Satellites play a crucial role in Internet of Things (IoT) applications that require precise positioning. Satellite orbit prediction serves as the foundation for providing accurate terminal location services. However, traditional satellite orbit prediction faces challenges like measurement errors, estimation errors, and unmodeled orbit disturbances, leading to low prediction accuracy. To address this issue, this article introduces a groundbreaking satellite digital twin (DT) system based on container technology. This system facilitates real-time mirroring, monitoring, optimization, and control of satellite orbit prediction with low power consumption. Leveraging the advantages of container technology allows for convenient and efficient model updating. Furthermore, a new satellite orbit error prediction model is explored within this system. This model utilizes the seasonal-trend decomposition using locally weighted regression (STL) method and the temporal convolutional network (TCN) algorithm. By decomposing satellite orbit data into multiple components, the proposed model achieves enhanced future orbit Prediction by combining predicted values from each component. Different from existing machine learning (ML) orbit prediction models, our proposed model explores the variation patterns of satellite orbit data from a trend and cycle perspective, rather than relying solely on collecting more data and training larger models to improve prediction accuracy, which makes the novel prediction scheme get good performance while keeping low prediction complexity. Extensive experiments validate the effectiveness of the proposed method using two publicly available satellite orbit datasets (ILRS catalogue and TLE catalogue). The experimental results show that compared with traditional orbit prediction models, the novel DT system has less model update time and occupies less memory. The mean absolute error (MAE) value of the new model is lower than the five ML models in existing researches, which proves that the proposed STL-TCN model has higher prediction accuracy than existing ML orbit prediction models. In addition, we discussed the impact of atmospheric pressure density on the STL-TCN model, and experiments have shown that the correction of different atmospheric pressure density models has a very small impact on the prediction accuracy of the STL-TCN model. Finally, we further investigate the generalization ability of the STL-TCN model for other satellite orbits and future time orbits, and the results show that the novel model has satisfactory generalization ability.

Persistent memory (PM) brings important opportunities for improving data storage including the widely used hash tables. However, PM is not friendly to small writes, which causes existing PM hashes to suffer from high hardware write amplification. Hybrid memory offers the performance and concurrency of DRAM and the durability and capacity of PM, but existing hybrid memory hashes cannot deliver high performance, low DRAM footprint, and fast recovery at the same time. This paper proposes WALSH, a flat hash with novel log-structured separate chaining designs to optimize the performance while ensuring low DRAM footprint and fast recovery. To address the overhead of hash resizing and garbage collection (GC), WALSH further proposes partial resizing/GC mechanisms and a 4-phase protocol for concurrent hash operations. As a result, WALSH is the first flat index for hybrid memory with embedded write aggregation ability. A comprehensive evaluation shows that WALSH substantially outperforms state-of-the-art hybrid memory hashes; e.g., its insert throughput is up to 2.4X that of related works while saving more than 87% of DRAM. WALSH also provides efficient recovery; e.g., it can recover a dataset with 1 billion objects in just a few seconds.

Anomaly detection is a critical task for maintaining the performance of a cloud system. Using data-driven methods to address this issue is the mainstream in recent years. However, due to the lack of labeled data for training in practice, it is necessary to enable an anomaly detection model trained on contaminated data in an unsupervised way. Besides, with the increasing complexity of cloud systems, effectively organizing data collected from a wide range of components of a system and modeling spatiotemporal dependence among them become a challenge. In this article, we propose TopoMAD, a stochastic seq2seq model which can robustly model spatial and temporal dependence among contaminated data. We include system topological information to organize metrics from different components and apply sliding windows over metrics collected continuously to capture the temporal dependence. We extract spatial features with the help of graph neural networks and temporal features with long short-term memory networks. Moreover, we develop our model based on variational auto-encoder, enabling it to work well robustly even when trained on contaminated data. Our approach is validated on the run-time performance data collected from two representative cloud systems, namely, a big data batch processing system and a microservice-based transaction processing system. The experimental results show that TopoMAD outperforms some state-of-the-art methods on these two data sets.

Persistent memory (PM) has always been used in combination with DRAM to configure hybrid main memory systems that can obtain both the high performance of DRAM and large capacity of PM. There are critical management challenges in data placement, memory concurrency and workload scheduling for the concurrent execution of multiple application workloads. But the non-negligible performance gap between DRAM and PM makes the existing application-agnostic management strategies inefficient in reaching the full potential of hybrid memory. In this paper, we propose a series of application aware optimization strategies, including application aware data placement, adaptive thread allocation and inter-application interference avoiding, to improve the concurrent performance of different application workloads on hybrid memory. Finally, we provide the performance evaluation for our application aware solutions on real hybrid memory hardware with some comprehensive benchmark suites. Our experimental results show that the duration of multi-application concurrent execution on hybrid memory can be reduced by at most 60.7% for application aware data placement, 37.7% for adaptive thread allocation and 34.8% for workload scheduling with inter-application interference avoiding, respectively. And the additive effects of all these three optimization methods can reach 62.8% performance improvement with negligible overheads.

Based on the development status of telecom operators in the smart home business, and combined with the digital China strategy, this paper proposes a research idea and a technical solution for building a digital main center platform for smart home based on big data. This solution can realize a more convenient and easy-to-use big data productivity platform, provide a more comprehensive data index system, data processing capabilities, and data integration standards, and provide guidance for building a customizable and reliable data product system. This platform can enhance the powerful supporting force of the data economy under the new development pattern of the communications industry and promote the intelligent level of smart home business.

Abstract The unprecedented proliferation of mobile smart devices has propelled a promising computing paradigm, Mobile Crowd Sensing (MCS), where people share surrounding insight or personal data with others. As a fast, easy, and cost-effective way to address large-scale societal problems, MCS is widely applied into many fields, e.g., environment monitoring, map construction, public safety, etc. Despite the popularity, the risk of sensitive information disclosure in MCS poses a serious threat to the participants and limits its further development in privacy-sensitive fields. Thus, the research on privacy protection in MCS becomes important and urgent. This paper targets the privacy issues of MCS and conducts a comprehensive literature research on it by providing a thorough survey. We first introduce a typical system structure of MCS, summarize its characteristics, propose essential requirements on privacy on the basis of a threat model. Then, we survey existing solutions on privacy protection and evaluate their performances by employing the proposed requirements. In essence, we classify the privacy protection schemes into four categories with regard to identity privacy, data privacy, attribute privacy, and task privacy. Besides, we review the achievements on privacy-preserving incentives in MCS from four viewpoints of incentive measures: credit incentive, auction incentive, currency incentive, and reputation incentive. Finally, we point out some open issues and propose future research directions based on the findings from our survey.