Dependability in edge computing

Bagchi, Saurabh; Siddiqui, Muhammad-Bilal; Wood, Paul; Zhang, Heng

doi:10.1145/3362068

Cited by 48 publications

(25 citation statements)

References 6 publications

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“…Regarding studies that present definitions on dependability and trustworthiness, some papers explore the state of the art and definitions [9], [42], [43], [56][57][58][59][60][61][62][63]. We also found surveys on trustworthiness [64][65][66][67][68] and dependability [69] and a systematic literature review (SLR) on dependability [70].…”

Section: ) Conceptualmentioning

confidence: 99%

“…Some IoT applications are latency-sensitive and, consequently, require low latency for transmitting packets between IoT stages. Therefore, these systems are always available when it performs all actions in real-time [60].…”

Section: ) System Availabilitymentioning

confidence: 99%

“…The scalability feature implies providing more computing resources to the IoT system [63]. In a fog-based IoT system, nodes can become computationally saturated because, in a real scenario, the fog is responsible for dealing with thousands of devices [60]. Therefore, the scalable deployment of fog computing systems requires the necessary hardware infrastructure.…”

Section: ) System Availabilitymentioning

confidence: 99%

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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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Section: ) Conceptualmentioning

confidence: 99%

Section: ) System Availabilitymentioning

confidence: 99%

Section: ) System Availabilitymentioning

confidence: 99%

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A Survey on Trustworthiness for the Internet of Things

Ribeiro

Kamienski

2021

IEEE Access

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“…However, due to the constraints of the base hardware and their physical size, the computing resources of smart devices are generally limited [ 3 ] and cannot meet the requirements of these applications. Therefore, the technology of edge cloud computing (ECC) [ 4 , 5 , 6 ] is considered an effective and promising way to handle the challenges between the smart devices with limited resources and the mobile application with high demand. Different from the traditional cloud computing [ 7 , 8 ], ECC is more suitable for dealing with the sensitive tasks with low latency.…”

Section: Introductionmentioning

confidence: 99%

Latency-Optimal Computational Offloading Strategy for Sensitive Tasks in Smart Homes

Wang

Zheng

et al. 2021

Sensors

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In smart homes, the computational offloading technology of edge cloud computing (ECC) can effectively deal with the large amount of computation generated by smart devices. In this paper, we propose a computational offloading strategy for minimizing delay based on the back-pressure algorithm (BMDCO) to get the offloading decision and the number of tasks that can be offloaded. Specifically, we first construct a system with multiple local smart device task queues and multiple edge processor task queues. Then, we formulate an offloading strategy to minimize the queue length of tasks in each time slot by minimizing the Lyapunov drift optimization problem, so as to realize the stability of queues and improve the offloading performance. In addition, we give a theoretical analysis on the stability of the BMDCO algorithm by deducing the upper bound of all queues in this system. The simulation results show the stability of the proposed algorithm, and demonstrate that the BMDCO algorithm is superior to other alternatives. Compared with other algorithms, this algorithm can effectively reduce the computation delay.

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“…But taking advantage of EC is not an easy issue [6]. Firstly, IoT applications developers must deal with the decomposition of application functionality into a set of tasks and find out which of them could be allocated inside the Edge nodes [7] [8] in an optimal way, and this is a challenging process.…”

Section: Introductionmentioning

confidence: 99%

Energy-Efficient Deployment of IoT Applications in Edge-Based Infrastructures: A Software Product Line Approach

Cañete

Amor

Fuentes

2021

IEEE Internet Things J.

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In order to lower latency and reduce energy consumption, Edge Computing proposes offloading some computation intensive tasks usually performed in the Cloud onto nearby devices in the frontier/Edge of the access networks. However, current task offloading approaches are often quite simple. They neither consider the high diversity of hardware and software technologies present in edge network devices, nor take into account that some tasks may require some specific software and hardware infrastructure to be executed. This paper proposes a task offloading process that leans on Software Product Line technologies, which are a very good option to model the variability of software and hardware present in edge environments. Firstly, our approach automates the separation of application tasks, considering the data and operation needs and restrictions among them, and identifying the hardware and software resources required by each task. Secondly, our approach models and manages separately the infrastructure available for task offloading, as a set of nodes that provide certain hardware and software resources. This separation allows to reason about alternative offloading of tasks with different hardware and software resource requirements, in heterogeneous nodes and minimizing energy consumption. In addition, the offloading process considers alternative implementations of tasks to choose the one that best fits the hardware and software characteristics of available edge network infrastructure. The experimental results shows that our approach reduces the energy consumption in the user node by approximately 41%-62%, and the energy consumption of the devices involved in a task offloading solution by 34%-48%.

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Dependability in edge computing

Abstract: Edge computing holds great promise, and almost as many challenges in deployment.

Cited by 48 publications

References 6 publications

A Survey on Trustworthiness for the Internet of Things

A Survey on Trustworthiness for the Internet of Things

Latency-Optimal Computational Offloading Strategy for Sensitive Tasks in Smart Homes

Energy-Efficient Deployment of IoT Applications in Edge-Based Infrastructures: A Software Product Line Approach

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