@article {43337,
	title = {Opportunistic CPU sharing in Mobile Edge Computing deploying the Cloud-RAN},
	journal = {IEEE Transactions on Network and Service Management},
	year = {2023},
	publisher = { IEEE},
	keywords = {Cloud-RAN, Containers, Mobile edge computing, resource management},
	doi = {10.1109/TNSM.2023.3304067},
	author = {Andres Felipe Oca {Palacio} and Mah Rukh {Fida} and Juan Felipe Bot {Vega} and Elmokashfi, Ahmed and Bryhni, Haakon}
}
@inproceedings { M2EC2020,
	title = {Flexible 4G/5G Testbed Setup for Mobile Edge Computing using OpenAirInterface and Open Source MANO},
	journal = {Proceedings of the 2nd International Workshop on Recent Advances for Multi-Clouds and Mobile Edge Computing~(M2EC) in conjunction with the 34th International Conference on Advanced Information Networking and Applications~(AINA)},
	year = {2020},
	publisher = { Springer},
	address = {Caserta, Campania/Italy},
	abstract = {Setting up a working 4G/5G mobile network development testbed can be a highly complicated and error-prone task. In this paper, we therefore introduce our open source Virtual Network Function (VNF) for an OpenAirInterface-based Evolved Packet Core (EPC) for deployment with the Open Source Management and Orchestration (Open Source MANO, OSM) framework. By using our VNF as basis, it will be easily possible to create own testbeds and extend them with further functionality, particularly {\textendash} but not limited to {\textendash} Mobile Edge Computing (MEC) setups. In a simple proof of concept, we demonstrate a basic transport protocol performance evaluation in a deployed test network.},
	keywords = {5G, Evolved Packet Core, Mobile edge computing, Network Function Virtualisation, Open Source MANO, OpenAirInterface},
	author = {Dreibholz, Thomas}
}
@inproceedings { PDP2019,
	title = {Mobile Edge as Part of the Multi-Cloud Ecosystem: A Performance Study},
	journal = {Proceedings of the 27th Euromicro International Conference on Parallel, Distributed and Network-Based Processing~(PDP)},
	year = {2019},
	month = {02/2019},
	pages = {59-66},
	publisher = {IEEE Computer Society},
	address = {Pavia, Lombardia/Italy},
	abstract = {Cloud computing has revolutionized the way of application usage and deployment: applications run cost-effectively in remote data centers. With the increasing need for mobility and micro-services, particularly with the upcoming 5G mobile broadband networks, there is also a strong demand for mobile edge computing (MEC): applications run in small cloud systems in close proximity to the user, in order to minimize latencies. Both cloud and MEC have their advantages and disadvantages. Combining the two approaches in a unified multi-cloud, consisting of both traditional cloud services provisioned over heterogeneous cloud platforms and MEC systems, has the potential of obtaining the best out of both worlds. However, a comprehensive study is needed to evaluate the performance gains and the overheads involved for 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 round-trip 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 solutions, in terms of an extension proposed to our existing Melodic middleware platform for multi-cloud applications.},
	keywords = {Cloud computing, latency, Mobile edge computing, Multi-Cloud, Performance},
	isbn = {978-1-7281-1644-0},
	doi = {10.1109/PDP.2019.00017},
	author = {Dreibholz, Thomas and Mazumdar, Somnath and Zahid, Feroz and Taherkordi, Amirhosein and Ernst Gunnar {Gran}}
}
@article {25570,
	title = {Contract-theoretic Approach for Delay Constrained Offloading in Vehicular Edge Computing Networks},
	journal = {Mobile Networks and Applications},
	year = {2018},
	pages = {1-12},
	publisher = {Springer},
	address = {US},
