High-performance computing (HPC) systems are crucial for scientific advancement and engineering breakthroughs. Unexpected performance degradation or system failures can severely impact these endeavors. This paper introduces NodeSentry, a novel unsupervised anomaly detection framework tailored for compute nodes of large-scale HPC systems. NodeSentry leverages a combined approach of coarse-grained clustering and fine-grained model sharing to effectively address the challenges posed by the massive node scales, frequent job transitions, and complex patterns characteristic of modern HPC deployments. Evaluation on two real-world HPC datasets demonstrates NodeSentry’s superior performance, achieving an F1-score exceeding 0.876. This represents a 0.560 average improvement over existing best baseline methods, while simultaneously reducing training overhead by an average of 45.69%. Furthermore, to promote reproducibility and contribute to the broader research community, we open-source NodeSentry’s codebase and introduce a novel clustering adjustment and anomaly labeling tool specifically designed for HPC systems.

Geo-distributed computing, a paradigm that assigns computational tasks to globally distributed nodes, has emerged as a promising approach in cloud computing, edge computing, cloud-edge computing, and supercomputer computing (SC). It enables low-latency services, ensures data locality, and handles large-scale applications. As global computing capacity and task demands increase rapidly, scheduling tasks for efficient execution in geo-distributed computing systems has become an increasingly critical research challenge. It arises from the inherent characteristics of geographic distribution, including heterogeneous network conditions, region-specific resource pricing, and varying computational capabilities across locations. Researchers have developed diverse task scheduling methods tailored to geo-distributed scenarios, aiming to achieve objectives such as performance enhancement, fairness assurance, and fault-tolerance improvement. This survey provides a comprehensive and systematic review of task scheduling techniques across four major distributed computing environments, with an in-depth analysis of these approaches based on their core scheduling objectives. Through our analysis, we identify key research challenges and outline promising directions for advancing task scheduling in geo-distributed computing.

In recent years, many methods have been proposed to alleviate the biases in recommender systems by combining biased data and unbiased data. Among these methods, data imputation method is effective, but previous works only employ a straightforward model to generate imputed data, which can not fully characterize the data. In this paper, we propose a novel data imputation approach that combines an unbiased model and a debiasing model with adaptively learnt weights. We conduct extensive experiments on two public recommendation datasets and one production dataset to demonstrate the effectiveness and robustness of the proposed method.

In this paper, we present a framework to deal with the fraud detection task with extremely few labeled frauds. We involve human intelligence in the loop in a labor-saving manner and introduce several ingenious designs to the model construction process. Namely, a rule mining module is introduced, and the learned rules will be refined with expert knowledge. The refined rules will be used to relabel the unlabeled samples and get the potential frauds. We further present a model to learn with the reliable frauds, the potential frauds, and the rest normal samples. Note that the label noise problem, class imbalance problem, and confirmation bias problem are all addressed with specific strategies when building the model. Experimental results are reported to demonstrate the effectiveness of the framework.

Benefiting from the message passing mechanism, Graph Neural Networks (GNNs) have been successful on flourish tasks over graph data. However, recent studies have shown that attackers can catastrophically degrade the performance of GNNs by maliciously modifying the graph structure. A straightforward solution to remedy this issue is to model the edge weights by learning a metric function between pairwise representations of two end nodes, which attempts to assign low weights to adversarial edges. The existing methods use either raw features or representations learned by supervised GNNs to model the edge weights. However, both strategies are faced with some immediate problems: raw features cannot represent various properties of nodes (e.g., structure information), and representations learned by supervised GNN may suffer from the poor performance of the classifier on the poisoned graph. We need representations that carry both feature information and as mush correct structure information as possible and are insensitive to structural perturbations. To this end, we propose an unsupervised pipeline, named STABLE, to optimize the graph structure. Finally, we input the well-refined graph into a downstream classifier. For this part, we design an advanced GCN that significantly enhances the robustness of vanilla GCN [24] without increasing the time complexity. Extensive experiments on four real-world graph benchmarks demonstrate that STABLE outperforms the state-of-the-art methods and successfully defends against various attacks.

