Browsing by Author "El Marai, Oussama"

Now showing 1 - 6 of 6

Closed-Form Expression For The Resources Dimensioning of Softwarized Network Services
(2019) Prados Garzon, Johnny; Taleb, Tarik; El Marai, Oussama; Bagaa, Miloud
A4 Artikkeli konferenssijulkaisussa
Network Function Virtualization ecosystem enables the automation of deployment and scaling of softwarized network services (SNSs), thus reducing their operational expenditures. This enables operators to handle workload fluctuations, to keep the desired performance, with great agility and reduced costs. However, to realize the automation of such management practices, it is needed to determine the amount of required resources to allocate the SNS so that its performance requirements are met. This problem is commonly referred to as resources dimensioning problem. In this paper, we address the derivation of a closed-form expression for the optimal resources dimensioning of an SNS in terms of cost or energy efficiency. The performance requirement considered for the SNS is a limit on its mean response time. The performance model considered for the SNS is practical and accurate. The usefulness of the derived closed-form expression is successfully validated by means of simulation. The scenario considered for the validation is a video optimization chain located at the SGi-LAN of a mobile network. © 2019 IEEE.
Coalition Game-based Approach for Improving the QoE of DASH-based Streaming in Multi-servers Scheme
(2020-12) El Marai, Oussama; Bagaa, Miloud; Taleb, Tarik
A4 Artikkeli konferenssijulkaisussa
Dynamic Adaptive Streaming over HTTP (DASH) is becoming the de facto method for effective video traffic delivery at large scale. Its primer success factor returns to the full autonomy given to the streaming clients making them smarter and enabling decentralized logic of video quality decision at granular video chunks following a pull-based paradigm. However, the pure autonomy of the clients inherently results in an overall selfish environment where each client independently strives to improve its Quality of Experience (QoE). Consequently, the clients will hurt each other, including themselves, due to their limited scope of perception. This shortcoming could be addressed by employing a mechanism that has a global view, hence could efficiently manage the available resources. In this paper, we propose a game theoretical-based approach to address the issue of the client’s selfishness in multi-server setup, without affecting its autonomy. Particularly, we employ the coalitional game framework to affect the clients to the best server, ultimately to maximize the overall average quality of the clients while preventing re-buffering. We validate our solution through extensive experiments and showcase the effectiveness of the proposed solution.
Ensuring High QoE for DASH-based Clients using Deterministic Network Calculus in SDN Networks
(2019) El Marai, Oussama; Prados Garzon, Johnny; Bagaa, Miloud; Taleb, Tarik
A4 Artikkeli konferenssijulkaisussa
HTTP Adaptive Streaming (HAS) is becoming the de-facto video delivery technology over best- effort networks nowadays, thanks to the myriad advantages it brings. However, many studies have shown that HAS suffers from many Quality of Experience (QoE)-related issues in the presence of competing players. This is mainly caused by the selfishness of the players resulting from the decentralized intelligence given to the player. Another limitation is the bottleneck link that could happen at any time during the streaming session and anywhere in the network. These issues may result in wobbling bandwidth perception by the players and could lead to missing the deadline for chunk downloads, which result in the most annoying issue consisting of rebuffering events. In this paper, we leverage the Software-Defined Networking paradigm to take advantage of the global view of the network and its powerful intelligence that allows reacting to the network changing conditions. Ultimately, we aim at preventing the re-buffering events, resulting from deadline misses, and ensuring high QoE for the accepted clients in the system. To this end, we use Deterministic Network Calculus (DNC) to guarantee a maximum delay for the download of the video chunks while maximizing the perceived video quality. Simulation results show that the proposed solution ensures high efficiency for the accepted clients without any rebuffering events which result in high user QoE. Consequently, it might be highly useful for scenarios where video chunks should be strictly downloaded on- time or ensuring low delay with high user QoE such as serving video premium subscribers or remote control/driving of an autonomous vehicle in future 5G mobile networks. © 2019 IEEE.
