Showing papers by "Djamel Sadok published in 2020"

Resource allocation based on redundancy models for high availability cloud

Abstract: Today, most innovation on Information Technology and Communication is cloud-centric and an increasing number of organizations believe that this transition is ever more unavoidable. With this increased demand for Cloud services, providers are facing many challenges regarding how to avoid outages and optimization of resource management since they impact directly in costs and profits. In this paper, we propose the cost-based allocation (CBA), a resource allocation system that takes into consideration the minimum availability level required by the user, and the minimum cost to allocate resources while complying with the service availability forum redundancy models. Results show that, considering occupation and cost metrics, our CBA algorithm presents the best overall performance between evaluated strategies.

Assessing Deep Learning Models for Human-Robot Collaboration Collision Detection in Industrial Environments

Abstract: The increasing adoption of industrial robots to boost production efficiency is turning human-robot collaborative scenarios much more frequent. In this context, technical factory workers need to be safe at all times from collisions and prepare for emergencies and potential accidents. Another trend in industrial automation is the usage of machine learning techniques - specifically, deep learning algorithms - for image classification. Following these tendencies, this work evaluates the application of deep learning models to detect physical collision in human-robot interactions. Security camera images are used as the primary information source for intelligent collision detection. Unlike other proposed approaches in the literature that apply sensors like Light Detection And Ranging (LIDAR), Laser Range Finder (LRF), or torque sensors from robots, this work does not consider extra sensors, using only 2D cameras. Results show more than 99% of accuracy in the evaluated scenarios, revealing that approaches adopting deep learning algorithms could be promising for human-robot collision avoidance in industrial scenarios. The proposed models may support safety in industrial environments and reduce the impact of collision accidents.

Energy Consumption of a LoRaWAN Network using Jarvis Algorithm

Abstract: Energy consumption in LPWANs, such as LoRaWAN, plays an important role in determining the lifetime of wireless sensor networks motes. In this paper, the use of different battery technologies and the impact of the media access control layer (MAC) and physical layer (PHY) are studied. Among the metrics monitored in this scenario, it is energy efficiency and its impact on battery life for better performance. The Jarvis algorithm its run on the sensor network to examine the impact on the MAC and physical layer of LoRa. Its parameters such as spreading factor, channel activity detection, code rate and channel frequencies on the consumption of Energy; Executing the Jarvis algorithm showed that both technologies discharge in a similar way, but lithium batteries increase their resistance to discharge for a longer time and are more profitable when executing the Jarvis algorithm.

Using Reinforcement Learning to Allocate and Manage SFC in Cellular Networks

Abstract: In this paper, we propose the use of reinforcement learning to deploy a service function chain (SFC) of cellular network service and manage the VNFs operation. We consider that the SFC is deployed by the reinforcement learning agent considering a scenario with distributed data centers, where the virtual network functions (VNFs) are deployed in virtual machines in commodity servers. The VNF management is related to create, delete, and restart the VNFs. The main purpose is to reduce the number of lost packets taking into account the energy consumption of the servers. We use the Proximal Policy Optimization (PPO2) algorithm to implement the agent and preliminary results show that the agent is able to allocate the SFC and manage the VNFs, reducing the number of lost packets.