A fog‐based fault‐tolerant and QoE‐aware service composition in smart cities

Bidi, Arshia Hosseini; Movahedi, Zahra; Movahedi, Zeinab

doi:10.1002/ett.4326

Cited by 12 publications

(13 citation statements)

References 32 publications

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“…The authors in [ 22 ] have proposed a fault-tolerant architecture for service composition in a smart cities environment adopting fog computing technology. The proposed method is able to handle service composition problem via RESTful IoT technology.…”

Section: Related Workmentioning

confidence: 99%

Dynamic QoS/QoE-aware reliable service composition framework for edge intelligence

2022

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Edge intelligence has become popular recently since it brings smartness and copes with some shortcomings of conventional technologies such as cloud computing, Internet of Things (IoT), and centralized AI adoptions. However, although utilizing edge intelligence contributes to providing smart systems such as automated driving systems, smart cities, and connected healthcare systems, it is not free from limitations. There exist various challenges in integrating AI and edge computing, one of which is addressed in this paper. Our main focus is to handle the adoption of AI methods on resource-constrained edge devices. In this regard, we introduce the concept of Edge devices as a Service (EdaaS) and propose a quality of service (QoS) and quality of experience (QoE)-aware dynamic and reliable framework for AI subtasks composition. The proposed framework is evaluated utilizing three well-known meta-heuristics in terms of various metrics for a connected healthcare application scenario. The experimental results confirm the applicability of the proposed framework. Moreover, the results reveal that black widow optimization (BWO) can handle the issue more efficiently compared to particle swarm optimization (PSO) and simulated annealing (SA). The overall efficiency of BWO over PSO is 95%, and BWO outperforms SA with 100% efficiency. It means that BWO prevails SA and PSO in all and 95% of the experiments, respectively.

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Section: Related Workmentioning

confidence: 99%

Dynamic QoS/QoE-aware reliable service composition framework for edge intelligence

2022

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“…Parameters that an optimal placement strategy must keep in mind are latency, reducing the energy consumption, minimize the cost and response time. 20,21 There are many existing works related to service placement in cloud/fog computing. Rahman et al 7 sum up and outline the architecture of the fog computing model, functionality, related paradigms, security challenges, and various real-time applications such as smart grid, traffic control and increased reality.…”

Section: Related Workmentioning

confidence: 99%

“…Recent research works have revealed the significance and pertinence of fog computing. 20 However, in terms of implementation view, most of the works have not bestowed up on the technical aspect of the paradigm.…”

Section: Related Workmentioning

confidence: 99%

DEEDSP: Deadline‐aware and energy‐efficient dynamic service placement in integrated Internet of Things and fog computing environments

Raghavendra

Chawla

Gill

2021

Trans Emerging Tel Tech

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Fog computing has become adaptable and also as a promising infrastructure for providing elastic resources at the edge of the network. Fog computing reduces the transmission latency and consumption of bandwidth while processing the incoming requests from various Internet of Things (IoT) devices.Moreover, fog computing can support and facilitate geographically distributed applications with low and predictable latency. However, this technology also has significant research issues in its current stage such as successful implementation of service location models. In this article, we propose a deadline-aware and energy-efficient dynamic service placement (DEEDSP) technique for fog computing that supports the placement of IoT based services. Further, hyper-heuristic algorithm based energy-efficient service placement technique is proposed to balance the energy-delay trade-off based on different service placement decision criteria (eg, minimum response time or energy consumption).The proposed algorithm is able to dynamically minimize the energy consumption of the system while ensuring that the response time satisfies a given time constraint. Finally, the proposed technique is evaluated in simulated fog computing environment and experimental results show that this technique performs better than state-of-the-art placement techniques in terms of energy and latency.

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“…Over recent years, the Internet of Things (IoT) has been integrated in our daily lives, which make the use of IoT applications (access control or face recognition for instance) in the context of smart city [1,2], smart healthcare [3], smart home [4], etc., more and more popular. Fog computing paradigm, as an extension to cloud computing, plays a crucial role in executing the IoT applications.…”

Section: Introductionmentioning

confidence: 99%

“…The task scheduling process is performed in the fog layer closed to the end-user. 2 We use a classical formulation of task scheduling as an Integer Linear Programming (ILP) multi-objective optimization problem considering the selection of appropriate fog nodes under certain constraints with the aim of minimizing both task completion time and energy consumption as two QoS parameters in a fog computing system. To solve this optimization problem, we design a new approach called OppoCWOA, which is a combination of WOA, Opposition-based learning, and Chaos theory.…”

Section: Introductionmentioning

confidence: 99%

An efficient population-based multi-objective task scheduling approach in fog computing systems

2021

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With the rapid development of Internet of Things (IoT) technologies, fog computing has emerged as an extension to the cloud computing that relies on fog nodes with distributed resources at the edge of network. Fog nodes offer computing and storage resources opportunities to resource-less IoT devices which are not capable to support IoT applications with computation-intensive requirements. Furthermore, the closeness of fog nodes to IoT devices satisfies the low-latency requirements of IoT applications. However, due to the high IoT task offloading requests and fog resource limitations, providing an optimal task scheduling solution that considers a number of quality metrics is essential. In this paper, we address the task scheduling problem with the aim of optimizing the time and energy consumption as two QoS parameters in the fog context. First, we present a fog-based architecture for handling the task scheduling requests to provide the optimal solutions. Second, we formulate the task scheduling problem as an Integer Linear Programming (ILP) optimization model considering both time and fog energy consumption. Finally, we propose an advanced approach called Opposition-based Chaotic Whale Optimization Algorithm (OppoCWOA) to enhance the performance of the original WOA for solving the modelled task scheduling problem in a timely manner. The efficiency of the proposed OppoCWOA is shown by providing extensive simulations and comparisons with the original WOA and some existing meta-heuristic algorithms such as Artificial Bee Colony (ABC), Particle Swarm Optimization (PSO), and Genetic Algorithm (GA).

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A fog‐based fault‐tolerant and QoE‐aware service composition in smart cities

Cited by 12 publications

References 32 publications

Dynamic QoS/QoE-aware reliable service composition framework for edge intelligence

Dynamic QoS/QoE-aware reliable service composition framework for edge intelligence

DEEDSP: Deadline‐aware and energy‐efficient dynamic service placement in integrated Internet of Things and fog computing environments

An efficient population-based multi-objective task scheduling approach in fog computing systems

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