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Microservices Architecture (MSA) has emerged as a promising paradigm for building scalable and flexible software systems. While extensive research focuses on MSA's technical aspects, there remains a gap in understanding how practitioners make decisions to adopt and implement MSA in real-world organizational contexts. To address this gap, we conducted an in-depth qualitative study through 30 semi-structured interviews with experienced practitioners in the field. Our investigation unveils the intricate factors driving practitioners' decision-making processes during MSA adoption. We highlight the multifaceted influences of reusability, scalability, extensibility, maintainability, and other factors, shedding light on the motivations behind adopting MSA. Moreover, we delve into key strategies practitioners employ during MSA adoption, emphasizing the importance of the Modulith approach as a bridge between monolithic and MSA Our findings underscore the significance of practitioner experience in shaping MSA adoption decisions. [ABSTRACT FROM AUTHOR]

The growing advent of the Internet of Things (IoT) users is driving the adoption of cloud computing technologies. The integration of IoT in the cloud enables storage and computational capabilities for IoT users. However, security has been one of the main concerns of cloud-integrated IoT. Existing work attempts to address the security concerns of cloud-integrated IoT through authentication, access control, and blockchain-based methods. However, existing frameworks are somewhat limited by scalability, privacy, and centralized structures. To mitigate the existing problems, we propose a blockchain-based distributed access control method for secure storage in the IoT cloud (BL-DAC). Initially, the BL-DAC performs decentralized authentication using the Quantum Neural Network Cryptography (QNNC) algorithm. IoT users and edge nodes are authenticated in the blockchain deployed by distributed Trusted Authorities (TAs) using multiple credentials. The user data is classified into sensitive and non-sensitive categories using the Enhanced Seagull Optimization (ESO) algorithm. Also, the authentication to access this data is performed by a decentralized access control method using smart contract policy. Sensitive user data is encrypted using the QNNC algorithm and stored in the private cloud. In contrast, non-sensitive data is stored in the public cloud, and IPFS is used to store data in a decentralized manner with high reliability. In addition, data security is improved by using a hierarchical blockchain which improves scalability by managing the multiple blockchains hierarchically and is lightweight using Proof of Authentication Consensus (PoAH). The BL-DAC is simulated and validated using the Network Simulator-3.26 simulation tool and validated. This work shows better results than the compared ones in terms of validation metrics such as throughput (26%), encryption time (19%), decryption time (16%), response time (15%), block validation time (31%), attack detection rate (16%), access control precision (13%), and scalability (28%). [ABSTRACT FROM AUTHOR]

In this ever-developing technological world, one way to manage and deliver services is through cloud computing, a massive web of heterogenous autonomous systems that comprise adaptable computational design. Cloud computing can be improved through task scheduling, albeit it being the most challenging aspect to be improved. Better task scheduling can improve response time, reduce power consumption and processing time, enhance makespan and throughput, and increase profit by reducing operating costs and raising the system reliability. This study aims to improve job scheduling by transferring the job scheduling problem into a bin packing problem. Three modifies implementations of bin packing algorithms were proposed to be used for task scheduling (MBPTS) based on the minimisation of makespan. The results, which were based on the open-source simulator CloudSim, demonstrated that the proposed MBPTS was adequate to optimise balance results, reduce waiting time and makespan, and improve the utilisation of the resource in comparison to the current scheduling algorithms such as the particle swarm optimisation (PSO) and first come first serve (FCFS). [ABSTRACT FROM AUTHOR]

The smart city is a technological framework that connects the city's different components to create new opportunities. This connection is possible with the help of the Internet of Things (IoT), which provides a digital personality to physical objects. Some studies have proposed integrating Blockchain technology with IoT in different use cases as access, orchestration, or replicated storage layer. The majority of connected objects' capacity limitation makes the use of Blockchain inadequate due to its redundancy and its conventional processing-intensive consensus like PoW. This paper addresses these challenges by proposing a NOVEL model of a lightweight Blockchain framework (IoT-SmartChain), with a lightweight consensus and a lightweight structure. The framework architecture presents a role hierarchy of connected objects according to their computational and storage capacity. This organization allows all things to be linked even indirectly via different interfaces and to benefit from the power of high-capacity objects such as Fog and Edge computing nodes. Data is validated and added to the blockchain ledger by running a lightweight consensus called Proof of Random Participation (PoRP), which reduces the blockchain nodes' high computing power requirement. The TOPIC subscription-based data storage strategy called Assisted Selected Relevant Data in Local Ledger (ASRDLL) reduces the data size of a node's local ledger and the entire network's data size. This strategy is assisted by a centralized algorithm that optimizes the overall network size by adjusting the choice of TOPICS. The storage capacity, computational power, and energy consumption have been evaluated by a proof of concept implementation under NodeJS. [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]

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]

