Priority, network and energy‐aware placement of IoT‐based application services in fog‐cloud environments

Hassan, Hiwa Omer; Azizi, Sadoon; Shojafar, Mohammad

doi:10.1049/iet-com.2020.0007

Cited by 63 publications

(25 citation statements)

References 28 publications

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“…The distributed FC architecture can reduce the latency at the end devices and improve the QoS. For example in [31], the authors proposed a heuristic algorithm called Minimum Response (MinRE) to solve the service placement problem based on service demand, e.g. delay, power consumption, and computational resources.…”

Section: State-of-the-artmentioning

confidence: 99%

ARTNet: Ai-Based Resource Allocation and Task Offloading in a Reconfigurable Internet of Vehicular Networks

Akbar

Jan

et al. 2022

IEEE Trans. Netw. Sci. Eng.

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The convergence of Software-Defined Networking (SDN) and Internet of Vehicular (IoV) integrated with Fog Computing (FC), known as Software Defined Vehicular based FC (SDV-F), has recently been established to take advantage of both paradigms and efficiently control the wireless networks. SDV-F tackles numerous problems, such as scalability, load-balancing, energy consumption, and security. It lags, however, in providing a promising approach to enable ultra-reliable and delay-sensitive applications with high vehicle mobility over SDV-F. We propose ARTNet, an AI-based Vehicle-to-Everything (V2X) framework for resource distribution and optimized communication using the SDV-F architecture. ARTNet offers ultra-reliable and low-latency communications, particularly in highly dynamic environments, which is still a challenge in IoV. ARTNet is composed of intelligent agents/controllers, to make decisions intelligently about (i) maximizing resource utilization at the fog layer, and (ii) minimizing the average end-to-end delay of time-critical IoV applications. Moreover, ARTNet is designed to assign a task to fog nodes based on their states. Our experimental results show that considering a dynamic IoV environment, ARTNet can efficiently distribute the fog layer tasks while minimizing the delay.

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Section: State-of-the-artmentioning

confidence: 99%

ARTNet: Ai-Based Resource Allocation and Task Offloading in a Reconfigurable Internet of Vehicular Networks

Akbar

Jan

et al. 2022

IEEE Trans. Netw. Sci. Eng.

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“…However, the issue of resource heterogeneity is not addressed in their work. Azizi et al [40] designed a priority-based service placement policy that prioritizes tasks with deadlines; thus, the nearest deadlines are scheduled first. Further, their work considers both computational and communication demands.…”

Section: Latency Sensitivitymentioning

confidence: 99%

A novel approach for IoT tasks offloading in edge-cloud environments

Almutairi

Aldossary

2021

J Cloud Comp

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Recently, the number of Internet of Things (IoT) devices connected to the Internet has increased dramatically as well as the data produced by these devices. This would require offloading IoT tasks to release heavy computation and storage to the resource-rich nodes such as Edge Computing and Cloud Computing. Although Edge Computing is a promising enabler for latency-sensitive related issues, its deployment produces new challenges. Besides, different service architectures and offloading strategies have a different impact on the service time performance of IoT applications. Therefore, this paper presents a novel approach for task offloading in an Edge-Cloud system in order to minimize the overall service time for latency-sensitive applications. This approach adopts fuzzy logic algorithms, considering application characteristics (e.g., CPU demand, network demand and delay sensitivity) as well as resource utilization and resource heterogeneity. A number of simulation experiments are conducted to evaluate the proposed approach with other related approaches, where it was found to improve the overall service time for latency-sensitive applications and utilize the edge-cloud resources effectively. Also, the results show that different offloading decisions within the Edge-Cloud system can lead to various service time due to the computational resources and communications types.

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“…Hassan et al [50] have recently proposed a method for service placement in fog-cloud systems. They classified services into two categories: critical and normal ones.…”

Section: A Energy Consumption and Costmentioning

confidence: 99%

DECA: A Dynamic Energy Cost and Carbon Emission-Efficient Application Placement Method for Edge Clouds

Ahvar

Mann

et al. 2021

IEEE Access

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As an increasing amount of data processing is done at the network edge, high energy costs and carbon emission of Edge Clouds (ECs) are becoming significant challenges. The placement of application components (e.g., in the form of containerized microservices) on ECs has an important effect on the energy consumption of ECs, impacting both energy costs and carbon emissions. Due to the geographic distribution of ECs, there is a variety of resources, energy prices and carbon emission rates to consider, which makes optimizing the placement of applications for cost and carbon efficiency even more challenging than in centralized clouds. This paper presents a Dynamic Energy cost and Carbon emissionefficient Application placement method (DECA) for ECs. DECA addresses both the initial placement of applications on ECs and the re-optimization of the placement using migrations. DECA considers geographically varying energy prices and carbon emission rates as well as optimizing the usage of both network and computing resources at the same time. By combining a prediction-based A* algorithm with a Fuzzy Sets technique, DECA makes intelligent decisions to optimize energy cost and carbon emissions. Simulation results show the ability of DECA in providing a trade off and optimizing energy cost and carbon emission at the same time.INDEX TERMS Edge cloud, Energy consumption, Energy costs, Green computing, Carbon emission, Application placement.

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Priority, network and energy‐aware placement of IoT‐based application services in fog‐cloud environments

Cited by 63 publications

References 28 publications

ARTNet: Ai-Based Resource Allocation and Task Offloading in a Reconfigurable Internet of Vehicular Networks

ARTNet: Ai-Based Resource Allocation and Task Offloading in a Reconfigurable Internet of Vehicular Networks

A novel approach for IoT tasks offloading in edge-cloud environments

DECA: A Dynamic Energy Cost and Carbon Emission-Efficient Application Placement Method for Edge Clouds

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