The goal of the distributed computing paradigm known as "cloud computing," which necessitates a large number of resources and demands, is to share the resources as services delivered over the internet. Task ...
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The goal of the distributed computing paradigm known as "cloud computing," which necessitates a large number of resources and demands, is to share the resources as services delivered over the internet. Task scheduling is a very significant stage in today's cloud computing. While lowering the makespan and cost, the task scheduling method must schedule the tasks to the virtual machines. Various academics have proposed many scheduling methods for organizing work in cloud computing environments. Scheduling has been considered the most important for cloud computing since it might directly impact a system's performance, including the efficiency of resource utilization and running costs. This paper has compared all the already used algorithms that work on different parameters. We have tried to give better solutions for resource allocation and resource scheduling. In this study, various swarm optimization, evolutionary, physical, evolving, and fusion meta-heuristic scheduling methods are categorized according to the environment of the scheduling problem, the main scheduling goal, the task-resource mapping pattern, and the scheduling constraint. More specifically, the fundamental concepts of cloud task scheduling are addressed without difficulty.
Despite the significant growth of Internet of Things (IoT), there are prominent limitations of this emerging technology, such as limited processing power and storage. Along with the expansion of IoT networks, the fog-...
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Despite the significant growth of Internet of Things (IoT), there are prominent limitations of this emerging technology, such as limited processing power and storage. Along with the expansion of IoT networks, the fog-cloud computing paradigm has been developed to optimize the provision of services to IoT users by offloading computations to the more powerful processing resources. In this paper, with the aim of optimizing multiple objectives of makespan, energy consumption, and cost, we develop a novel automatic three-module algorithm to schedule multiple task graphs offloaded from IoT devices to the fog-cloud environment. Our algorithm combines the Genetic Algorithm (GA) and the Random Forest (RF) classifier, which we call Hybrid GARF (HGARF). Each of the three modules has a responsibility and they are repeated sequentially to extract knowledge from the solution space in the form of IF-THEN rules. The first module is responsible for generating solutions for the training set using a GA. Here, we introduce a chromosome encoding method and a crossover operator to create diversity for multiple task graphs. By expressing a concept called bottleneck and two conditions, we also develop a mutation operator to identify and reduce the workload of certain processing centers. The second module aims at generating rules from the solutions of the training set, and to that end employs an RF classifier. Here, in addition to proposing features to construct decision trees, we develop a format for extracting and recording IF-THEN rules. The third module checks the quality of the generated rules and refines them by predicting the processing resources as well as removing less important rules from the rule set. Finally, the developed HGARF algorithm automatically determines its termination condition based on the quality of the provided solutions. Experimental results demonstrate that our method effectively improves the objective functions in large-size task graphs by up to 13.24 % compared
cloud computing service composition integrates services, distributed and diverse by nature, into an integrated entity that can meet a user's requirement with better effectiveness. However, some obstacles regarding...
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cloud computing service composition integrates services, distributed and diverse by nature, into an integrated entity that can meet a user's requirement with better effectiveness. However, some obstacles regarding high latency and suboptimal Quality of Service (QoS) still exist in a dynamic multi-cloud environment. This study addresses the limitations of traditional optimization algorithms in service composition, specifically the premature convergence and lack of population diversity in the Moth-Flame Optimization (MFO) algorithm. We propose the modified MFO algorithm with a new mechanism called Stagnation Finding and Replacement (SFR) to enhance the diversity of the population. It finds the static solutions based on a distance metric from globally optimal representative solutions and replaces them. MFO-SFR drastically improved all QoS metrics, such as response time, delay, and service stability. Empirical evaluations prove that MFO-SFR outperforms the baseline methods of multi-cloud service composition. It provides acomputationally efficient and adaptive solution to cloud service composition problems, ensuring better resource utilization and higher user satisfaction in dynamic multi-cloud environments.
Load balancing in cloud computing plays a vital role in optimizing resource utilization, enhancing performance, and managing task allocation within dynamic and highly virtualized environments. This review paper compre...
