Architectural Resilience in Cloud, Fog and Edge Systems: A Survey

Prokhorenko, Victor; Babar, Muhammad Ali

doi:10.1109/access.2020.2971007

Cited by 52 publications

(24 citation statements)

References 130 publications

(121 reference statements)

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“…In the literature, many others have approached the Cloud security topic, such as A4Cloud FP7 Project [ 26 ], Cloud Broker Architecture [ 27 ], or Phantom [ 28 ]. Prokhorenko et al even though they address the area of data security and trustworthiness, in order to improve the architectural resilience in Cloud, Fog, and Edge systems, all points of identification, authorization, and authentication are made in the Cloud.…”

Section: Methodsmentioning

confidence: 99%

“…Prokhorenko et al even though they address the area of data security and trustworthiness, in order to improve the architectural resilience in Cloud, Fog, and Edge systems, all points of identification, authorization, and authentication are made in the Cloud. They do not have a mechanism for extending these resilience mechanisms to the Fog or Edge area of the system [ 26 ].…”

Section: Methodsmentioning

confidence: 99%

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Method to Increase Dependability in a Cloud-Fog-Edge Environment

Stan

Enyedi

Corches

et al. 2021

Sensors

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Robots can be very different, from humanoids to intelligent self-driving cars or just IoT systems that collect and process local sensors’ information. This paper presents a way to increase dependability for information exchange and processing in systems with Cloud-Fog-Edge architectures. In an ideal interconnected world, the recognized and registered robots must be able to communicate with each other if they are close enough, or through the Fog access points without overloading the Cloud. In essence, the presented work addresses the Edge area and how the devices can communicate in a safe and secure environment using cryptographic methods for structured systems. The presented work emphasizes the importance of security in a system’s dependability and offers a communication mechanism for several robots without overburdening the Cloud. This solution is ideal to be used where various monitoring and control aspects demand extra degrees of safety. The extra private keys employed by this procedure further enhance algorithm complexity, limiting the probability that the method may be broken by brute force or systemic attacks.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Method to Increase Dependability in a Cloud-Fog-Edge Environment

Stan

Enyedi

Corches

et al. 2021

Sensors

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“…Thus, a resilient system can prevent, tolerate, mitigate, remove, and predict failures [108], [109]. In a system with devices connected to a network, resilience is responsible for maintaining or recovering the communication service between devices, regardless of network failures [110], [111]. The concept of resilience is the system's ability to resist failures [112] using recoverability (survivability), adaptability, and the capacity to manage failures [13].…”

Section: ) Resiliencementioning

confidence: 99%

“…A resilient fog-based IoT system data flow must prevent data losses caused by the network connection failure between mist/fog components. The fog also needs to deal with data loss due to low storage capacity in mist/fog nodes memory [111].…”

Section: ) Resiliencementioning

confidence: 99%

A Survey on Trustworthiness for the Internet of Things

Ribeiro

Kamienski

2021

IEEE Access

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IoT systems use sensors to collect data from smart environments and manage resources through data analysis. An IoT system deals with many connected devices with different network and hardware constraints in a real-world scenario. An IoT system needs to handle low-latency data analysis, security threats, internal vulnerabilities, and network disconnections, which cause data loss and incorrect decisions. Trustworthiness (also known as dependability) provides various features for an IoT end-to-end data flow, such as resilience, security, availability, reliability, scalability, maintainability, heterogeneity, hardware resources management, fault management policies, and data quality. This paper presents a survey on trustworthiness and dependability in IoT systems and proposes the Trustworthiness for IoT Framework (TW-IoT) to provide trustworthiness at the data level for mist and fog-based IoT systems. The TW-IoT framework provides data trustworthiness to ensure a continuous and uninterrupted operation of IoT data flow. We also discuss challenges and trade-offs related to data trustworthiness in IoT.

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“…Fog computing is considered as an enabler of the Internet of Things (IoT) and a key technology for fifth-generation (5G) and beyond networks. Fog computing is an extension of the cloud computing paradigm, wherein the distributed computing and storage resources across the network are exploited to support the functionalities of a centralized cloud data center [1][2][3][4]. The key benefits that the fog computing paradigm provides in bringing the cloud computing functionalities closer to the end-nodes (ENs) at the edge of the network are lower network latency and the elimination of the possible bandwidth bottlenecks [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Optimizing the Number of Fog Nodes for Finite Fog Radio Access Networks under Multi-Slope Path Loss Model

et al. 2020

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Fog Radio Access Network (F-RAN) is a promising technology to address the bandwidth bottlenecks and network latency problems, by providing cloud-like services to the end nodes (ENs) at the edge of the network. The network latency can further be decreased by minimizing the transmission delay, which can be achieved by optimizing the number of Fog Nodes (FNs). In this context, we propose a stochastic geometry model to optimize the number of FNs in a finite F-RAN by exploiting the multi-slope path loss model (MS-PLM), which can more precisely characterize the path loss dependency on the propagation environment. The proposed approach shows that the optimum probability of being a FN is determined by the real root of a polynomial equation of a degree determined by the far-field path loss exponent (PLE) of the MS-PLM. The results analyze the impact of the path loss parameters and the number of deployed nodes on the optimum number of FNs. The results show that the optimum number of FNs is less than 7% of the total number of deployed nodes for all the considered scenarios. It also shows that optimizing the number of FNs achieves a significant reduction in the average transmission delay over the unoptimized scenarios.

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Architectural Resilience in Cloud, Fog and Edge Systems: A Survey

Cited by 52 publications

References 130 publications

Method to Increase Dependability in a Cloud-Fog-Edge Environment

Method to Increase Dependability in a Cloud-Fog-Edge Environment

A Survey on Trustworthiness for the Internet of Things

Optimizing the Number of Fog Nodes for Finite Fog Radio Access Networks under Multi-Slope Path Loss Model

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