Papers by Youssef Yaqinou
Multimedia Tools and Applications, 2021
The Mobile Edge Computing (MEC) environment provides leading-edge services to smart mobile device... more The Mobile Edge Computing (MEC) environment provides leading-edge services to smart mobile devices (SMDs). Besides, computation offloading is a promising service in 5G: it reduces battery drain and applications’ execution time. In this context, we consider a general system consisting of a multi-cell communication network where each base station (BS) is equipped with a MEC server to provide computation offloading services to nearby mobile users. In addition, each SMD handles multiple independent offloadable heavy tasks that are latency-sensitive. The purpose of this article is to jointly optimize tasks’ offloading decisions as well as the allocation of critical radio resources while minimizing the overall energy consumption. Therefore, we have formulated a bi-objective optimization problem that is NP-hard. Because of the short decision time constraint, the optimal solution implementation is infeasible. Accordingly, with the use of the weighted aggregation approach, we propose Intelligent Truncation based Hybrid Local Search (ITHLS) solution. In critical radio resources situations, our solution jointly minimizes the number of penalized SMDs and the overall consumed energy. Finally, simulation experiments were realized to study the ITHLS solution performance compared to some effective state of the art solutions, and the simulation results in terms of decision-making time, energy and number of truncated SMDs are very promising.
2019 5th International Conference on Optimization and Applications (ICOA), 2019
Mobile edge computing (MEC) provides remote computation capacity at the edge of mobile networks i... more Mobile edge computing (MEC) provides remote computation capacity at the edge of mobile networks in close proximity to smart mobile devices (SMDs). These devices generally possess limited processing capacity and battery power. Hence, heavy tasks that require a lot of computation and are energy consuming must be offloaded to a MEC server. Actually, it must consider the wireless network state, the number of SMDs requesting the computation offloading, and the available radio resources. In this paper, we consider a multi-mobile users MEC system, where multiple SMDs demand computation offloading to a MEC server, and we take into account the presence of particular subscribers having priority for computation offloading services. The purpose of this paper is to jointly optimize task offloading decisions and the allocation of critical radio resources while maintaining the priority of certain mobile device users and minimizing overall power consumption. Therefore, we have formulated a bi-objective optimization problem which is NP-Hard. Accordingly, with the use of the weighted aggregation approach, we propose and evaluate a heuristic that, in addition to minimizing the overall energy, minimizes the number of penalized SMDs in case where the radio resources are critical. The obtained results in terms of energy and satisfaction of the SMDs are very encouraging.
Lecture Notes in Mechanical Engineering, 2020
The Mobile Edge Computing (MEC) provides leading-edge services to multiple smart mobile devices (... more The Mobile Edge Computing (MEC) provides leading-edge services to multiple smart mobile devices (SMDs). Besides, computation offloading is a promising service in the 5G networks: it reduces battery drain and applications' execution time. These SMDs generally possess limited battery power and processing capacity. In addition, the local CPU frequency allocated to processing has a huge impact on SMDs energy consumption. In this paper, we consider a multiuser MEC system, where multiple SMDs demand computation offloading to a MEC server. The weighted sum of the overall energy consumptions and latencies represent the optimization problem's objective. In this problem, we jointly optimize offloading decisions, radio resource allocation and local computational resources allocation. The results obtained using our heuristic scheme show that it achieves good performance in terms of energy and latency. Accordingly, its achievement is encouraging compared to both cases where we perform local execution only or complete tasks offloading only.
2020 5th International Conference on Cloud Computing and Artificial Intelligence: Technologies and Applications (CloudTech)
In Mobile Cloud Computing, Smart Mobile Devices (SMDs) and Cloud Computing are combined to create... more In Mobile Cloud Computing, Smart Mobile Devices (SMDs) and Cloud Computing are combined to create a new infrastructure that allows data processing and storage outside the device. The Internet of Things refers to the billions of physical devices that are connected to the Internet. With the rapid development of these, it is clear that the requirements are largely based on the need for autonomous devices to facilitate the services required by applications that require rapid response time and flexible mobility. In this article, we study the management of computational resources and the trade-off between the consumed energy by an SMD and the processing time of its tasks. For this, we define a system model, a problem formulation and offer heuristic solutions for offloading tasks in order to jointly optimize the allocation of computing resources under limited energy and sensitive latency. In addition, we use the residual energy of the SMD battery and the sensitive latency of its tasks in defining the weighting factor of energy consumption and processing time.
Embedded Systems and Artificial Intelligence, 2020
The deployment of edge computing forms a two-tier mobile computing network where each computation... more The deployment of edge computing forms a two-tier mobile computing network where each computation task can be processed locally or at the edge node. In this paper, we consider a single mobile device equipped with a list of heavy off-loadable tasks. Our goal is to jointly optimize the offloading decision and the computing resource allocation to minimize the overall tasks processing time. The formulated optimization problem considers both the dedicated energy capacity and the processing deadlines. Therefore, as the obtained problem is NP-hard and we proposed a simulated annealing-based heuristic solution scheme. In order to evaluate and compare our solution, we carried a set of simulation experiments. Finally, the obtained results in terms of total processing time are very encouraging. In addition, the proposed scheme generates the solution within acceptable and feasible timeframes.
International Journal of Electrical and Computer Engineering (IJECE), 2019
With the fifth-generation (5G) networks, Mobile edge computing (MEC) is a promising paradigm to p... more With the fifth-generation (5G) networks, Mobile edge computing (MEC) is a promising paradigm to provide near computing and storage capabilities to smart mobile devices. In addition, mobile devices are most of the time battery dependent and energy constrained while they are characterized by their limited processing and storage capacities. Accordingly, these devices must offload a part of their heavy tasks that require a lot of computation and are energy consuming. This choice remains the only option in some circumstances, especially when the battery drains off. Besides, the local CPU frequency allocated to processing has a huge impact on devices energy consumption. Additionally, when mobile devices handle many tasks, the decision of the part to offload becomes critical. Actually, we must consider the wireless network state, the available processing resources at both sides, and particularly the local available battery power. In this paper, we consider a single mobile device that is en...
International Journal of Electrical and Computer Engineering (IJECE), 2020
In recent years, the importance of the mobile edge computing (MEC) paradigm along with the 5G, th... more In recent years, the importance of the mobile edge computing (MEC) paradigm along with the 5G, the Internet of Things (IoT) and virtualization of network functions is well noticed. Besides, the implementation of computation-intensive applications at the mobile device level is limited by battery capacity, processing capabalities and execution time. To increase the batteries life and improve the quality of experience for computationally intensive and latency-sensitive applications, offloading some parts of these applications to the MEC is proposed. This paper presents a solution for a hard decision problem that jointly optimizes the processing time and computing resources in a mobile edge-computing node. Hence, we consider a mobile device with an offloadable list of heavy tasks and we jointly optimize the offloading decisions and the allocation of IT resources to reduce the latency of tasks’ processing. Thus, we developped a heuristic solution based on the simulated annealing algorith...
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Papers by Youssef Yaqinou