The efficient scheduling of tasks on virtual machines (VMs) is paramount in cloud computing environments. The complexity and dynamism of today's applications require a more insightful and adaptive approach to task...
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(纸本)9798331300579
The efficient scheduling of tasks on virtual machines (VMs) is paramount in cloud computing environments. The complexity and dynamism of today's applications require a more insightful and adaptive approach to task allocation to ensure optimal resource utilization and service delivery. Traditional scheduling approaches often fall short when it comes to considering the multi-dimensional attributes of tasks and VMs, such as makespan, deadline, memory, and bandwidth requirements. These methodologies lack the ability to dynamically adapt to the ever-evolving requirements of tasks and the capacities of VMs, leading to suboptimal performance and resource wastage. In this paper, we present a novel approach that fuses BiLSTM & BiGRU with Exponential Smoothing Recurrent Neural Network (ES-RNN) to create a more robust and adaptive task scheduling mechanism under real-time scenarios. This model holistically assesses task capacity based on its makespan, deadline, memory, and bandwidth requirements. Similarly, VM capacity is evaluated based on its RAM, MIPS, bandwidth, and the number of processing elements. The fusion of these advanced neural architectures provides a deeper understanding of the task-VM mapping, enabling a more intelligent and efficient scheduling decision. Our approach demonstrates a marked improvement over traditional techniques, with tangible benefits such as reduced makespan by 4.9% and improved VM computation efficiency by 3.5%. The practical implications of our methodology are profound. By integrating our model into real-world cloud environments, organizations can expect to see an enhanced deadline hit ratio by 1.5%, ensuring that critical tasks meet their time-sensitive objectives. Moreover, the decision-making process becomes significantly more agile, resulting in a decision delay reduction of 4.5%, thereby promoting more responsive and efficient cloud computing operations. This work paves the way for a new era of intelligent cloud resource management, opt
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Stock market Prediction has been a topic of attention for numerous researchers since its beginning. Often traditional statistical methods get conflict to grab the complex, non-linear patterns in stock market data. Due...
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For computers to understand human activity or behavior in a variety of scenarios, reliable 3D human posture estimation is a prerequisite. Several difficulties have made such work more complex as it is influenced by va...
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Deep neural networks are gaining importance and popularity in applications and *** to the enormous number of learnable parameters and datasets,the training of neural networks is computationally *** and distributed com...
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Deep neural networks are gaining importance and popularity in applications and *** to the enormous number of learnable parameters and datasets,the training of neural networks is computationally *** and distributed computation-based strategies are used to accelerate this training *** Adversarial Networks(GAN)are a recent technological achievement in deep *** generative models are computationally expensive because a GAN consists of two neural networks and trains on enormous ***,a GAN is trained on a single *** deep learning accelerator designs are challenged by the unique properties of GAN,like the enormous computation stages with non-traditional convolution *** work addresses the issue of distributing GANs so that they can train on datasets distributed over many TPUs(Tensor Processing Unit).Distributed learning training accelerates the learning process and decreases computation *** this paper,the Generative Adversarial Network is accelerated using the distributed multi-core TPU in distributed data-parallel synchronous *** adequate acceleration of the GAN network,the data parallel SGD(Stochastic Gradient Descent)model is implemented in multi-core TPU using distributed TensorFlow with mixed precision,bfloat16,and XLA(Accelerated Linear Algebra).The study was conducted on the MNIST dataset for varying batch sizes from 64 to 512 for 30 epochs in distributed SGD in TPU v3 with 128×128 systolic *** extensive batch technique is implemented in bfloat16 to decrease the storage cost and speed up floating-point *** accelerated learning curve for the generator and discriminator network is *** training time was reduced by 79%by varying the batch size from 64 to 512 in multi-core TPU.
Online reviews significantly influence decision-making in many aspects of *** integrity of internet evaluations is crucial for both consumers and *** concern necessitates the development of effective fake review detec...
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Online reviews significantly influence decision-making in many aspects of *** integrity of internet evaluations is crucial for both consumers and *** concern necessitates the development of effective fake review detection *** goal of this study is to identify fraudulent text reviews.A comparison is made on shill reviews *** reviews over sentiment and readability features using semi-supervised language processing methods with a labeled and balanced Deceptive Opinion *** analyze textual features accessible in internet reviews by merging sentiment mining approaches with ***,the research improves fake review screening by using various transformer models such as Bidirectional Encoder Representation from Transformers(BERT),Robustly Optimized BERT(Roberta),XLNET(Transformer-XL)and XLM-Roberta(Cross-lingual Language model–Roberta).This proposed research extracts and classifies features from product reviews to increase the effectiveness of review *** evidenced by the investigation,the application of transformer models improves the performance of spam review filtering when related to existing machine learning and deep learning models.
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