Improved Cat Swarm Optimization Algorithm for Load Balancing in the Cloud Computing Environment

International Journal of Advanced Computer Science and Applications, Год журнала: 2023, Номер 14(7)

Опубликована: Янв. 1, 2023

Recently, cloud computing has gained recognition as a powerful tool for providing clients with flexible platforms, software services, and cost-effective infrastructures. Cloud is form of distributed that allows users to store process data in virtual environment instead physical server. This beneficial because it businesses quickly scale up or down their capacity, reducing the need invest expensive hardware. As tasks continue grow exponentially usage services increases, scheduling these across diverse machines poses challenging NP-hard optimization problem substantial requirements, including optimal resource utilization levels, short execution time, reasonable implementation cost. The issue consequently been addressed using variety meta-heuristic algorithms. In this paper, we propose new load-balancing approach Cat Swarm Optimization (CSO) algorithm order distribute load among various servers within center. Statistical analyses indicate our superior previous research regard energy consumption, makespan, time required 30%, 35%, 40%, respectively.

Язык: Английский

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A Novel Attack on Complex APUFs Using the Evolutionary Deep Convolutional Neural Network

Intelligent Automation & Soft Computing, Год журнала: 2023, Номер 37(3), С. 3059 - 3081

Опубликована: Янв. 1, 2023

As the internet of things (IoT) continues to expand rapidly, significance its security concerns has grown in recent years. To address these concerns, physical unclonable functions (PUFs) have emerged as valuable tools for enhancing IoT security. PUFs leverage inherent randomness found embedded hardware devices. However, it been shown that some can be modeled by attackers using machine-learning-based approaches. In this paper, a new deep learning (DL)-based modeling attack is introduced break resistance complex XAPUFs. Because training DL models problem falls under category NP-hard problems, there significant increase use meta-heuristics (MH) optimize parameters. Nevertheless, widely recognized finding right balance between exploration and exploitation when dealing with problems pose challenge. challenges, novel migration-based multi-parent genetic algorithm (MBMPGA) developed train convolutional neural network (DCNN) order achieve higher rate accuracy convergence speed while decreasing run-time attack. proposed MBMPGA, non-linear migration model biogeography-based optimization (BBO) utilized enhance ability GA. A crossover then The behavior MBMPGA examined on two real-world problems. benchmark outperforms other MH algorithms rate. are also compared previous attacking several simulated challenge-response pairs (CRPs). simulation results XAPUF datasets show paper obtains more than 99% even 8-XAPUF. addition, MBMPGA-DCNN state-of-the-art attacks reduced timeframe smaller number required sets CRPs. area curve (AUC) architectures. achieved sensitivities, specificities, accuracies 99.12%, 95.14%, 98.21%, respectively, test datasets, establishing most successful method.

Язык: Английский

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Intelligent fault detection strategy for knowledge entities in fault semantic networks of distribution network based on siamese networks

PLoS ONE, Год журнала: 2024, Номер 19(5), С. e0303084 - e0303084

Опубликована: Май 16, 2024

The advent of smart grid technologies has brought about a paradigm shift in the management and operation distribution networks, allowing for intricate system information to be encapsulated within semantic network models. These models, while robust, are not immune faults their knowledge entities, which can arise from myriad issues, potentially leading verification failures operational disruptions. Addressing this critical vulnerability, our research delves into development novel fault detection methodology specifically tailored entity variables networks networks. In approach, we first construct state space equation that models behavior presence faults. This foundational framework enables us apply an unknown input observer strategy effectively detect anomalies system. To bolster identification process, introduce innovative use siamese network, neural architecture is proficient differentiating between similar datasets. Through simulation scenarios, demonstrate efficacy proposed method.

Язык: Английский

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Enhancing Air Pollution Forecasting with LSTM and a Binary Chimp Optimization Algorithm

SSRN Electronic Journal, Год журнала: 2024, Номер unknown

Опубликована: Янв. 1, 2024

Язык: Английский

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A Roadmap Towards Optimal Resource Allocation Approaches in the Internet of Things

International Journal of Advanced Computer Science and Applications, Год журнала: 2023, Номер 14(6)

Опубликована: Янв. 1, 2023

Introducing new technologies has facilitated people's lives more than ever. As one of these emerging technologies, the Internet Things (IoT) enables objects we handle daily to interact with each other or humans and exchange information through by being equipped sensors communication technologies. IoT turns physical world into a virtual where heterogeneous devices can be interconnected controlled. IoT-based networks face numerous challenges, including energy sensor transmission limitations. New are needed spread platform, optimize costs, cover connections, reduce power consumption, diminish delays. Users systems typically use services that integrated networks. Service providers provide users on-demand services. The interrelationship between this request response must managed in way is done using resource allocation strategy. Therefore, plays major role resources involves matters such as how much, where, when available should provided user economically. environment also subject various maintaining quality service, achieving predetermined level storing power, controlling congestion, reducing costs. problem an NP-Hard one, many research efforts have been conducted on topic, algorithms developed. This paper reviews published publications allocation, outlining underlying principles, latest developments, current trends.