The outbreak of COVID-19 burgeons newborn services on online platforms and simultaneously buoys multifarious online fraud activities. Due to the rapid technological and commercial innovation that opens up an ever-expanding set of products, the insufficient labeling data renders existing supervised or semi-supervised fraud detection models ineffective in these emerging services. However, the ever accumulated user behavioral data on online platforms might be helpful in improving the performance of fraud detection on newborn services. To this end, in this paper, we propose to pre-train user behavior sequences, which consist of orderly arranged actions, from the large-scale unlabeled data sources for online fraud detection. Recent studies illustrate accurate extraction of user intentions~(formed by consecutive actions) in behavioral sequences can propel improvements in the performance of online fraud detection. By anatomizing the characteristic of online fraud activities, we devise a model named UB-PTM that learns knowledge of fraud activities by three agent tasks at different granularities, i.e., action, intention, and sequence levels, from large-scale unlabeled data. Extensive experiments on three downstream transaction and user-level online fraud detection tasks demonstrate that our UB-PTM is able to outperform the state-of-the-art designing for specific tasks.

Though Graph Neural Networks (GNNs) have been successful for fraud detection tasks, they suffer from imbalanced labels due to limited fraud compared to the overall userbase. This paper attempts to resolve this label-imbalance problem for GNNs by maximizing the AUC (Area Under ROC Curve) metric since it is unbiased with label distribution. However, maximizing AUC on GNN for fraud detection tasks is intractable due to the potential polluted topological structure caused by intentional noisy edges generated by fraudsters. To alleviate this problem, we propose to decouple the AUC maximization process on GNN into a classifier parameter searching and an edge pruning policy searching, respectively. We propose a model named AO-GNN (Short for AUC-oriented GNN), to achieve AUC maximization on GNN under the aforementioned framework. In the proposed model, an AUC-oriented stochastic gradient is applied for classifier parameter searching, and an AUC-oriented reinforcement learning module supervised by a surrogate reward of AUC is devised for edge pruning policy searching. Experiments on three real-world datasets demonstrate that the proposed AO-GNN patently outperforms state-of-the-art baselines in not only AUC but also other general metrics, e.g. F1-macro, G-means.

Online credit payment fraud detection plays a critical role in financial institutions due to the growing volume of fraudulent transactions. Recently, researchers have shown an increased interest in capturing users’ dynamic and evolving fraudulent tendencies from their behavior sequences. However, most existing methodologies for sequential modeling overlook the intrinsic structure information of web pages. In this paper, we adopt multi-scale behavior sequence generated from different granularities of web page structures and propose a model named SAH-RNN to consume the multi-scale behavior sequence for online payment fraud detection. The SAH-RNN has stacked RNN layers in which upper layers modeling for compendious behaviors are updated less frequently and receive the summarized representations from lower layers. A dual attention is devised to capture the impacts on both sequential information within the same sequence and structural information among different granularity of web pages. Experimental results on a large-scale real-world transaction dataset from Alibaba show that our proposed model outperforms state-of-the-art models. The code is available at https://github.com/WangliLin/SAH-RNN.

Fraud transactions have been the major threats to the healthy development of e-commerce platforms, which not only damage the user experience but also disrupt the orderly operation of the market. User behavioral data is widely used to detect fraud transactions, and recent works show that accurate modeling of user intentions in behavioral sequences can propel further improvements on the performances. However, most existing methods treat each transaction as an independent data instance without considering the transaction-level interactions accessed by transaction attributes, e.g., information on remark, logistics, payment, device and etc., which may fail to achieve satisfactory results in more complex scenarios. In this paper, a novel heterogeneous transaction-intention network is devised to leverage the cross-interaction information over transactions and intentions, which consists of two types of nodes, namely transaction and intention nodes, and two types of edges, i.e., transaction-intention and transaction-transaction edges. Then we propose a graph neural method coined IHGAT(Intention-aware Heterogeneous Graph ATtention networks) that not only perceives sequence-like intentions, but also encodes the relationship among transactions. Extensive experiments on a real-world dataset of Alibaba platform show that our proposed algorithm outperforms state-of-the-art methods in both offline and online modes.

