Browsing by Author "Belgacem, Ali"

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ChatGPT backend: A comprehensive analysis
(Institute of Electrical and Electronics, 2023) Belgacem, Ali; Bradai, Abbas; Beghdad-Bey, Kadda
Artificial intelligence (AI) has transformed the field of natural language processing, enabling substantial advances in understanding, interpreting, and producing human language. The ability of AI to find new solutions to difficult linguistic expressions has led to the birth of sophisticated language models such as ChatGPT. This model uses cutting-edge deep learning algorithms to produce high-quality, human-like writing in response to natural language inputs. ChatGPT has an amazing capacity to recognize context, evaluate sentiment, and provide coherent and appropriate replies. It has a wide range of applications, from virtual assistants and customer support bots to language translation and content development. Therefore, understanding its backend has become essential. In this paper, we summarize the key principles underlying the operation of ChatGPT's back-end. This study is required reading for ChatGPT researchers because it covers critical aspects of the ChatGPT backend. It includes essential information for researchers looking to improve ChatGPT's performance or create new language models based on its architecture
Intelligent multi-agent reinforcement learning model for resources allocation in cloud computing
(Elsevier, 2022) Belgacem, Ali; Mahmoudi, Saïd; Kihl, Maria
Now 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 time
Machine learning in the medical field: A comprehensive overview
(Institute of Electrical and Electronics Engineers Inc, 2023) Belgacem, Ali; Khoudi, Asmaa; Boudane, Fatima; Berrichi, Ali
Machine learning utilization in medicine has increased interest over the last few years. With its impressive results in treating diseases and medical conditions, it will be important to understand and analyze how the scientific community has used it. Thus, opening up space for new research and opportunities in medicine. The objective of this study is to review the literature on machine learning applications in the medical sector. Therefore, we conducted an extensive research by reviewing recent studies and surveys on machine-learning health solutions. As a result, we offer, in this paper, a fresh study affirming the foundations and necessities of a machine learning application in the medical field. We also provide a breakdown of current research trends, which highlights future research opportunities.
A machine learning model for improving virtual machine migration in cloud computing
(Springer, 2023) Belgacem, Ali; Mahmoudi, Saïd; Ferrag, Mohamed Amine
Cloud 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 stage
Multi-objective workflow scheduling in cloud computing : trade-off between makespan and cost
(Springer, 2021) Belgacem, Ali; Beghdad-Bey, Kadda
Recently, modern businesses have started to transform into cloud computing platforms to deploy their workflow applications. However, scheduling workflow under resource allocation is significantly challenging due to the computational intensity of the workflow, the dependency between tasks, and the heterogeneity of cloud resources. During resource allocation, the cloud computing environment may encounter considerable problems in terms of execution time and execution cost, which may lead to disruptions in service quality given to users. Therefore, there is a necessity to reduce the makespan and the cost at the same time. Often, this is modeled as a multi-objective optimization problem. In this respect, the fundamental research issue we address in this paper is the potential trade-off between the makespan and the cost of virtual machine usage. We propose a HEFT-ACO approach, which is based on the heterogeneous earliest end time (HEFT), and the ant colony algorithm (ACO) to minimize them. Experimental simulations are performed on three types of real-world science workflows and take into account the properties of the Amazon EC2 cloud platform. The experimental results show that the proposed algorithm performs better than basic ACO, PEFT-ACO, and FR-MOS