@article {Filali20222174,
	title = {Dynamic SDN-Based Radio Access Network Slicing With Deep Reinforcement Learning for URLLC and eMBB Services},
	journal = {IEEE Transactions on Network Science and Engineering},
	volume = {9},
	number = {4},
	year = {2022},
	note = {cited By 3},
	pages = {2174-2187},
	abstract = {Radio access network (RAN) slicing is a key technology that enables 5G network to support heterogeneous requirements of generic services, namely ultra-reliable low-latency communication (URLLC) and enhanced mobile broadband (eMBB). In this paper, we propose a two time-scales RAN slicing mechanism to optimize the performance of URLLC and eMBB services. In a large time-scale, an SDN controller allocates radio resources to gNodeBs according to the requirements of the eMBB and URLLC services. In a short time-scale, each gNodeB allocates its available resources to its end-users and requests, if needed, additional resources from adjacent gNodeBs. We formulate this problem as a non-linear binary program and prove its NP-hardness. Next, for each time-scale, we model the problem as a Markov decision process (MDP), where the large-time scale is modeled as a single agent MDP whereas the shorter time-scale is modeled as a multi-agent MDP. We leverage the exponential-weight algorithm for exploration and exploitation (EXP3) to solve the single-agent MDP of the large time-scale MDP and the multi-agent deep Q-learning (DQL) algorithm to solve the multi-agent MDP of the short time-scale resource allocation. Extensive simulations show that our approach is efficient under different network parameters configuration and it outperforms recent benchmark solutions. {\textcopyright} 2013 IEEE.},
	keywords = {5G mobile communication systems, Deep learning, EMBB, Heuristic algorithms, Heuristics algorithm, Learning algorithms, Low-latency communication, Markov processes, Multi agent systems, Network slicing, Optimisations, Optimization, Quality of service, Quality-of-service, Radio access networks, Reinforcement learning, Resource allocation, Resource Management, Software agents, Software defined networking, Software-defined networkings, Time measurement, Ultra reliable low latency communication, URLLC},
	doi = {10.1109/TNSE.2022.3157274},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85126273319\&doi=10.1109\%2fTNSE.2022.3157274\&partnerID=40\&md5=44d6a41c3d7b8b6d8c4d6918856f36c5},
	author = {Filali, A. and Mlika, Z. and Cherkaoui, S. 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.}
}
@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.}
}
@article {Filali202015947,
	title = {Preemptive SDN Load Balancing with Machine Learning for Delay Sensitive Applications},
	journal = {IEEE Transactions on Vehicular Technology},
	volume = {69},
	number = {12},
	year = {2020},
	note = {cited By 12},
	pages = {15947-15963},
	abstract = {SDN is a key-enabler to achieve scalability in 5G and Multi-access Edge Computing networks. To balance the load between distributed SDN controllers, the migration of the data plane components has been proposed. Different from most previous works which use reactive mechanisms, we propose to preemptively balance the load in the SDN control plane to support network flows that require low latency communications. First, we forecast the load of SDN controllers to prevent load imbalances and schedule data plane migrations in advance. Second, we optimize the migration operations to achieve better load balancing under delay constraints. Specifically, in the first step, we construct two prediction models based on Auto Regressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM) approaches to forecast SDN controllers load. Then, we conduct a comparative study between these two models and calculate their accuracies and forecast errors. The results show that, in long-term predictions, the accuracy of LSTM model outperforms that of ARIMA by 55\% in terms of prediction errors. In the second step, to select which data plane components to migrate and where the migration should happen under delay constraints, we formulate the problem as a non-linear binary program, prove its NP-completeness and propose a reinforcement learning algorithm to solve it. The simulations show that the proposed algorithm performs close to optimal and outperforms recent benchmark algorithms from the literature. {\textcopyright} 1967-2012 IEEE.},
	keywords = {5G mobile communication systems, Auto-regressive integrated moving average, Autoregressive moving average model, Balancing, Comparative studies, Controllers, Delay constraints, Delay-sensitive applications, Forecasting, Learning algorithms, Long short-term memory, Long-term prediction, Low-latency communication, Prediction errors, Predictive analytics, Reactive mechanism, Reinforcement learning},
	doi = {10.1109/TVT.2020.3038918},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097173809\&doi=10.1109\%2fTVT.2020.3038918\&partnerID=40\&md5=4dc77968a74ba95fb61deec16c598fbb},
	author = {Filali, A. and Mlika, Z. and Cherkaoui, S. and Kobbane, A.}
}
@conference {Filali2018,
	title = {SDN Controller Assignment and Load Balancing with Minimum Quota of Processing Capacity},
	booktitle = {IEEE International Conference on Communications},
	volume = {2018-May},
	year = {2018},
	doi = {10.1109/ICC.2018.8422750},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85051428081\&doi=10.1109\%2fICC.2018.8422750\&partnerID=40\&md5=950211c49ab90c21d84d57b5ecf8c58f},
	author = {Filali, A. and Kobbane, A. and Elmachkour, M. and Cherkaoui, S.}
}
@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.}
}