Improving Live Video Streaming Performance for Smart City Services
(2024) El Marai, Oussama
School of Electrical Engineering | Doctoral dissertation (article-based)
Our world is rapidly moving in all its aspects toward a more digitized and connected life, including transportation, education, farming, and healthcare. A major enabler for such transformation is ICT-related tremendous innovations in networking, computation, and storage, both in software and hardware at affordable prices. Owing to these phenomenal advances, many revolutionary paradigms, such as multi-access edge computing, self-driving vehicles, and Smart Cities, have emerged, promising rosy prospects and a flourishing future. An eminent feature of these futuristic technologies is automation, where objects can communicate (i.e., sending and receiving data), understand their environment, and adapt to changing conditions by taking the right decisions. Also, stringent requirements (e.g., low latency communication) might be needed by many services for their proper functioning. To successfully accomplish these tasks, many paradigms (e.g., software-defined networking and machine learning techniques) should be involved at different levels (e.g., network and decision-making levels). Most of today's applications and systems (e.g., over-the-top and surveillance platforms) require video streaming as a key technology. Video streaming applications rank as the most bandwidth-intensive services, especially when delivered at higher resolutions, such as FHD and 4K. Fortunately, 5G technology is already available and promises higher bandwidth that can reach up to 20GB. In addition, it requires huge data storage spaces when historical data is needed, which no longer becomes an issue with the dawn of edge and cloud computing. The target consumer (i.e., humans or machines) might demand heavy computation resources, often requiring GPU processing, which is also nowadays readily available and affordable. This dissertation is all about harnessing video streaming technology for enabling Smart City services and paradigms, such as self-driving vehicles. Towards this end, we start by addressing the problem of improving video streaming performance in terms of delivered video quality, stall-free sessions, and low latency streaming, for various services, including video streaming services and some use cases of self-driving vehicles. As data is the fuel that empowers most Smart City systems and services, we propose a cost-efficient and sustainable solution to create the digital twin of city roads, which mainly relies on video streaming data. The proposed solution represents an essential step towards realizing the Smart City paradigm and would create a valuable data asset that feeds and benefits various systems and domains such as intelligent transportation systems and tourism. Owing to the extreme importance of situational awareness in Smart Cities, notably in dense urban areas, we leverage the proposed digital twinning solution and machine learning techniques to raise the awareness of connected vehicles about their surroundings, as well as overall street awareness per defined regions while accounting for the amount of transmitted data over the network to avoid video streaming performance degradation.
Online Server-side Optimization Approach for Improving QoE of DASH Clients
(2017-12) El Marai, Oussama; Taleb, Tarik
A4 Artikkeli konferenssijulkaisussa
The many advantages of Dynamic Adaptive streaming over HTTP (DASH) made it one of the most prevalent video streaming technologies in recent years. Unfortunately, many studies have unveiled the QoE issue of users when multiple DASH clients compete for the bandwidth of a bottleneck link. This issue consists of several aspects, namely the frequent encoding changes, the unfair bandwidth allocation, the inefficient bandwidth utilization, and the relatively long convergence time. These aspects are indeed conflicting each other and resolving them entails tradeoffs. In this paper, we propose a new mathematical model that leverages a score matrix to ensure a fair sharing of the server's bottleneck link between competing clients and satisfies the requests of as many clients as possible and that is for efficient bandwidth utilization. The proposed solution is compared against notable solutions through computer-based simulations, and the results show that the proposed solution achieves high scores in terms of both efficiency and fairness.
Qualifying 5G SA for L4 Automated Vehicles in a Multi-PLMN Experimental Testbed
(2021-06-15) Pastor Figueroa, Giancarlo; Mutafungwa, Edward; Costa Requena, Jose; Li, Xuebing; El Marai, Oussama; Saba, Norshahida; Zhanabatyrova, Aziza; Xiao, Yu; Mustonen, Timo; Myrsky, Matthieu; Lammi, Lauri; Hamid, Umar Zakir Abdul; Boavida, Marta; Catalano, Sergio; Park, Hyunbin; Vikberg, Pyry; Lyytikäinen, Viljami
A4 Artikkeli konferenssijulkaisussa
National roaming, multi-SIM and edge computing constitute key 5G technologies for the cooperative perception and remote driving of L4 (automated) vehicles. To that end, this article reports our progress to trial these technologies at the multi-PLMN experimental 5G SA testbed of Aalto University, Finland. Overall, the objective is to qualify 5G as a core connectivity for connected, cooperative and automated mobility.