Supporting Location Transparent Services in a Mobile Edge Computing Environment

Gilly, Katja; Filiposka, Sonja; Mishev, Anastas

doi:10.4316/aece.2018.04002

Cited by 5 publications

(4 citation statements)

References 9 publications

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“…However, the introduced hierarchical set of communities helps localise the traffic as much as possible. For a more detailed per community efficiency analysis, please refer to the work of Gilly et al…”

Section: Implementation Of the Proposal And Simulation Resultsmentioning

confidence: 99%

Mobile‐aware dynamic resource management for edge computing

Filiposka

Mishev

Gilly

2019

Trans Emerging Tel Tech

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Shifting cloud computing capabilities close to the edge enables provisioning of low latency location‐based internet services that are adapted to user behaviour. However, this can be achieved neither with a simple move of the physical hosts closer to edge networks, nor continuing to abide by the same principles as the ones implemented in traditional cloud computing approaches. In order to accomplish the promised high quality of service, changes must be made to the resource management techniques so that they are adapted to the requirements of fog computing. This paper introduces a novel location‐based handoff management and its corresponding implementation of dynamic resource management modules that introduce resource allocation and migration strategies adapted specifically to fog computing. The aim is the implementation of the follow‐me behaviour of edge resources considering a tightly coupled perspective with the user location. The proposal is based on the concept of mapping physical areas to logical resource communities where the node mobility triggers migrations to the corresponding community. The results analysis from the simulation scenarios shows that the effectiveness of our community‐based dynamic resource management proposal in following the geographical trajectory of mobile users with wearable devices is over 80% even in highly saturated environments. The comparison with traditional resource management techniques clearly presents the advantages of our proposal, while the parameter wise in‐depth analysis discusses dependencies on the number of nodes, speed, and available resources.

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Section: Implementation Of the Proposal And Simulation Resultsmentioning

confidence: 99%

Mobile‐aware dynamic resource management for edge computing

Filiposka

Mishev

Gilly

2019

Trans Emerging Tel Tech

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“…However, the choice of the location of the MEC host where the MEC service is provided is important when aiming to reduce latency to the vehicle's connected device to a minimum. We have adapted Filiposka et al two level hierarchical framework [18], that was originally designed for cloud computing infrastructures to edge computing solutions, implementing a mobile-aware dynamic resource management [19] that also includes a minimum latency ''follow me'' proposal based on applications/services fast migration [20] as a mobile device moves around the coverage area. Considering a vehicular network, we continue this work while going some steps further in the definition of the MEC architecture and detailing the structure, operation and interaction of the necessary MEC modules/functionalities in order to provide dynamic and low latency MEC vehicular services.…”

Section: Low Latency Offloading Proposalmentioning

confidence: 99%

Offloading Edge Vehicular Services in Realistic Urban Environments

et al. 2020

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The imminent deployment of 5G and the rapid development of multi-access edge computing standards are demanding advances in terms of vehicular low latency offloading design and modelling proposals. In this paper we describe the functionalities of a high-level multi-access edge computing orchestrator that arranges location based vehicular edge services by the means of hierarchical dynamic resource management. In this way low latency responses can be guaranteed due to the geo-aware and energy efficient service allocation and dynamic migration. The first steps towards the definition of a vehicle to infrastructure communication specification are also provided. We study the efficiency of our proposal applied to an infotainment case study deployed in the city centre of Alicante, Spain. The simulation results obtained show that latencies perceived by vehicles generally range from optimal to first order sub-optimal scales all over the coverage area and that the presented offloading solution energetically scales with the number of hosts at the edge. INDEX TERMS Energy efficiency, location based services, migration, multi-access edge computing, orchestration, vehicular low latency offloading.

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“…In the cases when there is no possibility to migrate the service due to the lack of resources in the destination sub-tree leaf, the migration is aborted and the service runs in sub-optimal conditions. For a more detailed description of the dynamic resource management system implementation in CloudSim, please refer to [9].…”

Section: Dynamic Resource Management For Mec Servicesmentioning

confidence: 99%

Dynamic Resource Management of Real-Time Edge Services for Intelligent Vehicular Networks: A Case Study

Gilly¹,

Filiposka²,

Alcaraz³

et al. 2019

ELEKTRON ELEKTROTECH

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The Internet of Vehicles requires high bandwidth and low latency services to unleash the potential of fully connected vehicles. Thus, the offloading proposals that successfully manage massive real-time service requests from vehicle nodes are needed. In this paper, we analyse the dynamic resource management for intelligent vehicular networks based on Multi-access Edge Computing architecture services. Using a combination of CloudSim and SUMO simulators, we present a case study of infotainment services in the city centre of Alicante, in Spain, that shows a high degree of optimality both in service allocation and migration when considering dense urban environments.

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Supporting Location Transparent Services in a Mobile Edge Computing Environment

Cited by 5 publications

References 9 publications

Mobile‐aware dynamic resource management for edge computing

Mobile‐aware dynamic resource management for edge computing

Offloading Edge Vehicular Services in Realistic Urban Environments

Dynamic Resource Management of Real-Time Edge Services for Intelligent Vehicular Networks: A Case Study

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