Node cooperation for workload offloading in a fog computing network via multi-objective optimization

Vakilian, Shakoor; Fanian, Ali; Falsafain, Hossein; Gulliver, Thomas Aaron

doi:10.1016/j.jnca.2022.103428

Cited by 7 publications

(2 citation statements)

References 43 publications

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“…In an IoT system based on the cloud-edge architecture, terminal devices with relatively limited computational resources can transfer their computing tasks to the edge servers nearby. Furthermore, edge servers can choose to complete these offloaded tasks or defer their tasks to the cloud center [2]. In this case, local computational resources are mainly dedicated to the tasks with higher requirements for response time, therefore improving the response speed and alleviating the bottleneck in the cloud [3].…”

Section: Introductionmentioning

confidence: 99%

A Blockchain-Based Distributed Computational Resource Trading Strategy for Internet of Things Considering Multiple Preferences

Wang

Ai²,

Cao

et al. 2023

Symmetry

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The architecture of cloud–edge collaboration can improve the efficiency of Internet of Things (IoT) systems. Recent studies have pointed out that using IoT terminal devices as destinations for computing offloading can promote further optimized allocation of computational resources. However, in practice, this idea encounters the problem that participants might lack the motivation to take over computational tasks from others. Although the edge and the terminal are provided with symmetrical positions in collaborative offloading, their computational resources and capabilities are asymmetric. To mitigate this issue, this paper designs a distributed strategy for the trading of computational resources. The most prominent feature of our strategy is its multi-preference optimization objective that takes into account the overall satisfaction with task delay, energy cost, trading prices, and user reputation of participants. In addition, this paper proposes a system architecture based on the Blockchain-as-a-Service (BaaS) design to give full play to the good distributed technology features of blockchain, such as decentralization, traceability, immutability, and automation. Meanwhile, BaaS delivers decentralized identifier (DID) based distributed identity infrastructure for the distributed computational resource trading stakeholders as well. In the simulation evaluation, we compare our trading strategy based on a matching mechanism called multi-preference matching (MPM) to trading using the classical double auction (DA) matching mechanism. The results show that our computational resource trading strategy is able to offload and execut more tasks, achieving a better throughput compared to the DA-based strategy.

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Section: Introductionmentioning

confidence: 99%

A Blockchain-Based Distributed Computational Resource Trading Strategy for Internet of Things Considering Multiple Preferences

Wang

Ai²,

Cao

et al. 2023

Symmetry

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“…The possession of correct information on the energy demand-response, which is even enhanced with energy supply and storage with complex knowledge of the status of other nodes that are active in the PED trading, is crucial [31]. Here, prediction systems play a vital role and are one of the key aspects in smart city infrastructure, taking into account optimal node task offloading in the fog environment [32] as well as its proper scheduling [33]. Therefore, prediction techniques shall play an important role not only for the prediction task but also for the system stability monitoring and predictability evaluation, anomaly detection, etc.…”

mentioning

confidence: 99%

Living Lab Long-Term Sustainability in Hybrid Access Positive Energy Districts—A Prosumager Smart Fog Computing Perspective

Vohnout

Bukovský

Chou

et al. 2023

IEEE Internet Things J.

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Living Lab, one of the recent emerging smart city concepts, faces long-term sustainability challenges associated with its complexity and breadth of use. To be efficient, it must rely on comprehensive set of information distributed appropriately among all stakeholders to unleash its full innovation potential. This is especially true in the case of positive energy districts, where timely data dissemination is essential for prosumager decisions and their greedy behaviour. This paper interconnects intelligent information exchange, supported by ultra-low latency hybrid access network infrastructure, with the clever use of available fog computing resources to properly disseminate complex energy details to all participating entities. As the optimal distribution of information using proper task offloading is the convergence problem, we recalled higher-order neural units that helped maintain computational and energy efficiency in conjunction with the preservation of the overall system stability. We have achieved a reliable hourly energy consumption prediction with a computationally very lightweight alternative to commonly used deep neural network approaches that can be deployed on available smart appliances with ease. The application and simulation were performed on the dataset provided by one of Europe's smart city pioneers, where the prosumager positive energy district transition has already started.

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Non-Convex Optimization of Resource Allocation in Fog Computing Using Successive Approximation

Li,

Liu,

et al. 2024

J Syst Sci Complex

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Node cooperation for workload offloading in a fog computing network via multi-objective optimization

Cited by 7 publications

References 43 publications

A Blockchain-Based Distributed Computational Resource Trading Strategy for Internet of Things Considering Multiple Preferences

A Blockchain-Based Distributed Computational Resource Trading Strategy for Internet of Things Considering Multiple Preferences

Living Lab Long-Term Sustainability in Hybrid Access Positive Energy Districts—A Prosumager Smart Fog Computing Perspective

Non-Convex Optimization of Resource Allocation in Fog Computing Using Successive Approximation

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