm (GA) and the simulated annealing algorithm (SA) through simulation experiments. The experimental results show that the task offloading strategy based on PSO can reduce the delay of the MEC server, balance the energy consumption of the MEC server, and effectively realize the reasonable resource allocation.

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Section: Resource Allocationmentioning

confidence: 99%

Efficient task offloading using particle swarm optimization algorithm in edge computing for industrial internet of things

You

Tang

2021

J Cloud Comp

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“…The applications, services, application programming interface (API) and model-based workload have been implemented in [21][22][23][24][25][26][27]. The virtual machines, container and serverless aware resources are offered during workload execution in the system.…”

Section: Related Workmentioning

confidence: 99%

Hybrid Workload Enabled and Secure Healthcare Monitoring Sensing Framework in Distributed Fog-Cloud Network

et al. 2021

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The Internet of Medical Things (IoMT) workflow applications have been rapidly growing in practice. These internet-based applications can run on the distributed healthcare sensing system, which combines mobile computing, edge computing and cloud computing. Offloading and scheduling are the required methods in the distributed network. However, a security issue exists and it is hard to run different types of tasks (e.g., security, delay-sensitive, and delay-tolerant tasks) of IoMT applications on heterogeneous computing nodes. This work proposes a new healthcare architecture for workflow applications based on heterogeneous computing nodes layers: an application layer, management layer, and resource layer. The goal is to minimize the makespan of all applications. Based on these layers, the work proposes a secure offloading-efficient task scheduling (SEOS) algorithm framework, which includes the deadline division method, task sequencing rules, homomorphic security scheme, initial scheduling, and the variable neighbourhood searching method. The performance evaluation results show that the proposed plans outperform all existing baseline approaches for healthcare applications in terms of makespan.

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“…GAPSO [26] combines the best characteristics of the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) to distribute accordingly the data that are required from each edge node that participates in a scientific workflow. In particular, GA's crossover and mutation functions are integrated into PSO, whose ability to quickly converge to a solution is exploited.…”

Section: High Level Descriptionmentioning

confidence: 99%

On the Use of Intelligent Models towards Meeting the Challenges of the Edge Mesh

et al. 2021

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Nowadays, we are witnessing the advent of the Internet of Things (IoT) with numerous devices performing interactions between them or with their environment. The huge number of devices leads to huge volumes of data that demand the appropriate processing. The “legacy” approach is to rely on Cloud where increased computational resources can realize any desired processing. However, the need for supporting real-time applications requires a reduced latency in the provision of outcomes. Edge Computing (EC) comes as the “solver” of the latency problem. Various processing activities can be performed at EC nodes having direct connection with IoT devices. A number of challenges should be met before we conclude a fully automated ecosystem where nodes can cooperate or understand their status to efficiently serve applications. In this article, we perform a survey of the relevant research activities towards the vision of Edge Mesh (EM), i.e., a “cover” of intelligence upon the EC. We present the necessary hardware and discuss research outcomes in every aspect of EC/EM nodes functioning. We present technologies and theories adopted for data, tasks, and resource management while discussing how machine learning and optimization can be adopted in the domain.

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Effective data placement for scientific workflows in mobile edge computing using genetic particle swarm optimization

Cited by 22 publications

References 26 publications

Efficient task offloading using particle swarm optimization algorithm in edge computing for industrial internet of things

Efficient task offloading using particle swarm optimization algorithm in edge computing for industrial internet of things

Hybrid Workload Enabled and Secure Healthcare Monitoring Sensing Framework in Distributed Fog-Cloud Network

On the Use of Intelligent Models towards Meeting the Challenges of the Edge Mesh

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