Publications Scientifiques
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Item A machine learning model for improving virtual machine migration in cloud computing(Springer, 2023) Belgacem, Ali; Mahmoudi, Saïd; Ferrag, Mohamed AmineCloud Computing is a paradigm allowing access to physical and application resources online via the Internet. These resources are virtualized using virtualization software to make them available to users as a service. Virtual machines (VMs) migration technique provided by virtualization technology impacts the performance of the cloud. It is a significant concern in this environment. When allocating resources, the distribution of VMs is unbalanced, and their movement from one server to another can increase energy consumption and network overhead, necessitating an improvement in VM migrations. This paper addresses the VMs migration issue by applying a machine learning model to reduce the VMs migration number and energy consumption. The proposed algorithm (named VMLM) is based on improving VM’s migration process and selection. It has been benchmarked with JVCMMD and EVSP solutions. The simulation results demonstrate the efficiency of our proposal, which includes two phases the machine learning preparing stage and the VMs migration stageItem The impact of Covid-19 on energy consumption in Algeria- study and outlook(Web of Science, 2021) Zighed, Mohammed; Benotmane, BenamarEnergy consumption is a major concern in the world, and even in Algeria, because of its eco- nomic and social impact on people’s way of life. All aspects and activities of life, including energy consumption, have been influenced by the deep sanitary crisis related to the Covid-19 pandemic, which has affected the world from 2020 until today. This study examines the energy consumption in Algeria for 2020 during the coronavirus pandemic. It was reported that a huge decline of 13% was recorded in the national consumption of energy in 2020 (petroleum products and natural gas) compared to 2019, falling from 67 MTOE to 59 MTEO. Electricity consumption has also dropped at a rate of 4%. This trend was due to the lockdown and containment policies implying a set of mea- sures serving as a non-clinical approach to mitigate the spread of the virus and better managing this sanitary crisis. Some of these measures could benefit the national energy-saving strategy out- side of the Covid-19 crisis. However, more technical and behavioral measures are highly required to ensure more effective saving and rationalize the use of energy, the main drive of the economy.Item Intelligent multi-agent reinforcement learning model for resources allocation in cloud computing(Elsevier, 2022) Belgacem, Ali; Mahmoudi, Saïd; Kihl, MariaNow more than ever, optimizing resource allocation in cloud computing is becoming more critical due to the growth of cloud computing consumers and meeting the computing demands of modern technology. Cloud infrastructures typically consist of heterogeneous servers, hosting multiple virtual machines with potentially different specifications, and volatile resource usage. This makes the resource allocation face many issues such as energy conservation, fault tolerance, workload balancing, etc. Finding a comprehensive solution that considers all these issues is one of the essential concerns of cloud service providers. This paper presents a new resource allocation model based on an intelligent multi-agent system and reinforcement learning method (IMARM). It combines the multi-agent characteristics and the Q-learning process to improve the performance of cloud resource allocation. IMARM uses the properties of multi-agent systems to dynamically allocate and release resources, thus responding well to changing consumer demands. Meanwhile, the reinforcement learning policy makes virtual machines move to the best state according to the current state environment. Also, we study the impact of IMARM on execution time. The experimental results showed that our proposed solution performs better than other comparable algorithms regarding energy consumption and fault tolerance, with reasonable load balancing and respectful execution timeItem Energy-Aware HEVC software decoding on mobile heterogeneous Multi-Cores architectures(Schloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing, 2022) Khernache, Mohammed Bey Ahmed; Boukhobza, Jalil; Benmoussa, Yahia; Menard, DanielVideo content is becoming increasingly omnipresent on mobile platforms thanks to advances in mobile heterogeneous architectures. These platforms typically include limited rechargeable batteries which do not improve as fast as video content. Most state-of-the-art studies proposed solutions based on parallelism to exploit the GPP heterogeneity and DVFS to scale up/down the GPP frequency based on the video workload. However, some studies assume to have information about the workload before to start decoding. Others do not exploit the asymmetry character of recent mobile architectures. To address these two challenges, we propose a solution based on classification and frequency scaling. First, a model to classify frames based on their type and size is built during design-time. Second, this model is applied for each frame to decide which GPP cores will decode it. Third, the frequency of the chosen GPP cores is dynamically adjusted based on the output buffer size. Experiments on real-world mobile platforms show that the proposed solution can save more than 20% of energy (mJ/Frame) compared to the Ondemand Linux governor with less than 5% of miss-rate. Moreover, it needs less than one second of decoding to enter the stable