A Middleware for Mobile Edge Computing

Carrega, Alessandro; Repetto, Matteo; Gouvas, Panagiotis; Zafeiropoulos, Anastasios

doi:10.1109/mcc.2017.3791021

Cited by 28 publications

(12 citation statements)

References 10 publications

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“…More recently, there have been a number of other studies on messaging middleware for edge computing applications. The middleware investigated in [299] optimized diverse user QoS requirements and orchestrated connections between users and brokers, [300] leveraged SDN to monitor network conditions for resilient data exchange of mission-critical applications, and the messaging middleware proposed in [301] enabled the development and deployment of emerging applications in distributed and heterogeneous edge computing systems.…”

Section: ) Middleware For Edge Computingmentioning

confidence: 99%

A Survey of Multi-Access Edge Computing in 5G and Beyond: Fundamentals, Technology Integration, and State-of-the-Art

et al. 2020

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Driven by the emergence of new compute-intensive applications and the vision of the Internet of Things (IoT), it is foreseen that the emerging 5G network will face an unprecedented increase in traffic volume and computation demands. However, end users mostly have limited storage capacities and finite processing capabilities, thus how to run compute-intensive applications on resource-constrained users has recently become a natural concern. Mobile edge computing (MEC), a key technology in the emerging fifth generation (5G) network, can optimize mobile resources by hosting compute-intensive applications, process large data before sending to the cloud, provide the cloud-computing capabilities within the radio access network (RAN) in close proximity to mobile users, and offer context-aware services with the help of RAN information. Therefore, MEC enables a wide variety of applications, where the real-time response is strictly required, e.g., driverless vehicles, augmented reality, robotics, and immerse media. Indeed, the paradigm shift from 4G to 5G could become a reality with the advent of new technological concepts. The successful realization of MEC in the 5G network is still in its infancy and demands for constant efforts from both academic and industry communities. In this survey, we first provide a holistic overview of MEC technology and its potential use cases and applications. Then, we outline up-to-date researches on the integration of MEC with the new technologies that will be deployed in 5G and beyond. We also summarize testbeds and experimental evaluations, and open source activities, for edge computing. We further summarize lessons learned from state-of-the-art research works as well as discuss challenges and potential future directions for MEC research.

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Section: ) Middleware For Edge Computingmentioning

confidence: 99%

A Survey of Multi-Access Edge Computing in 5G and Beyond: Fundamentals, Technology Integration, and State-of-the-Art

et al. 2020

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“…It is also responsible for scheduling the offloading tasks in order to satisfy applications' users and Edge-Cloud System requirements. The EC is synchronizing its data with the centralized Cloud because if there is any failure, other edge nodes can take EC responsibility from the cloud [55,56].…”

Section: Edge Controllermentioning

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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Section: Edge Controllermentioning

confidence: 99%

Investigating and Modelling of Task Offloading Latency in Edge-Cloud Environment

Almutairi¹,

Aldossary²

2021

Computers, Materials &Amp; Continua

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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. However, different service architecture and offloading strategies have a different impact on the service time performance of IoT applications. Therefore, this paper presents an Edge-Cloud system architecture that supports scheduling offloading tasks of IoT applications in order to minimize the enormous amount of transmitting data in the network. Also, it introduces the offloading latency models to investigate the delay of different offloading scenarios/schemes and explores the effect of computational and communication demand on each one. A series of experiments conducted on an EdgeCloudSim show that different offloading decisions within the Edge-Cloud system can lead to various service times due to the computational resources and communications types. Finally, this paper presents a comprehensive review of the current state-of-the-art research on task offloading issues in the Edge-Cloud environment.

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A Middleware for Mobile Edge Computing

Cited by 28 publications

References 10 publications

A Survey of Multi-Access Edge Computing in 5G and Beyond: Fundamentals, Technology Integration, and State-of-the-Art

A Survey of Multi-Access Edge Computing in 5G and Beyond: Fundamentals, Technology Integration, and State-of-the-Art

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

Investigating and Modelling of Task Offloading Latency in Edge-Cloud Environment

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