DRAM faults are the main hardware cause of node unavailability in clouds. To enable early preventive actions and mitigate DRAM fault impacts, prior studies focus on predicting DRAM uncorrectable errors (UEs) that typically cause immediate node unavailability. However, in Alibaba Cloud, we observe that correctable error(CE) storm (numerous CEs occur in a short period) dominates 41% DRAM-caused node unavailability (DCNU). In this paper, we propose to predict DCNU by taking into account both UEs and CE storms. Specifically, observing that DCNUs have strong relevance to the temporal statistics and spatial patterns of CEs, we design novel spatio-temporal features and use soft labels to build a DCNU predictor. We propose novel rule mining algorithms that can generate accurate and interpretable rules to improve the prediction performance. Considering the predictor’s real effects cannot be evaluated by traditional metrics like F1-score, we propose a new metric, NURR, to quantify the node unavailability reduction rate and tune model hyperparameters with NURR. The comparative study shows that our approach achieves over 40% better NURR than existing methods and runs stably in the production environment.

DRAM failures are one of the leading failures in large-scale clouds. Previous studies focus on predicting DRAM uncorrectable errors (UEs) and mitigating the impact of DRAM failures through node-level workload migration and proactive dual in-line memory module (DIMM) replacement. For cloud systems, node migration means migrating all virtual machines (VMs) to other nodes. Such a coarse-grained migration consumes considerable resources. Inspired by the observation that DRAM errors tend to cluster in space, this paper proposes Pegasus, the first VM-level solution to mitigate DRAM failures. The key idea of Pegasus is to use the VM as the basic unit for predicting DRAM failures instead of the whole node. Specifically, we introduce a new concept of DRAM-caused risky VMs, which causes node unavailability when accessed. We design a novel Error-VM mapping framework deployed on a large-scale cloud. Statistical results confirm that DRAM errors are concentrated in the address space managed by a small number of VMs. By combining ECC and spatio-temporal features, our predictor achieves decent performance. Pegasus has been deployed online on over $\mathbf{3 0 0, 0 0 0}$ nodes in the production cloud. The comparative study shows that the prediction performance of Pegasus is comparable to node-level solutions. Meanwhile, our approach offers over $\mathbf{7 0 \%}$ lower costs and avoids more than $\mathbf{1 0 \%}$ of VM crashes compared to node-level mitigation.

With the increasing prevalence of web servers, protecting them from cyber attacks has become a crucial task for online service providers.Webshells, which are backdoors to websites, are commonly used by hackers to gain unauthorized access to web servers.However, traditional methods for detecting webshells often fail to produce satisfactory results due to the use of obfuscation or encryption to conceal their characteristics.In recent years, webshell detection methods based on deep learning (DL) have received significant attention, but they struggle to preserve the syntax and semantic information contained in the source code.In this paper, we propose a structuralaware webshell detection system to address these problems, denoted as SAWD.Specifically, we first generate the control flow graph (CFG) with syntax and semantic information from the PHP source code.Then, we leverage CFG to build our graph representation, which consists of the adjacency matrix and keywords-based basic block features.Finally, based on our graph representation, we adopt convolutional neural networks (GCN) combined with graph pooling to detect webshells more efficiently.Experimental results demonstrate that our method outperforms state-of-the-art webshell detection systems on the collected dataset.

Peer review refers to the process of a group of reviewers jointly assessing a thesis or a journal article. Reviewer assignment constitutes the major challenge in this process. However, many studies only focus on the predictive accuracy of the matching degree between reviewers and theses. Recent works show that the unitary accuracy metric can not satisfy the requirement of maximizing the comprehensive performance of the reviewer recommendation system. In this paper, we introduce a Chinese thesis reviewer personalized recommendation model(TRPRM) for the reviewer assignment problem. The TRPRM focuses on an extra crucial metric in recommendation system evaluation: novelty. The model uses a feature crossing method to mine the interactions among different research interests of author. Moreover, we analyze the role of accuracy and novelty as indicators of recommendation quality and present novel ways of how to measure them. Lastly, we apply our model to the reviewer assignment problem using a real dataset from a university in China to verify the comprehensive performance of our proposed approach.