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The paper analyzes the cause of throughput degradation issue when accessing interaction-intensive files and presents an enhanced HDFS architecture along with an associated storage allocation algorithm that overcomes the performance degradation problem.
This model, by using of a double knowledge diffusion approach, has been specifically designed to face the inherent lack of learning evidences, computational cost and continuous training requirements and, therefore, overcomes the performance and convergence rates offered by other learning methodologies.
Our decentralized peer-to-peer approach overcomes the performance and capacity limitations of centralized data repositories.
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In the current article, we provide a formal proof of problem complexity and present a new optimization algorithm that overcomes the performance-limiting assumptions in [23, 43].
The approach, exploiting a first order Chebyshev polynomial expansion, overcomes the performances of previously proposed DDFS based on Taylor approximation.
Alike WSN, RSN has been recently considered to overcome the performance degradation of a single radar.
The latter allows to overcome the performance inconsistency that has been often observed in previous fusion algorithms.
Adaptive modulation is exploited to overcome the performance enhancement limitation in source rate control schemes employing fixed modulation.
Moreover, radar sensor networks (RSN) have recently been considered to overcome the performance degradation of a single radar.
The FRWCs are used to overcome the performance degradation in terms of packet loss probability due to only use LMWCs.
Thread-level parallelism over multiple cores can be used to overcome the performance degradation at low voltage.
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CEO of Professional Science Editing for Scientists @ prosciediting.com