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Load balancing in cloud computing plays a vital role in optimizing resource utilization, enhancing performance, and managing task allocation within dynamic and highly virtualized environments. This review paper comprehensively explores the diverse spectrum of load balancing methods employed in cloud computing, shedding light on their characteristics, advantages, and limitations. More advanced load balancing techniques leverage intelligent algorithms and real-time data to make dynamic decisions. Both machine and deep learning-based approaches, including reinforcement learning and neural networks, have gained prominence for their ability to adapt to changing workloads and traffic patterns. These methods exhibit great promise in optimizing resource allocation and improving overall system performance. Additionally, this review delves into emerging trends such as edge computing, hybrid cloud deployments, and container orchestration, exploring the evolution of load balancing strategies to meet the demands of these evolving paradigms. This review paper offers a thorough overview of load balancing techniques in cloud computing, equipping researchers, practitioners, and cloud architects with essential insights for choosing the most appropriate load balancing strategies tailored to their specific needs and use cases. It also highlights key challenges and outlines future research directions in this evolving field.
Nowadays, high energy amount is being wasted by computing servers and personal electronic devices, which produce a high amount of carbon dioxide. Thus, it is required to decrease energy usage and pollution. Many appli...
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The evolution of smart cities is intrinsically linked to advancements in computing paradigms that support real-time data processing, intelligent decision-making, and efficient resource utilization. Edge and cloud comp...
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The evolution of smart cities is intrinsically linked to advancements in computing paradigms that support real-time data processing, intelligent decision-making, and efficient resource utilization. Edge and cloud computing have emerged as fundamental pillars that enable scalable, distributed, and latency-aware services in urban environments. cloud computing provides extensive computational capabilities and centralized data storage, whereas edge computing ensures localized processing to mitigate network congestion and latency. This survey presents an in-depth analysis of the integration of edge and cloud computing in smart cities, highlighting architectural frameworks, enabling technologies, application domains, and key research challenges. The study examines resource allocation strategies, real-time analytics, and security considerations, emphasizing the synergies and trade-offs between cloud and edge computing paradigms. The present survey also notes future directions that address critical challenges, paving the way for sustainable and intelligent urban development.
Most corporations and organizations rely heavily on access control to protect data accessibility and enable resource sharing across networks and departments. However, with the development of cloud computing, tradition...
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Most corporations and organizations rely heavily on access control to protect data accessibility and enable resource sharing across networks and departments. However, with the development of cloud computing, traditional boundary protection struggles to mitigate the increasing attacks and threats. In addition, most existing dynamic access control methods match static rules with dynamic metrics, which cause system damage through their delayed responses to threats and attacks. The zero-trust architecture (ZTA) provides continuous authentication and dynamic authorization for all users to accommodate the security demands of cloud computing. Drawing inspiration from the ZTA, we first present a TBAC (Trust-based Access Control) model and design a trust assessment methodology to update user trustworthiness. Then, we introduce dynamic rules in the TBAC model to implement a dynamic access control system DR-TBAC (TBAC with Dynamic Rule). We apply the DQN (Deep Q-Network) algorithm to dynamically update the trust thresholds based on static rules comparing dynamic trust with predefined trust thresholds to achieve adaptive access control policies. In this paper, we rebuild the cloud security access environment from the perspective of dynamic trust and rule optimization and strengthen the constraints on user behaviors throughout the access control lifecycle of cloud computing. Finally, a thorough analysis and assessment regarding offline training models and the online deployment of the DR-TBAC system into the cloud platform highlight its security and accuracy relative to baseline models.
cloud technology provides scaled resources to support real-world applications, which often adopt cloud infrastructure for storage and computing. As cloud platforms enlarge their user numbers, optimizing infrastructure...