Consumer lending service is escalating in E-Commerce platforms due to its capability in enhancing buyers' purchasing power, improving average order value, and increasing revenue of the platforms. Credit risk forecasting and credit limits setting are two fundamental problems in E-Commerce/online consumer lending services. Currently, the majority of institutes rely on two-separate-step methods to resolve. First, build a rating model to evaluate credit risk, and then design heuristic strategies to set credit limits, which requires a large amount of prior knowledge and lacks theoretical justifications. In this paper, we propose an end-to-end multi-view and multi-task learning based approach named MvMoE (Multi-view-aware Mixture-of-Experts network) to solve these two problems simultaneously. First, a multi-view network with a hierarchical attention mechanism is constructed to distill users' heterogeneous financial information into shared hidden representations. Then, we jointly train these two tasks with a view-aware multi-gate mixture-of-experts network and a subsequent progressive network to improve their performances. With the real-world dataset contained 5.44 million users, we investigate the effectiveness of MvMoE. Experimental results exhibit that the proposed model is able to improve AP over 5.60% on credit risk forecasting and MAE over 9.52% on credit limits setting compared with conventional methods. Meanwhile, MvMoE has good interpretability, which better underpins the imperative demands in financial industries.

Graph-based fraud detection approaches have escalated lots of attention recently due to the abundant relational information of graph-structured data, which may be beneficial for the detection of fraudsters. However, the GNN-based algorithms could fare poorly when the label distribution of nodes is heavily skewed, and it is common in sensitive areas such as financial fraud, etc. To remedy the class imbalance problem of graph-based fraud detection, we propose a Pick and Choose Graph Neural Network (PC-GNN for short) for imbalanced supervised learning on graphs. First, nodes and edges are picked with a devised label-balanced sampler to construct sub-graphs for mini-batch training. Next, for each node in the sub-graph, the neighbor candidates are chosen by a proposed neighborhood sampler. Finally, information from the selected neighbors and different relations are aggregated to obtain the final representation of a target node. Experiments on both benchmark and real-world graph-based fraud detection tasks demonstrate that PC-GNN apparently outperforms state-of-the-art baselines.

Financial credit risk assessment serves as the impetus to evaluate the credit admission or potential business failure of customers in order to make early actions prior to the actual financial crisis. It aims to predict the probability that a customer may belong to a high-risk group, which is usually formulated as a binary classification problem. However, due to the lack of high-risk samples, the prevailing models suffer from the severe class-imbalance problem. Oversampling those high-risk users could alleviate this problem but the effect of noise examples is also amplified. In this paper, we propose a novel adversarial data augmentation method to solve the class imbalance problem in financial credit risk assessment. We train a generator for synthetic sample generation with a discriminator to identify real or fake instances. Besides, an auxiliary risk discriminator is trained cooperatively with the generator to assess the credit risk. Experimental results on three real-world datasets demonstrate the effectiveness of the proposed

Credit risk forecasting generally aims to evaluate the default probability of users in financial service. It is usually regarded as a binary classification problem, which suffers from the severe class-imbalance problem due to the extremely limited throngs and the concept drift problem brought by the delayed verification. In this paper, we investigate these problems in credit risk forecasting and propose a semi-supervised meta-learning based approach called TRUST (TRainable Undersampling with Self Training) to resolve. First, it decides whether to sample the data through meta-learning based reinforcement learning. Secondly, it learns the distribution of the data that have not yet shown financial performance via self-training and updates the model trained in the first step. Finally, the updated model is evaluated on the validation dataset, the result of which will be fed back through the evaluator. These three steps will be iterated until the model converges. With the real-world industrial dataset containing 1.75 million users, we investigate the effectiveness of our method. Experimental results exhibit that the proposed method is able to improve AP over 5.94% on credit risk forecasting task compared with the recent methods.