state and the overhead represents less than 1% of the frame decoding timeItem Mobile robot energy modelling integrated into ros and gazebo-based simulation environment(2021) Touzout, Walid; Benazzouz, Djamel; Benmoussa, YahiaMobile robots' autonomy is limited by the capacity of their batteries; thus, their energy consumption estimation and management are important issues to deal with energy minimization techniques, such as path planning, tasks scheduling etc. These techniques need to be tested, evaluated, and approved for different scenarios; however, this cannot be feasible in case of huge scenarios and may require much hardware setup. In this paper, we introduce a numerical solution by enriching the Robot Operating System (ROS) infrastructure with modelling, monitoring, and energy management tools by integrating an energy consumption model of a differential drive mobile robot into ROS/Gazebo-based simulator. The obtained results involve realtime power consumption of the virtual robot for predefined scenarios, and the total energy consumption is monitored numerically at the end of each scenario without any hardware requirement.Item Differential Drive Mobile Robot Energy Model Integration into ROS–Based Simulation Environment(2019) Touzout, Walid; Benazzouz, Djamel; Ouelmokhtar, Hand; Benmoussa, YahiaNowadays, mobile robots are used in different applications however they are constrained by the limitation of their batteries making reducing energy consumption a significant challenge for mobile robots’ community. Thus, energy consumption modelling is therefore becoming an important approach to reducing energy cost. However, power reduction techniques evaluation and approbation may require much hardware configuration and can be time consuming especially in case of huge scenarios. We present in this paper a methodology used to enrich the Robot Operating System (ROS) infrastructure with modelling, monitoring, and energy management tools by integrating an energy consumption model of a differential drive mobile robot into ROS and Gazebo-based simulator. The simulation results illustrate the instantaneous power consumption of the robot for three different scenarios. Thereafter, the total energy consumption can be monitored without any hardware requirement at the end of each scenarioItem A new stochastic petri nets modeling for dual cluster heads configuration in Energy-Harvesting WSNs(IEEE, 2021) Oukas, Nourredine; Boulif, MenouarThis paper proposes a new Stochastic Petri Nets modeling to describe the route taken by the packets to reach the base station from any sensor node in wireless sensor networks. This formulation examines the case where the network is structured into clusters. Each cluster contains two leaders: A Cluster Head and a Collector that cooperate to route the packets from the source to the endpoint. This configuration aims to conserve energy by balancing it through the network. Furthermore, given that a sensor node consumes the majority of its power in the communication process that is affected by the distance of the recipient, this formulation associates data gathering and data processing to the Collector whereas it associates the far sending task to the cluster head. From the proposed formulation, we derive performance formulas and we conduct some experimental analysis that allows to determine the most suitable compromise between energy consumption reduction and longevity of serviceItem Unmanned surface vehicle energy consumption modelling under various realistic disturbances integrated into simulation environment(Elsevier, 2021) Touzout, Walid; Benmoussa, Yahia; Benazzouz, Djamel; Moreac, Erwan; Diguet, Jean-PhilippeEnergy consumption estimation and management of the maritime Unmanned Surface Vehicles (USV) is an important issue to deal with energy minimization techniques such as path planning, tasks scheduling, etc. In this paper, we introduce the energy consumption parameter in USV simulation through three contributions: 1) An analytic USV's energy consumption model is developed based on the three-degrees-of-freedom dynamic model of surface vessels. 2) A reverse engineering approach is proposed to identify the previously used dynamic model parameters based on a set of scenarios executed within a recent simulation environment. 3) The simulator engine is enriched with the consumption modelling tools such that the power absorbed by the USV is instantaneously calculated and returned; thus, the required energy of any predefined scenario is available as a new simulation resultItem Generalized stochastic petri nets modelling for energy harvesting WSNs considering neighbors with different vicinity levels(IEEE, 2020) Oukas, Nourredine; Boulif, MenouarIn this paper, we use Generalized Stochastic PetriNets (GSPN) formalism to model the communication betweenan SN and its neighbors in wireless sensor networks (WSN).This modelling considers several actual considerations such assensor vacations and retrial calls phenomenon. Furthermore,given that sensor nodes (SN) consume almost all their energy inthe transmission process that varies according to the distanceof the neighbors, our model considers different levels of vicinityfor communicating neighbors. Our study proves that ourmodelling, which provides a more realistic approach todescribe the actual behavior of the WSN, can identify the inputparameter scenario to have a network with a good compromisebetween longevity and performanceItem A revised BROGO algorithm for leader election in wireless sensor and IoT networks(IEEE, 2017) Bounceur, Ahcène; Bezoui, Madani; Euler, Reinhardt; Lalem, Farid; Lounis, Massinissa