In recent years, Mobile Edge Computing (MEC) has been widely used for latency-sensitive tasks, but task scheduling in dynamic edge environments still faces two key challenges. First, edge devices are prone to failures, and existing fault-tolerance mechanisms lack task-aware modeling, making it hard to ensure timeliness and reliability under failures. Second, due to limited perception, high communication costs, and complex task structures, current scheduling strategies still struggle with adaptability and stability in dynamic systems. In this paper, we propose a Fault-Tolerant Discrete Soft Actor-Critic scheduling algorithm (FT-DSAC). Initially, we design a Primary-Backup-based Fault-Tolerant (PBFT) scheduling mechanism, which constrains task offloading locations and start times to effectively mitigate the impact of failures on task execution. Furthermore, we incorporate the Centralized Training and Distributed Execution (CTDE) architecture, which enables implicit collaborative scheduling decisions among edge servers to optimize system performance and reduce communication overhead. Finally, We conduct extensive experiments using both simulated data generated by DAGGEN and real-world workflow data. Experimental results show that the proposed algorithm significantly improves task execution success rates by 6%-19% and reduces latency by 9%-27% compared to mainstream benchmarks.

Federated learning (FL) enables training machine learning models across distributed devices while preserving data privacy. However, the growing demand for large-scale model training raised by data-driven intelligent applications faces critical bottlenecks in traditional FL frameworks, including communication overhead and latency, edge heterogeneity, and inefficient resource utilization. To address these challenges, we propose a cloud-edge-end collaborative federated learning (CECFL) algorithm to empower the larger model training. CECFL introduces a dual-phase hierarchical aggregation mechanism, including synchronous end-edge aggregation to harmonize localized model updates from massive resource-constrained devices, and semi-asynchronous edge-cloud aggregation to mitigate stragglers and communication delays across heterogeneous tiers. Furthermore, we design a deep reinforcement learning based adaptive orchestration framework that dynamically optimizes end-edge associations and edge participation rates, ensuring efficient resource allocation. Extensive experiments are carried out to demonstrate the high effectiveness of the CECFL in terms of improving the model accuracy and the system efficiency.

Stream processing jobs often require elastic scaling in response to dynamically changing loads. In recent years, the development of serverless technology has provided new potential for elastic scaling of stream processing jobs. However, directly implementing stream processing jobs to serverless platforms faces many challenges such as program complexity, high latency and load imbalance. In this paper, we propose an efficient stream processing system under serverless environment. Our system provides a high-level programming interface and can automatically determine the parallelism under the current load through a non-linear model. Additionally, it reduces the latency and increases the throughput of stream processing jobs through decentralized orchestration and soft affinity scheduling policies. We conducted a series of experiments to evaluate the performance of our system. The experimental results show that our system effectively improves the throughput and reduces the latency of stream processing jobs under serverless environment compared with the widely-used scheduling and orchestration methods used in serverless environment.

In the commercial sphere, such as operations and maintenance, advertising, and marketing recommendations, intelligent decision-making utilizing data mining and neural network technologies is crucial, especially in resource allocation to optimize ROI. This study delves into the Cost-aware Binary Treatment Assignment Problem (C-BTAP) across different industries, with a focus on the state-of-the-art Direct ROI Prediction (DRP) method. However, the DRP model confronts issues like covariate shift and insufficient training data, hindering its real-world effectiveness. Addressing these challenges is essential for ensuring dependable and robust predictions in varied operational contexts. This paper presents a robust Direct ROI Prediction (rDRP) method, designed to address challenges in real-world deployment of neural network-based uplift models, particularly under conditions of covariate shift and insufficient training data. The rDRP method, enhancing the standard DRP model, does not alter the model's structure or require retraining. It utilizes conformal prediction and Monte Carlo dropout for interval estimation, adapting to model uncertainty and data distribution shifts. A heuristic calibration method, inspired by a Kaggle competition, combines point and interval estimates. The effectiveness of these approaches is validated through offline tests and online A/B tests in various settings, demonstrating significant improvements in target rewards compared to the state-of-the-art method.