Fraud transactions obtain the rights and interests of e-commerce platforms by illegal ways, and have been the emerging threats to the healthy development of these platforms. Recently, user behavioral data is extensively exploited to detect fraud transactions, and it is usually processed as a sequence consisting of individual actions. However, such sequence-like user behaviors have logical patterns associated with user intentions, which motivates a fine-grained management strategy that binds and cuts off these actions into intention-related segments. In this paper, we devise a tree-like structure named behavior tree to reorganize the user behavioral data, in which a group of successive sequential actions denoting a specific user intention are represented as a branch on the tree. We then propose a novel neural method coined LIC Tree-LSTM(Local Intention Calibrated Tree-LSTM) to utilize the behavior tree for fraud transactions detection. In our LIC Tree-LSTM, the global user intention is captured by an attentional method applied on different branches. Then, we calibrate the entire tree by attentions within tree branches to pinpoint the balance between global and local user intentions. We investigate the effectiveness of LIC Tree-LSTM on a real-world dataset of Alibaba platform, and the experimental results show that our proposed algorithm outperforms state-of-the-art methods in both offline and online modes. Furthermore, our model provides good interpretability which helps us better understand user behaviors.

Default user detection plays one of the backbones in credit risk forecasting and management. It aims at, given a set of corresponding features, e.g., patterns extracted from trading behaviors, predicting the polarity indicating whether a user will fail to make required payments in the future. Recent efforts attempted to incorporate attributed heterogeneous information network (AHIN) for extracting complex interactive features of users and achieved remarkable success on discovering specific default users such as fraud, cash-out users, etc. In this paper, we consider default users, a more general concept in credit risk, and propose a multi-view attributed heterogeneous information network based approach coined MAHINDER to remedy the special challenges. First, multiple views of user behaviors are adopted to learn personal profile due to the endogenous aspect of financial default. Second, local behavioral patterns are specifically modeled since financial default is adversarial and accumulated. With the real datasets contained 1.38 million users on Alibaba platform, we investigate the effectiveness of MAHINDER, and the experimental results exhibit the proposed approach is able to improve AUC over 2.8% and [email protected]=0.1 over 13.1% compared with the state-of-the-art methods. Meanwhile, MAHINDER has as good interpretability as tree-based methods like GBDT, which buoys the deployment in online platforms.

Understanding the I/O characteristics of supercomputers is crucial for grasping accurate I/O workloads and uncovering potential I/O inefficiency. We collect and analyze I/O traces from two production supercomputers, and find that the I/O traffic peaks in the system not only occur in short periods of time but also originate from a minority of adjacent compute nodes, which we call spatially bursty I/O. Since modern supercomputers widely adopt I/O forwarding architecture, in which an I/O node performs I/O on behalf of a subset of compute nodes in the vicinity, spatially bursty I/O will cause significant load imbalance and underutilization on the I/O nodes. To address such problems, we quantitatively analyze the two causes of spatially bursty I/O, including uneven I/O distribution on job's processes and uneven job nodes distribution on the system. Two different solutions are proposed to mobilize more I/O nodes to participate in job's I/O activity. (1) We change the I/O node mapping, making adjacent compute nodes use different I/O nodes instead of a same one. (2) According to the job's I/O characteristics extracted from history I/O traces, we distribute the compute nodes of data-intensive jobs more sparsely to utilize more I/O nodes. Extensive evaluations of both solutions show that they can further exploit the potential of I/O forwarding layer. We have deployed the proposed I/O node mapping on a production supercomputer for 11 months. Our experience finds that it can effectively promote I/O performance, balance loads, and alleviate I/O interference.