	abstract = {Mobile Edge Computing (MEC) is a promising solution to improve vehicular services through offloading computation to cloud servers in close proximity to mobile vehicles. However, the self-interested nature together with the high mobility characteristic of the vehicles make the design of the computation offloading scheme a significant challenge. In this paper, we propose a new Vehicular Edge Computing (VEC) framework to model the computation offloading process of the mobile vehicles running on a bidirectional road. Based on this framework, we adopt a contract theoretic approach to design optimal offloading strategies for the VEC service provider, which maximize the revenue of the provider while enhancing the utilities of the vehicles. To further improve the utilization of the computing resources of the VEC servers, we incorporate task priority distinction as well as additional resource providing into the design of the offloading scheme, and propose an efficient VEC server selection and computing resource allocation algorithm. Numerical results indicate that our proposed schemes greatly enhance the revenue of the VEC provider, and concurrently improve the utilization of cloud computing resources.},
	keywords = {Cloud, Contract theory, Mobile edge computing, Vehicular network},
	issn = {1572-8153},
	doi = {10.1007/s11036-018-1032-0},
	author = {Zhang, K. and Mao, Y. and Leng, S. and Maharjan, Sabita and Vinel, A. and Zhang, Yan}
}
@article {25578,
	title = {Mobile Edge Computing: A Survey},
	journal = {IEEE Internet of Things Journal},
	volume = {5},
	year = {2017},
	month = {02/2018},
	publisher = {ACM IEEE},
	abstract = {Mobile Edge Computing (MEC) is an emergent\&nbsp;architecture where cloud computing services are extended to the\&nbsp;edge of networks leveraging mobile base stations. As a promising\&nbsp;edge technology, it can be applied to mobile, wireless and wireline\&nbsp;scenarios, using software and hardware platforms, located at\&nbsp;the network edge in the vicinity of end-users. MEC provides\&nbsp;seamless integration of multiple application service providers\&nbsp;and vendors towards mobile subscribers, enterprises and other\&nbsp;vertical segments. It is an important component in the 5G\&nbsp;architecture which supports variety of innovative applications\&nbsp;and services where ultra low latency is required. This paper is\&nbsp;aimed to present a comprehensive survey of relevant research and\&nbsp;technological developments in the area of MEC. It provides the\&nbsp;definition of MEC, its advantages, architectures, and application\&nbsp;areas; where we in particular highlight related research and\&nbsp;future directions. Finally, security and privacy issues and related\&nbsp;existing solutions are also discussed.},
	keywords = {Fog Computing, IoT, Mobile cloud computing, Mobile edge computing},
	doi = {10.1109/JIOT.2017.2750180},
	author = {Abbas, Nasir and Zhang, Yan and Taherkordi, Amir and Skeie, Tor}
}
@article {25639,
	title = {Selective Offloading in Mobile Edge Computing for Green Internet of Things},
	journal = {IEEE Network Magazine},
	volume = {32},
	year = {2017},
	pages = {54-60},
	publisher = {IEEE},
	abstract = {Mobile Edge Computing (MEC) provides the radio access networks with cloud computing capabilities to fulfill the requirements of the Internet of Things (IoT) services such as high reliability and low latency. Offloading services to edge servers can alleviate the storage and computing limitations and prolong the lifetimes of the IoT devices. However, offloading in MEC faces scalability problems due to the massive number of IoT devices. In this article, we present a new integration architecture of the cloud, MEC and IoT, and propose a lightweight request and admission framework to resolve the scalability problem. Without coordination among devices, the proposed framework can be operated at the IoT devices and computing servers separately, by encapsulating latency requirements in offloading requests. Then, a selective offloading scheme is designed to minimize the energy consumption of devices, where the signalling overhead can be further reduced by enabling the devices to be selfnominated or self-denied for offloading. Simulation results show that our proposed selective offloading scheme can satisfy the latency requirements of different services and reduce the energy consumption of the IoT devices.},
	keywords = {energy efficiency, Internet of things, Mobile edge computing, scalability, Selective offloading},
	doi = {10.1109/MNET.2018.1700101},
	author = {Lyu, X. and Tian, H. and Jiang, L. and Vinel, A. and Maharjan, Sabita and Gjessing, Stein and Zhang, Yan}
}
