Using a machine learning (ML) model as a core component in database knob tuning has demonstrated remarkable advancements in recent years. However, a model that optimizes both categorical and continuous values in the same way may not guarantee efficiency and effectiveness in knob tuning. This is due to the fact that the usual assumption of a differentiable input space for efficient exploration of continuous spaces does not hold true in categorical spaces. Moreover, the inherent complexity of interdependences among knobs and the high-dimensionality of the configuration space compound the challenges of tuning. In this paper, we propose TopTune, which employs tailored optimization for continuous and categorical knobs, to achieve accelerated tuning efficiency and improved tuning performance. Specifically, we decompose the configuration space into two orthogonal subspaces: categorical and continuous spaces. Subsequently, we employ Bayesian optimization models, i.e., SMAC and GP to explore the categorical and continuous subspaces, respectively. These two models will alternately explore the two spaces with the proposed communication mechanism to ensure TopTune can capture the dependence between continuous and categorical knobs. Furthermore, to balance efficiency and accuracy, we utilize a knob-dimensional projection strategy to reduce the exploration domain by embedding the high-dimension configuration space into a lower-dimensional proxy space. In addition, we implement batch Bayesian optimization technology, which enables parallel knob evaluation while balancing exploration and exploitation. We evaluate TopTune under different benchmarks (SYSBENCH, TPC-C, and JOB), metrics (throughput and latency), and DBMSs (MySQL and Dameng). Extensive experiments demonstrate that TopTune identifies better configurations in up to approximately 12.2× less time while achieving a 10.7% improvement in throughput compared to state-of-the-art methods.

Abstract Hashing-based unsupervised cross-modal retrieval has gained significant attention in the big data management community due to its low storage overhead and rapid retrieval speed. However, current methods often lack effective alignment strategies to reduce the modality gap. They also fail to explore the latent structural information of the training data for accurate relationship learning, resulting in sub-optimal cross-modal retrieval performance. To tackle these challenges, we propose a novel unsupervised cross-modal hashing method called G raph C ontrastive-and- R econstructive H ashing ( GCRH ). Specifically, GCRH first performs global graph contrastive learning , which involves both intra-modal and inter-modal pairs. This facilitates the learning of more discriminative hash codes through intra-modal discrimination and inter-modal alignment objectives. To further bridge the modality gap, GCRH conducts local graph reconstruction using GCN-based decoders to reconstruct the original features of one modality from the hash codes of another. The integration of contrastive-and-reconstructive learning with graph structural information enables GCRH to generate high-quality hash codes that are both well-aligned and discriminative. Extensive experiments on three benchmark datasets substantiate the superior cross-modal retrieval performance of GCRH .

Workload execution can account for 90% of the total database knob tuning time, which is often the bottleneck for efficient knob tuning in practice. Reducing the tuning time by using a compressed workload is a natural solution. However, many existing workload compression methods are designed for OLAP workloads, which reduce the number of queries needed for analysis tasks by sampling a small subset of queries. These methods are less effective for OLTP workloads in knob-tuning tasks, as they often disregard essential contextual details, including query sequence and concurrency. As a result, configurations that perform well on the compressed OLTP workload may not deliver similar competitive performance on the original workload. To address these challenges, we first define the objective of OLTP workload compression for knob tuning. We then propose a slice-based compression method, SCompression , which compresses workloads by slicing based on time intervals while preserving concurrency. SCompression achieves the objective by focusing on generating a compressed workload that (1) executes faster than the original workload and (2) produces performance variations similar to the source workload under different configurations. SCompression works in three steps: (1) dividing the workload into segments to capture regular performance fluctuations, (2) slicing each segment to preserve concurrency and transaction context, and (3) sampling slices under execution time constraints using a cluster-based approach to ensure representativeness. Finally, SCompression replays the compressed workload to produce the performance that mirrors the source workload. Extensive experiments on real-world and benchmark OLTP workloads show that SCompression is a cost-effective solution for knob tuning, accelerating tuning by up to 40× with only a 5% performance reduction.

