@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.}
}
@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.}
}
@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 {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 {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 {Amine20171245,
	title = {A many-to-many matching game in ultra-dense LTE HetNets},
	booktitle = {2017 13th International Wireless Communications and Mobile Computing Conference, IWCMC 2017},
	year = {2017},
	note = {cited By 0},
	pages = {1245-1250},
	abstract = {In this work, we focus our study to improving the energy efficiency of mobile cellular users in ultra-dense LTE HetNets. The hyper-dense co-channel deployment of indoor LTE small cell networks (SCNs) within LTE macro cell networks (MCNs) will aggravate the effect of cross-tier interferences caused by the uplink transmissions of macro-indoor users located inside the overlapping zones of small base station (SBS) coverage areas. Hence, degrading the uplink performance at the level of SBSs adopting closed access policy. In order to eliminate the severe cross-tier interferences, each SBS attempts to open the access for macro-indoor users that accept only the SBS with Max-SINR offer. This will lead to network congestion problems in several SCNs. Wherefore, we formulate our problem as a many-to-many matching game. Then, we introduce an algorithm that computes the optimal many-to-many stable matching which consist of assigning each macro-indoor user with multi-homing capabilities to the most suitable set of SBSs and vice versa based on their preference profiles. With regard to the conventional Max-SINR association scheme, our solution can effectively improve the energy efficiency of cellular users. Moreover, it can ensure load balancing in ultra-dense LTE HetNets. {\textcopyright} 2017 IEEE.},
	doi = {10.1109/IWCMC.2017.7986463},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85027886532\&doi=10.1109\%2fIWCMC.2017.7986463\&partnerID=40\&md5=a4a95449b5ea20b54454dcf7b7bd1e59},
	author = {Amine, M. and Walid, A. and Kobbane, A. and Cherkaoui, S.}
}
