Currently,applications accessing remote computing resources through cloud data centers is the main mode of operation,but this mode of operation greatly increases communication latency and reduces overall quality of se...
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Currently,applications accessing remote computing resources through cloud data centers is the main mode of operation,but this mode of operation greatly increases communication latency and reduces overall quality of service(QoS)and quality of experience(QoE).Edge computing technology extends cloud service functionality to the edge of the mobile network,closer to the task execution end,and can effectivelymitigate the communication latency ***,the massive and heterogeneous nature of servers in edge computing systems brings new challenges to task scheduling and resource management,and the booming development of artificial neural networks provides us withmore powerfulmethods to alleviate this ***,in this paper,we proposed a time series forecasting model incorporating Conv1D,LSTM and GRU for edge computing device resource scheduling,trained and tested the forecasting model using a small self-built dataset,and achieved competitive experimental results.
The extensive spread of DeepFake images on the internet has emerged as a significant challenge, with applications ranging from harmless entertainment to harmful acts like blackmail, misinformation, and spreading false...
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Several newly developed techniques and tools for manipulating images, audio, and videos have been introduced as an outcome of the recent and rapid breakthroughs in AI, machine learning, and deep learning. While most a...
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Data security and user privacy have become crucial elements in multi-tenant data *** traffic types in the multi-tenant data center in the cloud environment have their characteristics and *** the data center network(DC...
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Data security and user privacy have become crucial elements in multi-tenant data *** traffic types in the multi-tenant data center in the cloud environment have their characteristics and *** the data center network(DCN),short and long flows are sensitive to low latency and high throughput,*** traditional security processing approaches,however,neglect these characteristics and *** paper proposes a fine-grained security enhancement mechanism(SEM)to solve the problem of heterogeneous traffic and reduce the traffic completion time(FCT)of short flows while ensuring the security of multi-tenant traffic ***,for short flows in DCN,the lightweight GIFT encryption method is *** Intra-DCN long flows and Inter-DCN traffic,the asymmetric elliptic curve encryption algorithm(ECC)is *** NS-3 simulation results demonstrate that SEM dramatically reduces the FCT of short flows by 70%compared to several conventional encryption techniques,effectively enhancing the security and anti-attack of traffic transmission between DCNs in cloud computing ***,SEM performs better than other encryption methods under high load and in largescale cloud environments.
In this paper, we employ an unmanned aerial vehicle (UAV) to ensure the freshness of sensing data, as measured by the age of information (AoI), in Internet of Things (IoT) networks. Specifically, the UAV switches betw...
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In the contemporary world, humanoid robots are likely to play a key role in various fields, including health care, domestic service, hospitality, business, and military and security activities. The robots are employed...
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Log anomaly detection is an important paradigm for system *** log anomaly detection based on Long Short-Term Memory(LSTM)networks is time-consuming to handle long *** model is introduced to promote ***,most existing T...
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Log anomaly detection is an important paradigm for system *** log anomaly detection based on Long Short-Term Memory(LSTM)networks is time-consuming to handle long *** model is introduced to promote ***,most existing Transformer-based log anomaly detection methods convert unstructured log messages into structured templates by log parsing,which introduces parsing *** only extract simple semantic feature,which ignores other features,and are generally supervised,relying on the amount of labeled *** overcome the limitations of existing methods,this paper proposes a novel unsupervised log anomaly detection method based on multi-feature(UMFLog).UMFLog includes two sub-models to consider two kinds of features:semantic feature and statistical feature,*** applies the log original content with detailed parameters instead of templates or template IDs to avoid log parsing *** the first sub-model,UMFLog uses Bidirectional Encoder Representations from Transformers(BERT)instead of random initialization to extract effective semantic feature,and an unsupervised hypersphere-based Transformer model to learn compact log sequence representations and obtain anomaly *** the second sub-model,UMFLog exploits a statistical feature-based Variational Autoencoder(VAE)about word occurrence times to identify the final anomaly from anomaly *** experiments and evaluations are conducted on three real public log *** results show that UMFLog significantly improves F1-scores compared to the state-of-the-art(SOTA)methods because of the multi-feature.
The need for a personalized user experience brought recommendation systems to the forefront of digital innovation. However, traditional approaches tend to often forget human emotions, which represent a critical driver...
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Cloud computing involves accessing and using computing resources, such as servers, storage, and software applications, over the Internet, enabling scalable access on demand. Cloud computing systems are becoming an ess...
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The automatic identification o f h eart d iseases is a challenging problem in the medical field, a nd i t constitutes the primary cause of death. Heart failure prediction, one of the symptoms of cardiovascular disease...
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