Motivated by the need to reduce vendor lock-in and address concerns regarding dedicated hardware availability, cloud applications have increasingly adopted a multi-cloud deployment strategy, in which cloud applications are deployed in different zones associated with various cloud service providers. When deploying cloud applications in multi-clouds, there are three crucial and coupled metrics:deployment cost,access delayandtraffic demand. Unfortunately, existing works overlook either the data transfer cost in deployment cost or the access delay and traffic demand requirements, resulting in high operating costs or low user QoS. To bridge this gap, this paper proposes theCost-EfficientApplicationDeployment Framework (CADER) with tenant requirement guarantee in multi-clouds environment. However, due to the challenges of service price heterogeneity, transfer cost diversity, and resource limitation, achieving cost-efficient cloud application deployment while satisfying all tenant requirements is not an easy task. To tackle this issue, we design an approximate algorithm based on the random rounding method and prove that its approximate ratio is$O(\log g)$, where$g$is the number of cloud zones. Results of in-depth simulations indicate that CADER can reduce the application deployment cost ranging from 16% to 38% compared to commonly used alternatives while ensuring the satisfaction of tenant requirements.

Service function chain (SFC) consists of multiple ordered network functions (e.g., firewall, load balancer) and plays an important role in improving network security and ensuring network performance. Offloading SFCs onto programmable switches can bring significant performance improvement, but it suffers from unbearable reconfiguration delays, making it hard to cope with network workload dynamics in a timely manner. To bridge the gap, this paper presents OptRec, an efficient SFC proactive reconfiguration optimization framework based on deep reinforcement learning (DRL). OptRec predicts future traffic and places SFCs on programmable switches in advance to ensure the timeliness of the SFC reconfiguration, which is a proactive approach. However, it is non-trivial to extract effective features from historical traffic information and global network states, while ensuring efficient and stable model training. To this end, OptRec introduces a multi-level feature extraction model for different types of features. Additionally, it combines reinforcement learning and autoregressive learning to enhance model efficiency and stability. Results of in-depth simulations based on real-world datasets show the average prediction error of OptRec is less than 3 can increase the system throughput by up to 69.6 compared with other alternatives.

Service function chain (SFC) consists of multiple ordered network functions (e.g., firewall, load balancer) and plays an important role in improving network security and ensuring network performance. Offloading SFCs onto programmable switches can bring significant performance improvement, but it suffers from unbearable reconfiguration delays, making it hard to cope with network workload dynamics in a timely manner. To bridge the gap, this paper presents OptRec, an efficient SFC reconfiguration optimization framework based on deep reinforcement learning (DRL). OptRec predicts future traffic and places SFCs on programmable switches in advance to ensure the timeliness of the SFC reconfiguration, which is a proactive approach. However, it is non-trivial to extract effective features from historical traffic information and ensure efficient and stable model training. To this end, OptRec introduces a multi-level feature extraction model for different types of features. Additionally, it combines reinforcement learning and autoregressive learning to enhance model efficiency and stability. Results of in-depth simulations based on real-world datasets show the average prediction error of OptRec is less than 3% and OptRec can increase the system throughput by up to 69.6%~72.6% compared with other alternatives.

Autoencoders (AEs) have demonstrated excellent performance in the field of hyperspectral unmixing (SU), due to their self-supervised nature and ease of implementation. Recently proposed AE-based networks contend that local spatial information limits further improvement in unmixing accuracy and tends to explore and utilize global information, which improves unmixing accuracy at the expense of increased computational complexity. However, we believe that precise unmixing can be achieved by fully leveraging local information. In this article, we propose a unidirectional local-attention AE network (ULA-Net) that explores spatial information pixel by pixel and achieves accurate spatial–spectral feature fusion. ULA-Net utilizes unidirectional local attention (ULA) module to calculate the correlation between neighboring pixels and the central pixel within local regions, extracting discriminative local information. Moreover, ULA-Net effectively extracts relevant spatial information and suppresses irrelevant information based on a double fusion strategy (DFS) module. This process achieves more accurate control over the contribution of spatial information by implementing information fusion in both the pixel and feature dimensions. To address spectral variability, we implement the extended linear mixing model (ELMM) in the decoder part to improve unmixing accuracy without increasing the number of parameters. We conduct ablation experiments to investigate the roles of each module. Experimental results on both synthetic and real datasets demonstrate the effectiveness of the proposed network.

