A Heterogeneous Cloud-Edge Collaborative Computing Architecture with Affinity-Based Workflow Scheduling and Resource Allocation for Internet-of-Things Applications

Lyu, Shuyu; Dai, Xinfa; Zhong, Ma; Ying, Z. G.; Liu, Xing; Gao, Yi; HU, Zhekun

doi:10.1007/s11036-023-02113-x

Cited by 5 publications

(2 citation statements)

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“…Enter the hybrid cloud-edge paradigm-a groundbreaking approach that redefines the landscape of computational research. Hybrid cloud-edge environments seamlessly integrate the computational capabilities and expansive data storage of cloud infrastructure with the real-time processing prowess of edge computing 5,9 . By decentralizing computational tasks and distributing them across both cloud and edge nodes, hybrid environments promise to revolutionize the way scientific workflows are orchestrated.…”

Section: Introductionmentioning

confidence: 99%

“…At the core of this paradigm shift lies the notion of proximity-aware computing. Edge computing resources, situated in close proximity to the data sources they serve, offer a compelling advantage in terms of reduced latency and enhanced data locality [9][10][11] . Tasks that require real-time processing or access to localized data can be offloaded to edge nodes, bypassing the latency inherent in cloud-based processing.…”

Section: Introductionmentioning

confidence: 99%

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Enhancing Workflow Efficiency: A Modified Firefly Algorithm for Hybrid Cloud-Edge Environments

Alsadie,

Alsulami

2024

Preprint

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Efficient scheduling of scientific workflows in cloud computing environments is essential for optimizing resource utilization and minimizing completion time. In this study, we comprehensively evaluate different scheduling algorithms, focusing on the Modified Firefly Optimization Algorithm (ModFOA) in comparison with existing methods like Ant Colony Optimization (ACO), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO). Our investigation considers key performance metrics such as makespan, resource utilization, and energy consumption across diverse configurations and scenarios. Scientific workflows often involve intricate tasks with dependencies, posing challenges for efficient scheduling. While existing algorithms have shown promise, they may not fully address the unique requirements of cloud environments, leading to suboptimal outcomes. Therefore, we propose evaluating ModFOA’s effectiveness in scheduling scientific workflows in the cloud. Through comparative analysis, ModFOA demonstrates superior performance in terms of makespan, achieving lower completion times across various configurations. Additionally, ModFOA exhibits competitive resource utilization and moderate energy consumption, positioning it as a promising solution for optimizing workflow scheduling in cloud environments. This study underscores the significance of selecting efficient scheduling algorithms and highlights ModFOA’s potential in improving workflow scheduling and resource management in cloud environments. Further research could focus on refining ModFOA parameters and validating its practicality in real-world cloud computing scenarios.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%