System logs contain valuable information and they have emerged as one of the most crucial data sources for system monitoring aimed at enhancing service quality. IT support teams and system administrators are in dire need of an intelligent log-based QA system to help them quickly identify, diagnose, and resolve issues. In this paper, we propose a novel method for constructing log-based question-answering (QA) data using large language models, addressing challenges associated with limited dataset size and diversity in existing log-based QA systems. Our pipeline consists of three steps: generating questions, answering log questions, and refining question-answer pairs. The purpose of the generating questions is to create a diverse set of log-related queries that cover a wide range of potential issues. The second step, answering log questions, aims to extract relevant information from the logs to address the generated questions. This step ensures accurate and context-aware responses. Refining question-answer pairs is intended to improve the overall quality and consistency of the generated log-based QA data. We present a case study using ChatGPT to generate a new dataset, LogQuAD, containing over 28,000 question-answer pairs derived from more than 31,000 raw logs, representing a significant increase compared to existing datasets like LogQA. In our experimental setting, we sample half of the data as the training set and use memory-effect fine-tuning to fine-tune the model, named Gloss. Experimental results show that our method can generate high-quality log-based QA data, leading to improved performance of log-based QA models. Notably, our fine-tuned 7B model outperforms the LLaMA-65B model. This approach can potentially save valuable time for IT support teams and system administrators, enabling proactive problem resolution and optimal system performance.

Molecular dynamics is a widely adopted simulation method for analyzing the movement of atoms and molecules. Traditional molecular dynamics simulation methods are computationally intensive and difficult to simulate a large number of atoms. In contrast, molecular dynamics based on deep potential models such as DeePMD can leverage deep learning techniques to improve simulation efficiency. Although DeePMD has incorporated mainstream deep learning frameworks, it still suffers from low performance and efficiency during its model training on heterogeneous systems such as CPU and GPU. Particularly, a large number of operators cannot be accelerated by GPU, resulting in low utilization of GPU computational resources. In this paper, we comprehensively analyze the computational bottlenecks and the corresponding root causes of DeePMD. We correspondingly propose several novel optimization strategies. Specifically, for preprocessing, we identify the computation redundancies and the GPU parallelization opportunities for performance optimization. For training, we propose optimization strategies such as operator fusion, redundancy elimination, and concurrent execution of multiple streams and threads in the computation process. Moreover, we apply systematical optimization of computational graphs and operators. The evaluation results show that DeePMD can achieve significant speedups in several cases after applying our proposed optimizations, resulting in a maximum overall speedup of 6.36× with acceptable accuracy.

Big data applications are widely adopted to mine valuable information from a tremendous amount of industry data, which is commonly represented as a series of map-reduce operations. Among various map-reduce frameworks, Hadoop is most commonly adopted for data processing at large scale. Although Hadoop eases the development of highly scalable distributed big data applications, inefficient implementation due to poor coding practice and deep software abstractions can cause severe performance issues such as unreasonable slowdown, high response latency, and waste of computing resources, which can lead to unsatisfactory serving delay or significant maintenance cost. In this paper, we first categorize three common types of redundant patterns in big data applications. Then we propose a tool-assisted optimization workflow to detect the redundant patterns automatically, which profiles the application by sampling hardware performance monitoring units. Moreover, we present a profiling visualization method that can help to pinpoint the redundant codes. Based on these approaches, we optimize several big data applications by eliminating redundancies, yielding up to 14.8% performance improvement.

With the development of event knowledge graph technology, researchers have solved the singleness problem of event graph based on temporal relationship by constructing event logic graph, but have not integrated the multiple relationships among events with time series data for trend prediction.In addition, due to the impact of COVID-19, corporate credit risks have been gradually exposed in recent years, and defaults have occurred frequently.The technology of event graph and event logic graph is mostly used for event schema induction, script induction, etc., but abundant graph knowledge is not well exploited for forecast task.To fill this gap, we construct an event logic graph by extracting various types of event relationships, such as causal relationship, sequential relationship, parallel relationship, and reversal relationship.Different types of edges among events are used to represent different relationships.Combined with the time series of corporate credit bonds, a temporal convolutional network driven by event logic graph is built, and applied to forecast corporate credit risk.We extract structured events from financial news, construct event logic graph and learn the graph knowledge.Then, the event logic graph embedding is combined with time series of bonds to forecast whether the corporate will default.Experiments show that the proposed method outperforms baseline methods in forecasting credit risk.