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cloud technology provides scaled resources to support real-world applications, which often adopt cloud infrastructure for storage and computing. As cloud platforms enlarge their user numbers, optimizing infrastructure usage and balancing the utility and satisfaction between the service provider and users under the Service Level Agreements (SLAs) becomes crucial. Since the dynamism from millions of user workloads makes task scheduling more challenging, especially in the emergence of energy-saving and real-time Quality of Service (QoS) requirements, traditional heuristic scheduling strategy fails to meet the more accurate and optimized operations. We propose an intelligent learning-based cloud task scheduling (ILbCTS) algorithm that leverages Deep Reinforcement Learning (DRL) technology to address this. The intelligent cloud task scheduling is a dynamic process that continuously adapts the action based on the changing states and the available rewards. The empirical studies with job sets of 1000, 5000 and 10,000 show that the ILbCTS algorithm outperforms the existing task scheduling algorithms, such as PSO, MBO and MOPSO, in terms of execution time, energy conservation and success rate of task scheduling.
The evolution of Artificial Intelligence of Things (AIoT), coupled with advancements in edge and cloud computing, has enabled the development of real-time IoT-based applications. Integrating Internet of Things (IoT) a...
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The evolution of Artificial Intelligence of Things (AIoT), coupled with advancements in edge and cloud computing, has enabled the development of real-time IoT-based applications. Integrating Internet of Things (IoT) and AI-driven edge-cloud services can address challenges such as early disease detection, system performance, data management and environmental sustainability in cloud-centric healthcare environments. To address these challenges, we propose HealthAIoT, a new architecture that utilises AIoT with cloud computing services to create a smart healthcare system. In our current implementation, HealthAIoT assesses the risk of developing diabetes mellitus in healthy individuals based on their personal health metrics and medical history;however, the proposed framework is fundamentally designed to be disease-agnostic and can be extended to incorporate detection and monitoring for other diseases. The HealthAIoT architecture consists of two main modules: a diabetes predictor and a cloud scheduler. The diabetes prediction module and cloud scheduler both utilise Multilayer Perceptron (MLP) models. The cloud scheduler manages health-related data and application requests from IoT devices, optimising resource utilisation and minimising the environmental impact of cloud services. The performance of the HealthAIoT framework is tested using the realistic testbed cloudAIBus. Experimental results demonstrate that the MLP-based diabetes predictor achieves 78.30% accuracy and an F1-score of 0.7719 on unseen patient data while cloud scheduler achieves 93.6% accuracy. Further, system performance is evaluated using metrics including energy consumption, carbon-free energy usage, cost, execution time, and latency. By identifying individuals at the highest risk of developing diabetes, the framework enables targeted preventative interventions, optimises resource usage and maximises impact, while also serving as a foundational framework for broader healthcare applications.
Scheduling workflows in this cloud computing era might as well be the way to go, given that resource allocation will be significantly improved, besides reduced execution time and costs. Most conventional scheduling al...
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Scheduling workflows in this cloud computing era might as well be the way to go, given that resource allocation will be significantly improved, besides reduced execution time and costs. Most conventional scheduling algorithms lack the potential for optimal performance among conflicting objectives like performance, cost-efficiency, and resource utilization. The paper proposes a new multi-objective workflow scheduling framework, where the Spider Monkey Optimization algorithm will be combined with the Fuzzy Self-Defense Algorithm. SMO algorithm emulates the foraging behavior of spider monkeys for a compelling exploration of the complex solution space to find superior task-resource mappings. Besides this, a fuzzy selfdefense strategy tackles the inherent uncertainties of dynamic cloud environments to make the framework more adaptable and resilient against failures and performance degradation. The proposed framework will be multi-objective, including the optimization of minimizing execution time, optimization of resource utilization, and energy consumption. Therefore, the model will significantly improve the balance of those competing goals, drawing strengths from SMO and fuzzy logic. The effectiveness is further validated through extensive experiments using synthetic and real-world workflow applications in a simulated cloud environment. Indeed, notable improvements have been observed along all the key performance indicators related to execution time, energy efficiency, and resource utilization. Besides, the hybrid framework is much more scalable and flexible in handling massive workflows, establishing its value as a practical resource management solution in cloud computing.
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