@article {Abouaomar2022,
	title = {Service Function Chaining in MEC: A Mean-Field Game and Reinforcement Learning Approach},
	journal = {IEEE Systems Journal},
	year = {2022},
	note = {cited By 0},
	pages = {1-12},
	abstract = {Multiaccess edge computing (MEC) and network virtualization technologies are important enablers for fifth generation networks to deliver diverse services. Services are often provided as fully connected virtual network functions (VNFs), through service function chaining (SFC). However, the problem of allocating SFC resources at the edge still faces many challenges related to the way VNFs are placed, chained, and scheduled. In this article, to solve these problems, we propose a game theory-based approach with the objective to reduce service latency in the context of SFC at the edge. The problem of allocating SFC resources can be divided into two subproblems: 1) the VNF placement and routing subproblem, and 2) the VNF scheduling subproblem. For the former subproblem, we formulate it as a mean-field game in which VNFs are contending over edge resources aiming at reducing the resource consumption of MEC nodes and reducing latency for users. We also propose a reinforcement learning-based technique, where the Ishikawa{\textendash}Mann learning algorithm is used. For the later subproblem, we formulate it as a matching game between VFNs and edge resources to find the execution order of the VNFs while reducing the latency. To efficiently solve it, we propose a modified version of the many-to-one deferred acceptance algorithm (DAA), called the enhanced multistep DAA. To illustrate the performance of the proposed approaches, we perform extensive simulations. The results show that the approaches achieve up to 40\% less resource consumption, and up to 38\% less latency than the benchmarked state-of-the-art methods. IEEE},
	keywords = {Computation theory, Computer games, Edge computing, Game, Game theory, Learning algorithms, Network function virtualization, Problem solving, Processor scheduling, Reinforcement learning, Resource allocation, Resource allocation/provisioning, Resource Management, Resources allocation, Routings, Scheduling, Service function chaining, Service functions, Transfer functions, Virtual reality, Virtualizations},
	doi = {10.1109/JSYST.2022.3171232},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85131721006\&doi=10.1109\%2fJSYST.2022.3171232\&partnerID=40\&md5=e7ecad4d011f9a66bf06f1e9f3f4d176},
	author = {Abouaomar, A. and Cherkaoui, S. and Mlika, Z. and Kobbane, A.}
}
@conference {Abouaomar2021273,
	title = {A deep reinforcement learning approach for service migration in MEC-enabled vehicular networks},
	booktitle = {Proceedings - Conference on Local Computer Networks, LCN},
	volume = {2021-October},
	year = {2021},
	note = {cited By 4},
	pages = {273-280},
	abstract = {Multi-access edge computing (MEC) is a key enabler to reduce the latency of vehicular network. Due to the vehicles mobility, their requested services (e.g., infotainment services) should frequently be migrated across different MEC servers to guarantee their stringent quality of service requirements. In this paper, we study the problem of service migration in a MEC-enabled vehicular network in order to minimize the total service latency and migration cost. This problem is formulated as a nonlinear integer program and is linearized to help obtaining the optimal solution using off-the-shelf solvers. Then, to obtain an efficient solution, it is modeled as a multi-agent Markov decision process and solved by leveraging deep Q learning (DQL) algorithm. The proposed DQL scheme performs a proactive services migration while ensuring their continuity under high mobility constraints. Finally, simulations results show that the proposed DQL scheme achieves close-to-optimal performance. {\textcopyright} 2021 IEEE.},
	keywords = {Deep learning, Edge computing, Infotainment, Integer programming, Learning schemes, Markov processes, Multi agent systems, Multi-access edge computing, Multiaccess, Q-learning, Quality of service, Reinforcement learning, Reinforcement learning approach, Service migration, Vehicle mobility, Vehicular networks},
	doi = {10.1109/LCN52139.2021.9524882},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85118466383\&doi=10.1109\%2fLCN52139.2021.9524882\&partnerID=40\&md5=8e35dfee04f347da3f757f57f5d575c8},
	author = {Abouaomar, A. and Mlika, Z. and Filali, A. and Cherkaoui, S. and Kobbane, A.}
}
@conference {Abouaomar2021,
	title = {Mean-Field Game and Reinforcement Learning MEC Resource Provisioning for SFC},
	booktitle = {2021 IEEE Global Communications Conference, GLOBECOM 2021 - Proceedings},
	year = {2021},
	note = {cited By 0},
	abstract = {In this paper, we address the resource provisioning problem for service function chaining (SFC) in terms of the placement and chaining of virtual network functions (VNFs) within a multi-access edge computing (MEC) infrastructure to reduce service delay. We consider the VNFs as the main entities of the system and propose a mean-field game (MFG) framework to model their behavior for their placement and chaining. Then, to achieve the optimal resource provisioning policy without considering the system control parameters, we reduce the proposed MFG to a Markov decision process (MDP). In this way, we leverage reinforcement learning with an actor-critic approach for MEC nodes to learn complex placement and chaining policies. Simulation results show that our proposed approach outperforms benchmark state-of-the-art approaches. {\textcopyright} 2021 IEEE.},
