COMITMENT: A Fog Computing Trust Management Approach

Al-Khafajiy, Mohammed; Baker, Thar; Asim, Muhammad; Guo, Zehua; Ranjan, Rajiv; Longo, Antonella; Puthal, Deepak; Taylor, Mark

doi:10.1016/j.jpdc.2019.10.006

Cited by 92 publications

(45 citation statements)

References 65 publications

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“…In addition, a cloud workflow scheduling algorithm is proposed based on an attack-defense game model, where a task-VM mapping algorithm is presented to improve the workflow efficiency and different VMs are provided for workflow executions [29]. A fog computing trust management approach that assesses and manages the trust levels of the nodes is proposed to reduce the malicious attacks and the service response time in the fog computing environment [30]. Everything-as-a-Resource as a paradigm is proposed to design collaborative applications for the web [31].…”

Section: T Thmentioning

confidence: 99%

A proactive resource allocation method based on adaptive prediction of resource requests in cloud computing

Chen

Wang

Liu

2021

J Wireless Com Network

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With the development of big data and artificial intelligence, cloud resource requests present more complex features, such as being sudden, arriving in batches and being diverse, which cause the resource allocation to lag far behind the resource requests and an unbalanced resource utilization that wastes resources. To solve this issue, this paper proposes a proactive resource allocation method based on the adaptive prediction of the resource requests in cloud computing. Specifically, this method first proposes an adaptive prediction method based on the runs test that improves the prediction accuracy of resource requests, and then, it builds a multiobjective resource allocation optimization model, which alleviates the latency of the resource allocation and balances the utilizations of the different types of resources of a physical machine. Furthermore, a multiobjective evolutionary algorithm, the Nondominated Sorting Genetic Algorithm with the Elite Strategy (NSGA-II), is improved to further reduce the resource allocation time by accelerating the solution speed of the multiobjective optimization model. The experimental results show that this method realizes the balanced utilization between the CPU and memory resources and reduces the resource allocation time by at least 43% (10 threads) compared with the Improved Strength Pareto Evolutionary algorithm (SPEA2) and NSGA-II methods.

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Section: T Thmentioning

confidence: 99%

A proactive resource allocation method based on adaptive prediction of resource requests in cloud computing

Chen

Wang

Liu

2021

J Wireless Com Network

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“…Authors have provided some recommendations that can help in addressing, at least partially, the issue of trust in the Fog environment such as using blockchain technology to maintain the log files, which has the records of events and messages exchanging in Fog nodes in a distributed way. 84,[106][107][108][109][110][111] So, if a Fog node misbehaves, other trusted nodes can discover that. In other words, using Blockchain can enable a trustworthy IoT-Fog-Cloud architecture.…”

Section: Trust Managementmentioning

confidence: 99%

Fog computing security and privacy for the Internet of Thing applications: State‐of‐the‐art

Alzoubi

Osmanaj

Jaradat

et al. 2020

Security and Privacy

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Fog nodes are implemented near to end‐users Internet of Things (IoT) devices, which mitigate the impact of low latency, location awareness, and geographic distribution unsupported features of many IoT applications. Moreover, Fog computing decreases the data offload into the Cloud, which decreases the response time. Despite these benefits, Fog computing faces many challenges in meeting security and privacy requirements. These challenges occur due to the limitations of Fog computing resources. In fact, Fog computing may add new security and privacy issues. Although many papers have discussed the Fog security and privacy issues recently, most of these papers have discussed these issues at a very high level. This paper provides a comprehensive understanding of Fog privacy and security issue. In this survey, we review the literature on Fog computing to draw the state‐of‐the‐art of the security and privacy issues raised by Fog computing. The findings of this survey reveal that studying Fog computing is still in its infant stage. Many questions are yet to be answered to address the privacy and security challenges of Fog computing.

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“…In [27] a community-based technique is proposed to develop and propagate trust among vehicles. Similarly, in [28], a prognostic approach based on historical QoS experiences is used to build trust. In [29] a trust model based on fuzzy logic is proposed for fog computing.…”

Section: Related Work a Trusted Vehicular Task Offloadingmentioning

confidence: 99%

Blockchain-Based Reputation Management for Task Offloading in Micro-Level Vehicular Fog Network

et al. 2020

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With the widespread adoption of the internet of things (IoT) technologies towards building a smart city, connected devices often offload computation tasks to nearby edge locations (base stations) to reduce overall computation and network delay. However, serving an ever-increasing number of end devices at these traditional edge locations is becoming impossible, subsequently making them fail to deliver the agreed quality of service to all requesting devices. However, the backend cloud data center is available to serve these requests but incurred additional communication delay, thus, unsuitable for delay-sensitive applications. Furthermore, the fact that the underlying network is inherently ad hoc which makes it prone to malicious nodes affecting its overall performance. In this work, we propose a secure fog computing paradigm where roadside units (RSUs) are used to offload tasks to nearby fog vehicles based on repute scores maintained at a distributed blockchain ledger. The experimental results demonstrate a significant performance gain in terms of queuing time, end-to-end delay, and task completion rate when compared to the baseline queuing-based task offloading scheme.

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COMITMENT: A Fog Computing Trust Management Approach

Abstract: The version presented here may differ from the published version or from the version of the record. Please see the repository URL above for details on accessing the published version and note that access may require a subscription.

Cited by 92 publications

References 65 publications

A proactive resource allocation method based on adaptive prediction of resource requests in cloud computing

A proactive resource allocation method based on adaptive prediction of resource requests in cloud computing

Fog computing security and privacy for the Internet of Thing applications: State‐of‐the‐art

Blockchain-Based Reputation Management for Task Offloading in Micro-Level Vehicular Fog Network

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