@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 {Khoufi2017,
	title = {Path planning of mobile sinks in charge of data gathering: A coalitional game theory approach},
	booktitle = {2016 IEEE 35th International Performance Computing and Communications Conference, IPCCC 2016},
	year = {2017},
	note = {cited By 0},
	abstract = {Game theory is often used to find equilibria where no player can unilaterally increase its own payoff by changing its strategy without changing the strategies of other players. In this paper, we propose to use coalition formation to compute the optimized tours of mobile sinks in charge of collecting data from static wireless sensor nodes. Mobile sinks constitute a very attractive solution for wireless sensor networks, WSNs, where the application requirements in terms of node autonomy are very strong unlike the requirement in terms of latency. Mobile sinks allow wireless sensor nodes to save energy The associated coalition formation problem has a stable solution given by the final partition obtained. However, the order in which the players play has a major impact on the final result. We determine the best order to minimize the number of mobile sinks needed. We evaluate the complexity of this coalitional game as well as the impact of the number of collect points per surface unit on the number of mobile sinks needed and on the maximum tour duration of these mobile sinks. In addition, we show how to extend the coalitional game to support different latencies for different types of data. Finally, we formalize our problem as an optimization problem and we perform a comparative evaluation. {\textcopyright} 2016 IEEE.},
	doi = {10.1109/PCCC.2016.7820637},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85013413857\&doi=10.1109\%2fPCCC.2016.7820637\&partnerID=40\&md5=3c4a3fe9d6f76ca57b380ffcb337856d},
	author = {Khoufi, I. and Minet, P. and Koulali, M.-A. and Kobbane, A.}
}
@conference { ISI:000349673802062,
	title = {Packet dropping for Real-Time Applications in Wireless Networks},
	booktitle = {2013 IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS (ICC)},
	series = {IEEE International Conference on Communications},
	year = {2013},
	note = {IEEE International Conference on Communications (ICC), Budapest, HUNGARY, JUN 09-13, 2013},
	pages = {2949-2953},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {In this paper, we consider a multimedia data transmission system over a wireless channel, where packets are queued at the transmitter. Multimedia data transmission over wireless networks often suffers from delay, jitter and packet loss. The main problem to implement a wireless network is the high and variable bit error rate in the radio link (fading, shadowing etc). Among the most important performance measures for real time applications are the packet loss probability. In order to improve the radio link, one often retransmits packets that have not been well received (using the Automatic Retransmission reQuest). This however may lead to queuing phenomena and increased delay due to retransmissions, and to losses of packets due to buffer overflow. In this paper, we allow that the number of retransmission is finite. We model the system state by a three-dimensional Markov chain which represents the evolution the radio link state, the number of packets in the buffer and the number of transmission of a packet in the service. We use an advanced approach based on the theory of singular perturbation in order to compute some performance of interest.},
	isbn = {978-1-4673-3122-7},
	issn = {1550-3607},
	author = {Kobbane, A. and Ben-Othman, J. and El Koutbi, M.}
}