	keywords = {Computer games, Computing infrastructures, Computing resource, e-learning, Edge computing, Markov processes, Mean-field games, Multi-access edge computing, Multiaccess, Network function virtualization, Reinforcement learning, Reinforcement learnings, Resource provisioning, Service function chaining, Service functions, Transfer functions, Virtual addresses, Virtual reality},
	doi = {10.1109/GLOBECOM46510.2021.9685236},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85124062107\&doi=10.1109\%2fGLOBECOM46510.2021.9685236\&partnerID=40\&md5=8319d7756843c2ccadb8f9de4bec7eff},
	author = {Abouaomar, A. and Cherkaoui, S. and Mlika, Z. and Kobbane, A.}
}
@article {Filali2020197017,
	title = {Multi-access edge computing: A survey},
	journal = {IEEE Access},
	volume = {8},
	year = {2020},
	note = {cited By 49},
	pages = {197017-197046},
	abstract = {Multi-access Edge Computing (MEC) is a key solution that enables operators to open their networks to new services and IT ecosystems to leverage edge-cloud benefits in their networks and systems. Located in close proximity from the end users and connected devices, MEC provides extremely low latency and high bandwidth while always enabling applications to leverage cloud capabilities as necessary. In this article, we illustrate the integration of MEC into a current mobile networks{\textquoteright} architecture as well as the transition mechanisms to migrate into a standard 5G network architecture.We also discuss SDN, NFV, SFC and network slicing as MEC enablers. Then, we provide a state-of-the-art study on the different approaches that optimize the MEC resources and its QoS parameters. In this regard, we classify these approaches based on the optimized resources and QoS parameters (i.e., processing, storage, memory, bandwidth, energy and latency). Finally, we propose an architectural framework for a MEC-NFV environment based on the standard SDN architecture. {\textcopyright} 2020 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.},
	keywords = {5G mobile communication systems, Architectural frameworks, Bandwidth, Close proximity, Edge computing, Network function virtualization, Network slicing, Networks and systems, Optimized resources, QoS parameters, State of the art, Transition mechanism},
	doi = {10.1109/ACCESS.2020.3034136},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85099901543\&doi=10.1109\%2fACCESS.2020.3034136\&partnerID=40\&md5=bc99db081ebbbf5eb6d188c020bb33a2},
	author = {Filali, A. and Abouaomar, A. and Cherkaoui, S. and Kobbane, A. and Guizani, M.}
}
@conference {Ennya2019,
	title = {Computing Tasks Distribution in Fog Computing: Coalition Game Model},
	booktitle = {Proceedings - 2018 International Conference on Wireless Networks and Mobile Communications, WINCOM 2018},
	year = {2019},
	note = {cited By 0},
	doi = {10.1109/WINCOM.2018.8629587},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85062835283\&doi=10.1109\%2fWINCOM.2018.8629587\&partnerID=40\&md5=6f05637837203320cf2f7daa8f8c242d},
	author = {Ennya, Z. and Hadi, M.Y. and Abouaomar, A.}
}
@conference {Abouaomar2019,
	title = {Matching-Game for User-Fog Assignment},
	booktitle = {2018 IEEE Global Communications Conference, GLOBECOM 2018 - Proceedings},
	year = {2019},
	note = {cited By 0},
	doi = {10.1109/GLOCOM.2018.8647545},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85063543941\&doi=10.1109\%2fGLOCOM.2018.8647545\&partnerID=40\&md5=2e86072aa15d3175de06d25bff61c79f},
	author = {Abouaomar, A. and Kobbane, A. and Cherkaoui, S.}
}
@conference {Abouaomar201814,
	title = {Users-Fogs association within a cache context in 5G networks:Coalition game model},
	booktitle = {Proceedings - IEEE Symposium on Computers and Communications},
	volume = {2018-June},
	year = {2018},
	pages = {14-19},
	doi = {10.1109/ISCC.2018.8538500},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85059221795\&doi=10.1109\%2fISCC.2018.8538500\&partnerID=40\&md5=e324f3454d662cef58a9c78528c68a60},
	author = {Abouaomar, A. and Elmachkour, M. and Kobbane, A. and Tembine, H. and Ayaida, M.}
}
@conference {Abouaomar2017,
	title = {Caching, device-to-device and fog computing in 5th cellular networks generation: Survey},
	booktitle = {Proceedings - 2017 International Conference on Wireless Networks and Mobile Communications, WINCOM 2017},
	year = {2017},
	note = {cited By 0},
	abstract = {Many researches and standardization work on the challenges that 5th networks generation raised from the radio perspective while employing advanced techniques such as massive MIMO (Multiple-Input-Multiple-Output) and CoMP (Cooperative Multi-points Processes). However the backhaul problems such as bottlenecks has emerged due to the deployment of ultradense and heavy traffic that should be connected to the core networks. In this paper we investigate the caching as a promising solution to deal with the backhaul problems and the offload of the network. By caching the content near the users, at the base stations or at the device side via device-to-device communications or in advanced architecture of the cloud (In the Fog) is a promising solution to bring the interesting content closer to the users. Caching techniques are many, in this paper we grouped the most interesting ones with regard to different architectures, considering the cases and the quality of the solutions. {\textcopyright} 2017 IEEE.},
	doi = {10.1109/WINCOM.2017.8238174},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85041450036\&doi=10.1109\%2fWINCOM.2017.8238174\&partnerID=40\&md5=08c633efad0f6759f14c9373be0f0b07},
	author = {Abouaomar, A. and Filali, A. and Kobbane, A.}
}
