Building Energy Management systems (BEMS) are becoming very popular, for providing net Zero Energy Buildings (nZEBs). Attachment of these systems through IoT technologies, enables the building owners as well as utilit...
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In view of the high-speed data transmission requirements of the new generation of power line carrier communication in distributed photovoltaic information access, data compression sensing is required to improve. In th...
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the development of various mobile applications, the Internet of things (IoT), or smart cities generates massive amounts of data. Cloud computing allows businesses to store, manage, and process data using a network of ...
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ISBN:
(纸本)9798350316971
the development of various mobile applications, the Internet of things (IoT), or smart cities generates massive amounts of data. Cloud computing allows businesses to store, manage, and process data using a network of remote servers hosted on the Internet. However, cloud computing essentially suffers from data security concerns and lack of transparency and trust. Decentralized storage has gained popularity due to fault tolerance, scalability, privacy, and security properties. However, the widespread adoption of decentralized storage is facing obstacles due to concerns regarding performance and reliability. the need to efficiently schedule tasks from a large and diverse pool of resources is a challenge. In this study, we introduce a robust distributed storage network (Robust-DSN) that leverages the Inter-Planetary File System (IPFS) for content-based data management, Galois Field Arithmetic-based Reed-Solomon encoding for file partitioning, and Particle Swarm Optimization (PSO) for system optimization and lightweight multithreading for concurrent execution of tasks.
Scheduling distributed machine learning pipelines in edge environments is a growing area of research as developers work to bring large, high-accuracy models to relatively low-powered devices. Edge environment dynamics...
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ISBN:
(纸本)9798350316971
Scheduling distributed machine learning pipelines in edge environments is a growing area of research as developers work to bring large, high-accuracy models to relatively low-powered devices. Edge environment dynamics, such as device availability and connectivity, make distributed scheduling a more challenging problem than in traditional cloud environments. Existing approaches usually require significant a priori knowledge of the environment and make assumptions about model availability, both of which are impractical in real edge deployments. We address this problem by proposing a simple and efficient reverse auction algorithm, where a device that wants to distribute a large machine learning workload requests bids from available resources in the environment to construct connected pipelines. We implement our reverse auction scheduling on an existing distributed machine learning pipeline framework and perform an empirical evaluation using a real distributed edge computing testbed. We prove that scheduling distributed pipelines without repeating devices is an NP-complete problem, but that finding good latency or throughput pipelines is tractable for fixed device orderings.
Brain-computer interfaces based on motor imagery (MI-BCI) enable communication between the brain and the outside world. It accomplishes this by capturing and examining the EEG data generated when a person visualizes m...
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As a recently proposed network architecture, the computing power network (CPN) combines the ability of end, edge, cloud computing, and transmission network to realize the flexible and efficient scheduling and transact...
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the Internet of Vehicles realizes information sharing through the interconnection between entities such as vehicles and vehicles, vehicles and roads. As the computing tasks of vehicles become more and more complex, th...
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Cloud and big data applications increasingly rely on the softwarization of underlying infrastructure to achieve highly flexible reconfiguration. Software Defined networking (SDN) stands as a vital component, offering ...
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Fog computing composes of neighboring devices, which are connected as cluster to collect and compute data via given algorithms. Compared with cloud computing, it collects and processes data at the edge of device layer...
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the maritime sector is an industry that faces significant and various challenges related to cyber security and data management, such as fraud and user authentication. therefore, there is a need for a secure solution t...
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