Hybrid Mobile Edge Computing: Unleashing the Full Potential of Edge Computing in Mobile Device Use Cases

Reiter, Andreas; Prünster, Bernd; Zefferer, Thomas

doi:10.1109/ccgrid.2017.125

Cited by 38 publications

(11 citation statements)

References 10 publications

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“…(2) Ultra Reliable & Low Latency Communication (URLLC), which mainly serves users with high reliability and delay requirements, is mainly used in networking of vehicles, UAV, industrial control and other scenarios [10]. (3) Mass Machine Type Communication (MMTC), aiming at providing high endurance and high reliability services, relying on the high connectivity of 5G technology, promotes the integration of smart home, smart city and other industries, and truly realizes the concept of "interconnection of all things" [11].…”

Section: Application Of 5g Technologymentioning

confidence: 99%

5G Network Data Migration Service Based on Edge Computing

2021

Symmetry

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With the development of mobile network technology, the continuous increase of mobile traffic has put forward higher requirements for quality of service (QoS) issues such as asymmetric transmission delay. The paper mainly studies the energy distribution problem on the migration data link from the terminal device to the edge node in the mobile edge network. Multiple data service packages are set up at each hop on the migration data link, and these data service packages compete with each other, and ultimately only one terminal provides and stores energy for this hop. The migration strategy of the data service package is affected by the edge node, and the edge node changes the migration strategy according to the migration strategy of the data service package. The paper is based on the formation of nodes between the data service packages of different nodes on the 5G network data link to jointly control the migration strategy, coordinate the migration strategy formulated, and better coordinate the migration strategy. In this competitive game model, the optimal migration strategy of nodes is found out according to the terminal equipment access requirements. Then according to the node stability rules, the composition of nodes when the nodes are stable is obtained, the migration strategy of stable nodes and the migration and spectrum strategies of operators are obtained, and the migration strategy of joint control provides energy for edge nodes.

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Section: Application Of 5g Technologymentioning

confidence: 99%

5G Network Data Migration Service Based on Edge Computing

2021

Symmetry

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“…Shekhar et al [23] proposed a dynamic resource management scheme for mobile edge clouds. Reiter et al [24] proposed a hybrid edge computing platform to unleash the full potential of MEC platform for IoT applications. Chang et al [25] proposed energy-efficient optimization framework for computation offloading in fog computing system.…”

Section: Related Workmentioning

confidence: 99%

Adaptive Service Offloading for Revenue Maximization in Mobile Edge Computing With Delay-Constraint

Samanta

Chang

2019

IEEE Internet Things J.

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“…Another category of existing solutions has focused on enabling a hybrid edge computing model to improve the efficiency of mobile applications. Hybrid Mobile Edge Computing (HMEC) [14] makes use of edge-level computing units, in order to fulfill the needs of interactive mobile applications. It enables an interactive and flexible usage of proximate and distant computing resources through the HMEC framework.…”

Section: Related Workmentioning

confidence: 99%

Mobile Edge as Part of the Multi-Cloud Ecosystem: A Performance Study

Dreibholz

Mazumdar

Zahid

et al. 2019

2019 27th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP)

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Cloud computing has revolutionised the development and deployment of applications by running them cost-effectively in remote data centres. With the increasing need for mobility and micro-services, particularly with the emerging 5G mobile broadband networks, there is also a strong demand for mobile edge computing (MEC). It enables applications to run in small cloud systems in close proximity to the user in order to minimise latencies. Both cloud computing and MEC have their own advantages and disadvantages. Combining these two computing paradigms in a unified multi-cloud platform has the potential of obtaining the best of both worlds. However, a comprehensive study is needed to evaluate the performance gains and the overheads imposed by this combination to real-world cloud applications. In this paper, we introduce a baseline performance evaluation in order to identify the fallacies and pitfalls of combining multiple cloud systems and MEC into a unified MEC-multi-cloud platform. For this purpose, we analyze the basic, application-independent performance metrics of average roundtrip time (RTT) and average application payload throughput in a setup consisting of two private and one public cloud systems. This baseline performance analysis confirms the feasibility of MEC-multi-cloud and provides guidelines for designing an autonomic resource provisioning solution in terms of an extension proposed to our existing MELODIC middleware platform for multi-cloud applications.

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Hybrid Mobile Edge Computing: Unleashing the Full Potential of Edge Computing in Mobile Device Use Cases

Cited by 38 publications

References 10 publications

5G Network Data Migration Service Based on Edge Computing

5G Network Data Migration Service Based on Edge Computing

Adaptive Service Offloading for Revenue Maximization in Mobile Edge Computing With Delay-Constraint

Mobile Edge as Part of the Multi-Cloud Ecosystem: A Performance Study

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