Inquiry performs one of the backbones in current E-commerce websites. Detecting spams in inquiries is essential for these platforms but is surprisingly underexplored by current research. In this paper, we propose a system coined ISTBEL to detect spam inquiries. Motivated by the observations on both behavioral and linguistic differences between spammers and benign users, ISTBEL jointly utilizes the temporal-categorical behavioral sequence and text sequence to accomplish its purpose. At its heart, a variant of LSTM equipped interactive attentions is devised to strengthen the coherence between the categorical and temporal behaviors. A character-level CNN is adopted to capture the capricious linguistic patterns. These two modules are integrated and collaboratively contribute to the final predictions. Our system is trained through an end-to-end manner and can directly utilize raw data as input without tedious feature engineering. We have applied the system to inquiry spam detection on Alibaba.com, and results show ISTBEL is promising in detection spammers and outperforms baselines by a large margin. Meanwhile, ISTBEL can be easily applied to other applications which contain both behavioral and linguistic information.

The scheduling approach in MapReduce may result in the "long tail" problem because of the unreasonable task assignment and high scheduling overhead because of an amount of task scheduling operations. To address these problems, a new task scheduling approach for MapReduce, named "Iterative Task Scheduling Algorithm", is proposed. The new approach tries to schedule the map tasks according to the solution of the equation for the optimal task assignment. Thus the "long tail" problem can be mitigated effectively and the task scheduling operations can be significantly reduced. To support our new scheduling approach, two approaches are proposed: The first one is adopted to estimate task execution times of nodes and the second one is adopted to produce the optimal task assignment based on the known task execution times of nodes. Comprehensive experiments have been performed with the real log data from the Ali Cloud and the results verify the effectiveness of the new task scheduling approach. The map runtime of the job is reduced 23% in our experiments.

Virtualization is the foundation for cloud computing, and the virtualization can not be achieved without software defined, elastic, flexible and scalable virtual layers. Unfortunately, if multiple virtual storage devices are chained together, the system may be subject to severe performance degradation. While the read-ahead (RA) mechanism in storage devices plays a very important role to improve I/O performance, RA may not be effective as expected for multiple virtualization layers, since it is originally designed for one layer only. When I/O requests are passed through a long I/O path, they may trigger a chain reaction and lead to unnecessary data transmission and thus bandwidth waste. In this paper, we study the dynamic behavior of RA through multiple I/O layers and demonstrate that if controlled well, RA can greatly accelerate I/O speed. We present RAFlow, a RA control mechanism, to effectively improve I/O performance by strategically expanding RA window at each layer. Our real-world experiments show that it can achieve 20% to 50% performance improvement in I/O paths with up to 8 virtualized storage devices.

Due to the heterogeneity and the multigrain parallelism of the heterogeneous multi-core computer, communication and memory access show hierarchical characteristics ignored by other models. In this paper, a new model named mPlogP, is presented on the basis of the PlogP model, in which communication and memory access is abstracted by considering these new characteristics of the heterogeneous multi-core computer. It uses memory access to model the behavior of computation, estimates the execution time of every part of applications and guides the optimization of effective parallel programs. Finally this proposed model is validated by experiments that it can precisely evaluate the execution of parallel applications under the heterogeneous multi-core computer.

In the paper, we investigate the memory access technology on cell broadband engine architecture (CBEA), and develop a profiling infrastructure for memory management on the architecture. By registering the dynamic memory allocation and providing details of trace of memory access, the infrastructure provides the data partition information automatically which alleviates the burdens of programmer and provides a safety guarantee for aggressive data prefetch for computing task. On the other hand, the profile information is useful for analyzing the patterns of memory access and helpful for further performance optimization. Experimental results show that applications implemented based on our SDK library not only support aggressive memory access method without the requirement of external data partition information, but also could be optimized aggressively under the guideline of the profile information provided by the proposed SDK library.