@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.}
}
@article {Assila2020,
	title = {Improving caching resource management: A pricing economic approach using Cournot, Bertrand, and Stackelberg game models},
	journal = {International Journal of Communication Systems},
	year = {2020},
	note = {cited By 3},
	abstract = {Over-The-Top broadcasts a huge number of medias that mobile network operators have to manage efficiently before to deliver it to their subscribers. We propose an economic pricing approach to address caching resource management issues in the 5G wireless networks and to overcome limitations in terms of throughput, latency, and reliability. Moreover, we consider this approach based on an oligopolistic multi-market deducted from Cournot, Stackelberg, and Bertrand models. For simulation purpose, we consider the routing protocol (Ad-hoc On-Demand Distance Vector [AODV]) commonly used for the wireless network. We use the NS-2 package, and we analyze results in terms of End-to-End delay representing latency, throughput, packet delivery ratio, and normalized network load. {\textcopyright} 2020 John Wiley \& Sons, Ltd.},
	keywords = {5G mobile communication systems, Ad hoc on demand distance vector, caching, Costs, Cournot, Economics, Mobile network operators, Mobile telecommunication systems, Natural resources management, Packet delivery ratio, Resource allocation, Resource Management, Stackelberg, Stackelberg Games, wireless networks},
	doi = {10.1002/dac.4358},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85079387484\&doi=10.1002\%2fdac.4358\&partnerID=40\&md5=70ae8f696b34a21860572f8c3437dd46},
	author = {Assila, B. and Kobbane, A. and Ben-Othman, J.}
}
@conference {Fellir2020377,
	title = {A multi-Agent based model for task scheduling in cloud-fog computing platform},
	booktitle = {2020 IEEE International Conference on Informatics, IoT, and Enabling Technologies, ICIoT 2020},
	year = {2020},
	note = {cited By 9},
	pages = {377-382},
	abstract = {The Internet-of-Things (IoT) is a network of physical objects that communicate and exchange data between them over the Internet. These objects, or things, generate a massive amount of data that require powerful analytics approaches and data storage platforms - this is where cloud computing may well come into play. The classic cloud computing paradigm, however, faces several serious issues, due to centralization, such as low performance, latency issues, security issues, bandwidth obstacle, etc.. The Fog (Edge) computing paradigm has been proposed to improve the performance and to address other weaknesses, by providing IoT data processing and storage capabilities locally at the edges (IoT devices), instead of sending all the data to the centralized cloud. Whether in cloud or in fog computing, with billions of devices and a huge number of simultaneous requests, managing limited resources becomes essential. One of the important steps in resource management is task scheduling. In this paper, we consider task scheduling in a cloud-fog computing platform, we propose a multi-agent based model that aims at serving the most important task first, taking into consideration the task priority, its wait time, its status and the resources required to complete it successfully. Furthermore, in the scheduling process, we first propose an update to the priority value of the task, while taking into consideration its dependencies to other tasks and their priorities. Simulation results show that our proposed model can lead to better resource utilization and improve performance. {\textcopyright} 2020 IEEE.},
	keywords = {Autonomous agents, Computational methods, Computing paradigm, Computing platform, Data handling, Digital storage, Fog, Fog computing, Improve performance, Internet of thing (IOT), Internet of things, Multi agent systems, Multi-agent based modeling, Multitasking, Resource Management, Resource utilizations, Scheduling, Scheduling algorithms, Simulation platform, Storage capability},
	doi = {10.1109/ICIoT48696.2020.9089625},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85085496418\&doi=10.1109\%2fICIoT48696.2020.9089625\&partnerID=40\&md5=4ee0e2ab76422fc76603ec3b6f6942cb},
	author = {Fellir, F. and El Attar, A. and Nafil, K. and Chung, L.}
}
@article {11273146620151201,
	title = {Hybrid Approach for Project Portfolio Selection Taking Account of Resources Management and Interactions between Projects.},
	journal = {Journal of Digital Information Management},
	volume = {13},
	number = {6},
	year = {2015},
	pages = {451 - 461},
	abstract = {From among numerous opportunities, organizations must select the projects they decide to pursue. The consequences of poor decisions can be extremely expensive. This is why many methods and models for project portfolio selection have been developed. In this paper, we propose a project portfolio selection approach that combines essentially a business strategy method with a scoring model. The financial method is also present of the fact that the scoring model considers the financial value as an important criterion. The proposed hybrid approach also includes recommendations on the selection criteria provided. Two criteria are identified: the value and risk, the third is at the option of the organization for more flexibility. The approach provides a phase control on two elements: the management of resources and interactions between projects. [ABSTRACT FROM AUTHOR]},
	keywords = {Blended learning, Financial management, Portfolio management (Investments), Project Portfolio Management, Resource Management, Resource management {\textendash} Research, Simulation, Strategic planning},
	issn = {09727272},
	url = {http://search.ebscohost.com/login.aspx?direct=true\&db=iih\&AN=112731466\&site=ehost-live},
	author = {Benaija, Khadija and Kjiri, Laila}
}