Meta-learning excels in few-shot learning by extracting shared knowledge from the observed tasks. However, it needs the tasks to adhere to the i.i.d. constraint, which is challenging to achieve due to complex task relationships between data content. Current methods that create tasks in a one-dimensional structure and use meta-learning to learn all tasks flatly struggle with extracting shared knowledge from tasks with overlapping concepts. To address this issue, we propose further constructing tasks from the same environment into hyper-tasks. Since the distributions of hyper-tasks and tasks in a hyper-task can both be approximated as i.i.d. due to further summarization, the meta-learning algorithm can capture shared knowledge more efficiently. Based on the hyper-task, we propose a hierarchical meta-learning paradigm to meta-learn the meta-learning algorithm. The paradigm builds a customized meta-learner for each hyper-task, which makes meta-learners more flexible and expressive. We apply the paradigm to three classic meta-learning algorithms and conduct extensive experiments on public datasets, which confirm the superiority of hierarchical meta-learning in the few-shot learning setting. The code is released at https://github.com/tuantuange/H-meta-learning.

Recently, using machine learning for performance tuning of cloud database (CDB) service has shown great potentials. However, facing personalized requirements such as various restrictions for tuning with very different workloads, pre-trained models may mismatch or recommend suboptimal configurations given a new workload. On the other hand, if the system tunes configurations in an online fashion, the system will suffer from the cold start problem, resulting in long tuning time and performance fluctuation. To accommodate these problems, we propose an online CDB tuning system called HUNTER. The key feature of HUNTER is a hybrid architecture, which uses samples generated by Genetic Algorithm to warm-start the finer grained exploration of deep reinforcement learning. Meanwhile, we employ Principal Component Analysis, Random Forest, and Fast Exploration Strategy to reduce the search space and the update time of the learning model. In addition, we further propose a clone and parallelization scheme to stress-test workloads on multiple cloned CDB instances (CDBs), resulting in faster and safer configuration exploration. Extensive trials on CDB with public and real-world workloads demonstrate that, given the same time budget and resources, HUNTER improves performance and considerably decreases recommendation time compared to state-of-the-art tuning systems, with accelerations of up to 2.8× and 22.8× utilizing 1 and 20 cloned CDBs, respectively.

In most multi-label image recognition tasks, human visual perception keeps consistent for different spatial transforms of the same image. Existing approaches either learn the perceptual consistency with only image-level supervision or preserve the middle-level feature consistency of attention regions but neglect the (global) label dependencies between different objects over the dataset. To address this issue, we integrate graph convolution network (GCN) and propose G-CAM, which learns visual attention consistency via GCN based class attention mapping (CAM) for multi-label image recognition. G-CAM consists of an image feature extraction module to generate the feature maps of the original image and its transformed one and a GCN module to learn weighted classifiers that capture the label dependencies between different objects. Different from previous works which use fully-connected classification layer, G-CAM first fuses weighted classifiers with the feature vector to generate the predicted labels for each input image, then combines weighted classifiers with the feature maps to respectively obtain the transformed attention heatmaps of the original image and the attention heatmaps of its transformed one. We can compute the attention consistency loss according to the distance between these two attention heatmaps. Finally, this loss is combined with the multi-label classification loss to update the whole network in an end-to-end manner. We conduct extensive experiments on three multi-label image datasets including FLICKR25K, MS-COCO and NUS-WIDE. Experimental results demonstrate G-CAM can achieve better performance compared with the state-of-the-art multi-label image recognition methods.

