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Data mining nowadays became more and more important; it is used to extract knowledge from a large amount of data to help decision makers to make good decisions. Based on the type of the data and the type of the variable we want to predict, we choose the suitable algorithm, classification is one of the different algorithms used in data mining. In this survey paper, we first presented a brief overview about data mining and the data mining classification algorithms; then, we present classification algorithms used in data mining for prediction, and we present previous work and experiments using classification algorithms, such as Decision tree, Logistic regression and Naive Bayes. The aim is to give a clear insight of the classification algorithms and how they are used in several domains. [ABSTRACT FROM AUTHOR]

Employment is the main form of social integration, a factor of improving living conditions and preventing risks of poverty and vulnerability and the most appropriate indicator for assessing the level of social cohesion in a country. Graduates face every year real competitions to ensure their employability. We live now in a big data era, where the data is generated every day in large volumes, due to the use of technologies. But storing this data isn't the main objective, we need to use this data and take advantage of it by extracting valuable information from it in order to use it and improve a particular domain. Using data mining techniques will allow us to extract useful information and ameliorate employability by giving decision makers opportunities to make it better and predict in the future. In this paper, we present an intelligent system using data mining techniques on employability data, in a Big Data environment, which we used Hadoop ecosystem, and for data mining we used Rapid Miner Studio Educational Version 8.1.000. We presented first the characteristics of the proposed system, after that we presented the general architecture of the system and the tools and the technologies used, and finally we presented the system's process in details from the data collection till the results visualization. [ABSTRACT FROM AUTHOR]

Despite increased cloud service providers following advanced cloud infrastructure management, substantial execution time is lost due to minimal server usage. Given the importance of reducing total execution time (makespan) for cloud service providers (as a vital metric) during sustaining Quality-of-Service (QoS), this study established an enhanced scheduling algorithm for minimal cloudlet scheduling (CS) makespan with the deep Q-network (DQN) algorithm under MCS-DQN. A novel reward function was recommended to enhance the DQN model convergence. Additionally, an open-source simulator (CloudSim) was employed to assess the suggested work performance. Resultantly, the recommended MCS-DQN scheduler revealed optimal outcomes to minimise the makespan metric and other counterparts (task waiting period, resource usage of virtual machines, and the extent of incongruence against the algorithms). [ABSTRACT FROM AUTHOR]

Task scheduling is an attractive research topic in cloud computing nowadays. This process is very challenging and well known as NP-complete problem. Due to the dynamic and heterogeneous nature of user's request and provider's resource in cloud computing, the scheduling process still needs intelligent algorithms to achieve an efficient cloud resource allocation and to guarantee a good Quality of Service (QoS) for the users and their request classes. An important aspect for meeting these objectives is to design an effective task scheduling scheme which can not only satisfy users' varying priorities and QoS requirements, but also enhance providers' profit and system performances. In this paper, we introduce a new strategy to address the priority issue in both users' requests and providers' resources. We propose an efficient priority tasks scheduling called MCPTS, where the priority is adjusted according to four tasks' parameters including length, waiting time, deadline and burst time. MCPTS scheme consists of three sub-models such as tasks priority, task queueing priority and resources priority. A new hybrid multi-criteria decision-making (MCDM) method, namely ELECTRE III, and a meta-heuristic algorithm called differential evolution are proposed to evaluate and determine tasks' priorities. Further, we introduce a novel dynamic priority-queue algorithm based on queueing model. Furthermore, we adjust dynamically the resources priority based on tasks priority model in order to design an efficient and flexible relation between both resources and tasks classes. The proposed algorithm is validated through the CloudSim simulator. The experimental results indicate the superiority of MCPTS algorithm compared to other existing algorithms. Also, it shows the effectiveness of our algorithm in providing good system performance, satisfying users' priorities as well as QoS requirements, enhancing load balancing and improving resources utilization. [ABSTRACT FROM AUTHOR]

Task scheduling (TS) in cloud computing is a complex problem that involves balancing workload distribution, resource allocation, and power consumption. Existing methods often fail to optimize these objectives simultaneously and efficiently. This paper introduces a novel technique for scheduling independent tasks in cloud computing using multi-objective optimization and deep reinforcement learning (DRL). The proposed technique, DMOTS-DRL, combines Dueling deep Q-networks and dynamic prioritized experience replay to optimize two critical objectives: scheduling completion time (makespan) and power consumption. The performance of DMOTS-DRL is evaluated using CloudSim and compared with several state-of-the-art TS algorithms. The experimental results show that DMOTS-DRL outperforms the other algorithms in reducing makespan, power consumption, and other metrics, demonstrating its effectiveness and reliability for cloud computing services. Specifically, DMOTS-DRL achieves percentage improvements ranging from − 44.04 to − 0.19% in makespan, from − 0.26 to − 27.90% in power consumption, as well as better performance on other metrics such as energy consumption, degree of imbalance, resource utilization, and average waiting time. [ABSTRACT FROM AUTHOR]

Task scheduling is one of the most challenging aspects to improve the overall performance of cloud computing and optimize cloud utilization and Quality of Service (QoS). This paper focuses on Task Scheduling optimization using a novel approach based on Dynamic dispatch Queues (TSDQ) and hybrid meta-heuristic algorithms. We propose two hybrid meta-heuristic algorithms, the first one using Fuzzy Logic with Particle Swarm Optimization algorithm (TSDQ-FLPSO), the second one using Simulated Annealing with Particle Swarm Optimization algorithm (TSDQ-SAPSO). Several experiments have been carried out based on an open source simulator (CloudSim) using synthetic and real data sets from real systems. The experimental results demonstrate the effectiveness of the proposed approach and the optimal results is provided using TSDQ-FLPSO compared to TSDQ-SAPSO and other existing scheduling algorithms especially in a high dimensional problem. The TSDQ-FLPSO algorithm shows a great advantage in terms of waiting time, queue length, makespan, cost, resource utilization, degree of imbalance, and load balancing. [ABSTRACT FROM AUTHOR]