Computer
Mohammed E. Seno; Ban N. Dhannoon; Omer K. Jasim Mohammad
Abstract
Cloud computing is an evolving and high-demand research field at theforefront of technological advancements. It aims to provide software resources andoperates based on service-oriented delivery. Within the infrastructure as a service (IaaS)framework, the cloud offers end customers access to crucial infrastructure ...
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Cloud computing is an evolving and high-demand research field at theforefront of technological advancements. It aims to provide software resources andoperates based on service-oriented delivery. Within the infrastructure as a service (IaaS)framework, the cloud offers end customers access to crucial infrastructure resources,including CPU, bandwidth, and memory. When a cloud system fails to deliver asexpected, it is referred to as an event, signifying a deviation from the anticipated service.To meet their service-level agreement (SLA) obligations, cloud service providers (CSPs)must ensure continuous access to fault-tolerant, on-demand resources for their clients,particularly during outages. Consequently, finding the most efficient ways to accomplishtasks while considering the rapid depletion of resources has become an urgent concern.Researchers are actively working to develop optimal strategies tailored to the cloudenvironment. Machine learning plays a critical role in these endeavors, serving as a keycomponent in various cloud computing platforms. This study presents a comprehensiveliterature review of current research papers that employ machine learning algorithms topropose strategies for optimizing cloud computing environments. Additionally, the surveyprovides authors with invaluable resources by extensively exploring a diverse range ofmachine learning techniques and their applications in the field of cloud computing. Byexamining these areas, researchers aim to enhance their understanding of efficientresource allocation and scheduling, addressing the challenges posed by resource scarcitywhile meeting SLA obligations.
Computer
Asmaa Ibrahim Hussieen; Abeer Tariq MaoLood; Ekhlas Khalaf Gbash
Abstract
Conventional voting activities are often replaced by electronic voting (EV) in light of the quick expansion of the Internet. For a variety of reasons, various nations have lately switched to EV rather than conventional voting. Different EV systems were presented up to this point. In both practical and ...
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Conventional voting activities are often replaced by electronic voting (EV) in light of the quick expansion of the Internet. For a variety of reasons, various nations have lately switched to EV rather than conventional voting. Different EV systems were presented up to this point. In both practical and theoretical fields, on the other hand, there is no perfect solution. To meet such objectives, the researchers strive for preserving cryptographic primitives when developing high-efficiency e-voting schemes. The concept of fog computing was developed to improve network infrastructure to satisfy the demands of large amounts of data the same time as also increasing the efficiency of the processing power. Also, it has been created as well to address concerns with Cloud computing, like the distribution environment complexity, real-time response, mobility, and IoT application location awareness. The concentration of this study was on a complete review regarding the systems of EVs through various scholars as a platform to detect flaws or problems in the deployment of extremely secure EV systems. In addition, nations having a history of EV system adoption were examined. A concept for future work on establishing a safe EV system depends on problems discovered in numerous works.
Computer
sajjad shamkhi jaber; Yossra Ali; Nuha Ibrahim
Abstract
Task scheduling is one of the very crucial facets of cloud computing. The task scheduling method must assign jobs to virtual machines. In cloud computing, task scheduling includes a frontal influence on a system's resource utilization and operational costs. Diverse meta-heuristic algorithms, in addition ...
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Task scheduling is one of the very crucial facets of cloud computing. The task scheduling method must assign jobs to virtual machines. In cloud computing, task scheduling includes a frontal influence on a system's resource utilization and operational costs. Diverse meta-heuristic algorithms, in addition to their modifications, have been developed to improve the efficiency of task executions in the cloud. In this paper, a multiobjective optimization model is applied using the metaheuristics cuckoo search optimization algorithm (MCSO) to enhance the performance of a cloud system with limited computing resources while minimizing the time and cost. Finally, we analyze the performance of the proposed MCSO with the existing methods, such as Bee Life Algorithm (BLA), A Time–Cost aware Scheduling (TCaS) algorithm, Modified Particle Swarm Optimization (MPSO), and Round Robin (RR), for the evaluation metrics makespan and cost. Based on the outcomes of the experiments, it can be inferred that the proposed MCSO provides essential schedule jobs with the shortest makespan and average cost.