Current IaaS providers have deployed data centers worldwide, with resources continually increasing. Meanwhile, there is a rising trend in the concurrency of user requests and the diversity of user request types. To ac...
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ISBN:
(纸本)9798350368543;9798350368536
Current IaaS providers have deployed data centers worldwide, with resources continually increasing. Meanwhile, there is a rising trend in the concurrency of user requests and the diversity of user request types. To achieve better resource allocation, various complex scheduling architectures have been proposed. However, due to the challenges associated with real-world experiments, simulation systems are needed to build experimental environments for related research. As existing systems do not perform well enough, we construct LGDCloudSim. It is designed with full consideration of the characteristics of the largescale geographically distributed cloud data center scenarios. To support large-scale simulations, we propose state management optimization and operation process optimization methods. Experiments show that LGDCloudSim can simulate up to 5x10(8) hosts and 107 request concurrency. It also supports diverse scheduling architectures and different request types.
An increasingly prominent issue in recent times is the utilization of the Internet of things (IoT) for home automation systems. Home automation, also known as smart home technology, refers to the wireless and intellig...
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the large-scale grid connection of distributed energy resources is an effective way to solve the problems of power supply shortage and environmental pollution, and there is no unified standard for the communication pr...
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Traditional farming methods are becoming incapable of keeping up with global population growth. As a result, innovative farming ideas are desperately necessary to meet the food needs of a growing population. Intellige...
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Withthe integration of large-scale distributed PV into the distribution network, the PV penetration rate is increasing, and the imbalance between the PV output and the load leads to the reverse current in the line, c...
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this paper provides a comprehensive examination of Green Communication systems, focusing on strategies, technologies, and practices aimed at minimizing energy consumption and environmental impact in communication netw...
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Withthe rise of big data, the planning and management of intelligent distribution networks have ushered in new development opportunities. through the comprehensive integration, in-depth analysis, and effective mining...
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Optimising routing in Wireless sensor Networks (WSNs) is crucial for enhancing their performance, with a focus on energy conservation, reliable data transmission, and network stability. this research presents a unique...
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the primary objective of this paper is to devise and execute sensor fusion employing the Extended Kalman Filter (EKF) for parameter estimation in nonlinear systems. sensor fusion tackles a data processing challenge wh...
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Federated Learning (FL) has emerged as a novel distributed Machine Learning (ML) approach, to tackle the challenges associated with data privacy and overload in ML-based intrusion detection systems (IDSs). Drawing ins...
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ISBN:
(纸本)9798350361261;9798350361278
Federated Learning (FL) has emerged as a novel distributed Machine Learning (ML) approach, to tackle the challenges associated with data privacy and overload in ML-based intrusion detection systems (IDSs). Drawing inspiration from the FL architecture, we have introduced a hybrid ML IDS tailored for Wireless sensor Networks (WSNs). this system is crafted to leverage ML for achieving a two-layer intrusion detection mechanism in WSNs free from constraints posed by specific attack types. the architecture follows a server-client model compatible withthe configuration of sensor nodes, sink nodes, and gateways in WSNs. In this setup, client models located at sink nodes undergo training using sensing data while the server model at the gateway is trained using network traffic data. this two-layer training approach amplifies the efficiency of intrusion detection and ensures comprehensive network coverage. the results derived from our simulation experiments corroborate the effectiveness of the proposed hybrid ML IDS. It generates precise aggregation predictions and leads to a substantial reduction in redundant data transmissions. Furthermore, the system exhibits efficacy in detecting intrusions through a dual validation process.
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