Everything as a resource: Foundations and illustration through Internet-of-things

Baker, Thar; Ugljanin, Emir; Faci, Noura; Sellami, Mohamed; Maamar, Zakaria; Kajan, Ejub

doi:10.1016/j.compind.2017.10.001

Cited by 25 publications

(13 citation statements)

References 30 publications

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“…In the fourth term, the authors want to expand the framework to tackle aspects of quality of service (QoS) and quality of experience (QoE). Both aspects are very relevant aspects nowadays in the literature of the topic, e.g., [48][49][50][51][52][53][54]. While it is true that these nascent initiatives are more relevant to mobile networks, it is also true that these aspects are quite applicable also in organizational cloud scenarios.…”

Section: Discussion Limitations Conclusion and Future Workmentioning

confidence: 99%

Governance, Risk, and Compliance in Cloud Scenarios

Brandis¹,

Dzombeta²,

Colomo‐Palacios

et al. 2019

Applied Sciences

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Cloud computing is changing the way organizations approach technology and its infrastructure. However, in spite of its attractiveness, cloud computing can be seen as a threat in terms of compliance. Given its intrinsic distributed nature, regulations and laws may differ and customers and cloud providers must find a way to balance increasing compliance pressures with cloud computing benefits. In this paper, the authors present a framework aimed to help organizations to cope with compliance aspects in their cloud-oriented environments. Built upon current literature on the topic and qualitative approaches, the framework has been implemented in two organizations. Results from its contribution are encouraging, leading to adopter organizations to less reported compliance violations and higher contribution of cloud computing to overall quality of service and organizational compliance management.

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Section: Discussion Limitations Conclusion and Future Workmentioning

confidence: 99%

Governance, Risk, and Compliance in Cloud Scenarios

Brandis¹,

Dzombeta²,

Colomo‐Palacios

et al. 2019

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…They used buffering, scheduling and rate limiting to have a better RA. Authors in [46] see everything as resources and they illustrated their idea through RA in internet of things. Their case study was a healthcare-driven system that was based on IoT.…”

Section: Methods Based On Heuristic Algorithmmentioning

confidence: 99%

“…They proposed a new RA method for such a system. The evaluation of the proposed method which is called IoTR4HealthCare system was benchmarked against two existing systems using cost and latency criteria [46]. Hatti et al [47] proposed a RA algorithm based on fuzzy classification of jobs.…”

Section: Methods Based On Heuristic Algorithmmentioning

confidence: 99%

IoT Resource Allocation and Optimization Based on Heuristic Algorithm

Sangaiah

Hosseinabadi

Shareh

et al. 2020

Sensors

141

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The Internet of Things (IoT) is a distributed system that connects everything via internet. IoT infrastructure contains multiple resources and gateways. In such a system, the problem of optimizing IoT resource allocation and scheduling (IRAS) is vital, because resource allocation (RA) and scheduling deals with the mapping between recourses and gateways and is also responsible for optimally allocating resources to available gateways. In the IoT environment, a gateway may face hundreds of resources to connect. Therefore, manual resource allocation and scheduling is not possible. In this paper, the whale optimization algorithm (WOA) is used to solve the RA problem in IoT with the aim of optimal RA and reducing the total communication cost between resources and gateways. The proposed algorithm has been compared to the other existing algorithms. Results indicate the proper performance of the proposed algorithm. Based on various benchmarks, the proposed method, in terms of “total communication cost”, is better than other ones.

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“…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]. The proactive resource allocation method based on prediction is an effective solution to ensure the timelessness of resource allocation.…”

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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Everything as a resource: Foundations and illustration through Internet-of-things

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 25 publications

References 30 publications

Governance, Risk, and Compliance in Cloud Scenarios

Governance, Risk, and Compliance in Cloud Scenarios

IoT Resource Allocation and Optimization Based on Heuristic Algorithm

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

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