Due to the pervasive nature of new systems, information security raises new concerns and issues. Organizations need to understand the data and systems protection mechanisms. To address these concerns, some practices and standards now include paragraphs and chapters on how managing and governing information security. The proposed standards and best practices designed to guide organizations in there information security governance strategy, but do not define the practical framework to implement or measure the organization security governance. The aims of this work are to propose a capability assessment framework for the information security governance in organizations. The proposed capability maturity framework will help organizations to assess their capability maturity state and to address the procedural, technical and human aspects of information security governance and management process. The resultant framework called CAFISGO enables organizations to understand their key information security strengths, weaknesses, and areas for improvement. [ABSTRACT FROM AUTHOR]

Wireless sensor networks are increasingly used in a wide range of potential applications, including security and surveillance, control, actuation and maintenance of complex systems and fine-grain monitoring of indoor and outdoor environments. The nature of wireless sensor networks makes them very vulnerable to attack. The mobile nodes are randomly distributed, there are no physical obstacles for the adversary, therefore, they can be easily captured, and attacks can come from all directions and target any node. Consequently, security of wireless sensor networks (WSN) is the most challenging for this type of network. Intrusion Detection Systems (IDSs) can play an important role in detecting and preventing security attacks. In this paper, we propose a lightweight intrusion detection system for sensor networks. Our intrusion detection model exploits advantage of support vector machine (SVM) and signature model to detect malicious behaviors and provide a global lightweight IDS in cluster based topology. The proposed model can detect and prevent most of routing attacks. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) constitute a main component of Internet of Things (IoT) that is emerging as an attractive paradigm. In this paper, we propose a novel dual-radio architecture by adding a high-bandwidth radio on every sensor node. In WSNs, each sensor is equipped with two radio interfaces: the low-power IEEE 802.15.4 radio used on all sensors to transmit data within the network and the high-bandwidth 3G /LTE radio activated only on a subset of sensors, referred to as gateways, for sending data to the IoT. given the traffic demand for each sensor nodes, the number of gateways to deploy and the interference model, we propose a novel deployment method for placing optimal number of gateways in the WSNs where sensor nodes can join the IoT through the network's gateway, such that the lifetime and total throughput that can be supported is maximized while it also ensure a fairness among all sensor nodes. Due to the NP- hardness of the problem, we then propose a novel heuristic by decomposing the problem into two sub-problems and solving them separately. From our extensive simulation conducted, using ns-2, we identified that our model improves network performance, including the network lifetime prolongation, throughput maximization, network scalability improvement, the average data delivery delay reduction and lead a high traffic load to meet the diverse application needs of WSNs. [ABSTRACT FROM AUTHOR]

One of the main challenges in wireless sensor networks (WSN) is to save severely constrained energy resources, obtain long system lifetime and to guarantee quality of service (QoS). In this paper, a novel energy-aware coverage and connectivity preservation routing protocol (ECCRP) is presented to maximize the working time of full coverage in a given WSN regardless of the deployment patterns of the sensor nodes. The basic idea of the proposed ECCRP protocol is to take the remaining energy of the nodes, coverage redundancy of its sensing ranges and Euclidean distance between nodes and the optimal location of gateways into consideration when selecting gateways and cluster-head nodes. We define a cost metrics that favors those nodes being more energy-redundantly covered as better candidates for cluster heads in a way to determine an energy-efficient route for a data from elected cluster head to the base station through elected gateway. The simulation results demonstrate that the proposed protocol improve the performance of sensing coverage, reduce communications energy, prolong the effective network lifetime with optimal data delivery, increase the duration of the on-duty network and provide up to 139.90% of extra service time with 100% sensing coverage ratio comparing with other existing protocols. [ABSTRACT FROM AUTHOR]

Nowadays, Cloud Computing (CC) has emerged as a new paradigm for hosting and delivering services over the Internet. However, the wider deployment of Cloud and the rapid increase in the capacity, as well as the size of data centers, induces a tremendous rise in electricity consumption, escalating data center ownership costs and increasing carbon footprints. This expanding scale of data centers has made energy consumption an imperative issue. Besides, users' requirements regarding execution time, deadline, QoS have become more sophisticated and demanding. These requirements often conflict with the objectives of cloud providers, especially in a high-stress environment in which the tasks have very critical deadlines. To address these issues, this paper proposes an efficient Energy-Aware Tasks Scheduling with Deadline-constrained in Cloud Computing (EATSD). The main goal of the proposed solution is to reduce the energy consumption of the cloud resources, consider different users' priorities and optimize the makespan under the deadlines constraints. Further, the proposed algorithm has been simulated using the CloudSim simulator. The experimental results validate that the proposed approach can effectively achieve good performance by minimizing the makespan, reducing energy consumption and improving resource utilization while meeting deadline constraints. [ABSTRACT FROM AUTHOR]