Abstract Configuration tuning is vital to optimize the performance of a database management system (DBMS). It becomes more tedious and urgent for cloud databases (CDB) due to diverse database instances and query workloads, which make the job of a database administrator (DBA) very difficult. Existing solutions for automatic DBMS configuration tuning have several limitations. Firstly, they adopt a pipelined learning model but cannot optimize the overall performance in an end-to-end manner. Secondly, they rely on large-scale high-quality training samples which are hard to obtain. Thirdly, existing approaches cannot recommend reasonable configurations for a large number of knobs to tune whose potential values live in such high-dimensional continuous space. Lastly, in cloud environments, existing approaches can hardly cope with the changes of hardware configurations and workloads, and have poor adaptability. To address these challenges, we design an end-to-end automatic CDB tuning system, $${\texttt {CDBTune}}^{+}$$ CDBTune + , using deep reinforcement learning (RL). $${\texttt {CDBTune}}^{+}$$ CDBTune + utilizes the deep deterministic policy gradient method to find the optimal configurations in a high-dimensional continuous space. $${\texttt {CDBTune}}^{+}$$ CDBTune + adopts a trial-and-error strategy to learn knob settings with a limited number of samples to accomplish the initial training, which alleviates the necessity of collecting a massive amount of high-quality samples. $${\texttt {CDBTune}}^{+}$$ CDBTune + adopts the reward-feedback mechanism in RL instead of traditional regression, which enables end-to-end learning and accelerates the convergence speed of our model and improves the efficiency of online tuning. Besides, we propose effective techniques to improve the training and tuning efficiency of $${\texttt {CDBTune}}^{+}$$ CDBTune + for practical usage in a cloud environment. We conducted extensive experiments under 7 different workloads on real cloud databases to evaluate $${\texttt {CDBTune}}^{+}$$ CDBTune + . Experimental results showed that $${\texttt {CDBTune}}^{+}$$ CDBTune + adapts well to a new hardware environment or workload, and significantly outperformed the state-of-the-art tuning tools and DBA experts.

In multi-label image recognition, it has become a popular method to predict those labels that co-occur in an image via modeling the label dependencies. Previous works focus on capturing the correlation between labels, but neglect to effectively fuse the image features and label embeddings, which severely affects the convergence efficiency of the model and inhibits the further precision improvement of multi-label image recognition. To overcome this shortcoming, in this paper, we introduce Multi-modal Factorized Bilinear pooling (MFB) which works as an efficient component to fuse cross-modal embeddings and propose F-GCN, a fast graph convolution network (GCN) based multi-label image recognition model. F-GCN consists of three key modules: (1) an image representation learning module which adopts a convolution neural network (CNN) to learn and generate image representations, (2) a label co-occurrence embedding module which first obtains the label vectors via the word embeddings technique and then adopts GCN to capture label co-occurrence embeddings and (3) an MFB fusion module which efficiently fuses these cross-modal vectors to enable an end-to-end model with a multi-label loss function. We conduct extensive experiments on two multi-label datasets including MS-COCO and VOC2007. Experimental results demonstrate the MFB component efficiently fuses image representations and label co-occurrence embeddings and thus greatly improves the convergence efficiency of the model. In addition, the performance of image recognition has also been promoted compared with the state-of-the-art methods.

Configuration tuning is vital to optimize the performance of database management system (DBMS). It becomes more tedious and urgent for cloud databases (CDB) due to the diverse database instances and query workloads, which make the database administrator (DBA) incompetent. Although there are some studies on automatic DBMS configuration tuning, they have several limitations. Firstly, they adopt a pipelined learning model but cannot optimize the overall performance in an end-to-end manner. Secondly, they rely on large-scale high-quality training samples which are hard to obtain. Thirdly, there are a large number of knobs that are in continuous space and have unseen dependencies, and they cannot recommend reasonable configurations in such high-dimensional continuous space. Lastly, in cloud environment, they can hardly cope with the changes of hardware configurations and workloads, and have poor adaptability. To address these challenges, we design an end-to-end automatic CDB tuning system, CDBTune, using deep reinforcement learning (RL). CDBTune utilizes the deep deterministic policy gradient method to find the optimal configurations in high-dimensional continuous space. CDBTune adopts a try-and-error strategy to learn knob settings with a limited number of samples to accomplish the initial training, which alleviates the difficulty of collecting massive high-quality samples. CDBTune adopts the reward-feedback mechanism in RL instead of traditional regression, which enables end-to-end learning and accelerates the convergence speed of our model and improves efficiency of online tuning. We conducted extensive experiments under 6 different workloads on real cloud databases to demonstrate the superiority of CDBTune. Experimental results showed that CDBTune had a good adaptability and significantly outperformed the state-of-the-art tuning tools and DBA experts.