Band selection (BS) is an effective approach to alleviate the spectral redundancy of a hyperspectral image (HSI). The emerging deep-learning-based BS methods have become a hot topic due to their ability to model nonlinear relationships between spectral bands. However, existing deep-learning-based BS methods fail to accurately extract the representativeness of each band as a result of the limitation of interpretation networks. Moreover, existing deep-learning methods cannot fully utilize the interband correlation and the spatial information of HSIs for BS. To solve these issues, in this letter, we propose a novel Transformer reconstruction network for unsupervised BS, termed BSFormer. Specifically, the Transformer reconstruction network, which contributes to leveraging the spectral–spatial information of the HSI, consists of a Transformer-based band attention (TBA) module and a convolutional autoencoder (CAE)-based reconstruction module. On this basis, we design a novel band evaluation criterion composed of representative metric and redundancy metric, which are interpreted with the help of the multihead self-attention layer in the TBA module. The designed criterion can fully use the band representativeness and interband correlation for BS. Experimental results on three well-known hyperspectral datasets verify that the proposed BSFormer can yield better classification performance than the competitors.

Spatial information can play a supporting role in spectral unmixing. In this letter, we propose a dual branch autoencoder network to incorporate spatial-contextual information for spectral-spatial unmixing. The two branches leverage different architectures to efficiently extract spatial information and spectral information. In the first branch, we use fully connected layers to extract spectral information, where the neuron in each layer can capture all spectral features. In the second branch, 2-D convolution is adopted to exploit spatial features, which does not require hand-crafted assumptions compared with conventional methods. Then the extracted features are concatenated and propagated to generate the abundance and reconstruct the pixel. Moreover, to solve the drawbacks of the existing reconstruction functions, we propose a new function termed squared sine distance to improve the convergence quality of the proposed network. Experimental results reveal the effectiveness of our proposed method on both synthetic data and real-world data.

Band selection (BS) is an effective means to solve the problems of spectral redundancy and Hughes phenomenon in hyperspectral images (HSIs). However, existing BS methods fail to take into account the representativeness, redundancy, and information content of the selected bands simultaneously, and most of them lack consideration of the inherent nonlinear relationship between bands. To address these problems, we propose a novel unsupervised BS framework that can comprehensively consider band representativeness, redundancy, and information content (RRI) in this letter. The band representativeness is estimated by a three-dimensional convolutional autoencoder, which can capture the inherent nonlinear relationship between the bands and leverage the spatial information of the HSI. The redundancy and the information content of a band subset are restricted and enhanced by the correlation coefficient and the information divergence, respectively. Subsequently, RRI combines these three indicators as the subset evaluation criterion and utilizes immune clone selection algorithm to search for the desired band subset. Experimental results verify that the proposed RRI method can provide higher classification accuracy than the competitors and is robust to noisy bands.

Autoencoder is an efficient technique for unsupervised feature learning, which can be applied to hyperspectral unmixing. In this letter, we present an autoencoder network with adaptive abundance smoothing (AAS) to solve the challenges of previous techniques. Specifically, the proposed method uses a multilayer encoder to obtain the abundance and a single-layer decoder to reconstruct the image. The AAS algorithm tackles the outliers by exploiting the spatial-contextual information and can be adaptive for each pixel. Moreover, the softmax function is used as the encoder output function with the help of L1/2regularization to produce sparse output. Experimental results of the synthetic and real data reveal the superior performance of the proposed method against other competitors.

Spectral unmixing is a fundamental issue that needs to be addressed in the application of hyperspectral images. Due to the complex imaging conditions in remote sensing, it is common for the same object to have different spectral signatures. In this paper, we present a novel endmember bundle extraction method based on pixel purity index and superpixel segmentation to deal with this problem, where each material is represented by a set of similar endmember spectra. This method improves the accuracy of endmember extraction and focuses on the removal of redundant endmembers, leading to less spectral unmixing error than existing endmember bundle extraction algorithms. The experimental results on both synthetic dataset and real dataset demonstrate that the proposed method performs effectively in extracting variable endmember sets.