Язык: Английский

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Inverted Ant Colony Optimization Algorithm for Data Replication in Cloud Computing

International Journal of Advanced Computer Science and Applications, Год журнала: 2023, Номер 14(7)

Опубликована: Янв. 1, 2023

Data replication is crucial in enhancing data availability and reducing access latency cloud computing. This paper presents a dynamic duplicate management method for storage systems based on the Inverted Ant Colony Optimization (IACO) algorithm fuzzy logic system. The proposed approach optimizes decisions focusing energy consumption, response time, cost. Extensive simulations demonstrate that IACO-based outperforms existing techniques, achieving remarkable 25% reduction significant 15% improvement substantial 20% cost reduction. By addressing research gap concerning integrating IACO replication, our work contributes to advancing computing solutions large datasets. offers viable efficient improve resource utilization system performance, benefiting various scientific fields.

Язык: Английский

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Cloud Task Scheduling using Particle Swarm Optimization and Capuchin Search Algorithms

International Journal of Advanced Computer Science and Applications, Год журнала: 2023, Номер 14(7)

Опубликована: Янв. 1, 2023

Cloud providers offer heterogeneous virtual machines for the execution of a variety tasks requested by users. These are managed cloud provider, eliminating need users to set up and maintain their hardware. This makes accessing computing resources necessary run applications services more accessible cost-effective. The task scheduling problem can be expressed as discrete optimization issue known NP-hard. To address this problem, we propose hybrid meta-heuristic algorithm using Capuchin Search Algorithm (CapSA) Particle Swarm Optimization (PSO) algorithm. PSO excels in global exploration, while CapSA is adept at fine-tuning solutions through local search. We aim achieve better convergence solution quality integrating both algorithms. Our proposed method's performance thoroughly evaluated extensive experimentation, comparing it standalone approaches. findings reveal that our outperforms individual techniques terms total time cost metrics. novelty work lies synergistic integration CapSA, addressing limitations traditional methods scheduling. approach opens intriguing directions future research dynamic scheduling, multi-objective optimization, adaptive algorithms, with emerging technologies, real-world deployment scenarios.

Язык: Английский

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QoS and Energy-aware Resource Allocation in Cloud Computing Data Centers using Particle Swarm Optimization Algorithm and Fuzzy Logic System

International Journal of Advanced Computer Science and Applications, Год журнала: 2023, Номер 14(10)

Опубликована: Янв. 1, 2023

Cloud computing has become a viable option for many organizations due to its flexibility and scalability in providing virtualized resources via the Internet. It offers possibility of hosting pervasive applications consumer, scientific, business domains utilizing pay-as-you-go model. This makes cloud cost-effective solution businesses as it eliminates need large investments hardware software infrastructure. Furthermore, enables quickly easily scale their services meet demands customers. Resource allocation is major challenge computing. known NP-hard problem can be solved using meth-heuristic algorithms. study optimizes resource Particle Swarm Optimization (PSO) algorithm fuzzy logic system developed under proposed time cost models environment. Receiving, processing, waiting are included The model incorporates processing receiving costs. Two experiments demonstrate performance algorithm. simulation results potential our mechanism, demonstrating improved over previous approaches aspects such providers' total income, users' revenue, utilization, energy consumption.

Язык: Английский

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Deep learning method to optimize the quality of services in UAV-aided 5G/6G networks

Wireless Networks, Год журнала: 2023, Номер 30(8), С. 7003 - 7013

Опубликована: Ноя. 20, 2023

Язык: Английский

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Nature-Inspired Optimization for Virtual Machine Allocation in Cloud Computing: Current Methods and Future Directions

International Journal of Advanced Computer Science and Applications, Год журнала: 2023, Номер 14(11)

Опубликована: Янв. 1, 2023

An expanding range of services is offered by cloud data centers. The execution application tasks facilitated assigning (VMs) Virtual Machines to (PMs) Physical Machines. Speaking VM allocation in the service center, two key factors are taken into consideration: quality (QoS) and energy consumption. center aims optimize these aspects while allocating VMs. On other hand, users have their priorities focus on specific requirements, particularly throughput reliability. User requirements considered resulting that meets QoS targets optimizes Cloud centers must, therefore, find a balance between efficiency considering user's requirements. To achieve this, various optimization algorithms techniques must be employed. objective best VMs PMs. Due NP-hardness problem, nature-inspired meta-heuristic become commonly used solve it. However, there no comprehensive in-depth review papers this area. This paper bridge knowledge gap providing an understanding significance metaheuristic methods address issue effectively. It not only highlights role played but also examines existing methods, provides comparisons strategies based parameters, concludes with valuable recommendations for future research.

Язык: Английский

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