Managing diverse data formats and improving query processing efficiency at cloud service centers is crucial in the era of interconnected heterogeneous devices. Lakehouse effectively manages heterogeneous data but faces challenges in maintaining SQL query performance and data independence in large-scale analyses. Precomputation, which stores intermediate results in advance, reduces query latency by a space-for-time trade-off. This paper addresses the challenges of matching and rewriting results in lakehouse environments. By combining pre-computation with the low-cost and dynamically scalable storage characteristics of cloud storage, we introduce an optimized lakehouse architecture with an in-memory index and dynamic task scheduling to enhance OLAP performance while balancing data center resources. Experiments demonstrate improvements in query performance ranging from 6.1 % to 64.9% over native lakehouse architectures and reductions in resource occupation from the original 75.6% to 17.3% with preloading and 25.0% with dynamic scheduling.

Considering the potential benefits to lifespan and performance, zoned flash storage is expected to be incorporated into the next generation of consumer devices. However, due to the limited volatile cache and heterogeneous flash cells of consumer-grade flash storage, adopting a zone abstraction requires additional internal hardware design to maximize its benefits. To understand and efficiently improve the hardware design on consumer-grade zoned flash storage, we present ConZone—the first emulator tailored to the characteristics of consumer-grade zoned flash storage. Users can explore the internal architecture and management strategies of consumer-grade zoned flash storage and integrate the optimization with software. We validate the accuracy of ConZone by realizing a hardware architecture for consumer-grade zoned flash storage and comparing it with the state-of-the-art. We also make a case study for read performance research with ConZone to explore the design of mapping mechanisms and cache management strategies.

WriteBooster is widely adopted as a non-volatile write buffer to enhance user experience for smartphones. How-ever, the host suffers sub-optimal write performance when using WriteBooster due to the lack of utilization of rich semantic infor-mation. With the zoned storage being included in smartphones, WriteBooster design presents new opportunities. In this paper, we propose Zoned-WB, a WriteBooster management scheme based on zoned storage to utilize the rich semantic information on the host to improve user experience. Specifically, Zoned- Wbincludes two parts, zoned storage-based WB and foreground request-aware WB. First, the zoned storage-based WB is aimed at designing WriteBooster management scheme based on zoned storage. Second, foreground request-aware WB is designed to adaptively adjust the capacity quota for different types of requests in Writebooster based on rich semantic information on the host. We evaluate Zoned-WB on a zoned storage emulator with workloads collected from smartphones. Evaluation results show that Zoned- Wbcan effectively improve user experience.

With the rapid development of the MIoT(mobile Internet of things), the number of MIoT devices has increased rapidly. The collection and management of the status information continuously submitted by MIoT devices has brought great pressure to the network bandwidth of the data center. Existing edge data storage services lack the ability of data range query to support MIoT stream analysis, so we propose a cloud-edge collaborative data service, CEDS, to solve that problem. It uses edge computing nodes to store data sent by devices nearby and uses a central node to manage metadata and query data on edge nodes. In order to reduce the network transmission during data query, we use query splitting and pushdown optimization techniques to make each edge node only return aggregated data within the query range. To improve the query latency of global search, we propose an edge data indexing mechanism based on the compressed Rosetta filter. We test the CEDS performance with the taxi management task. Experiments illustrate that CEDS can efficiently support storing and range query of MIoT data streams with a small network traffic cost.

This paper investigates the long-term vessel trajectory prediction problem. A challenge in long-term prediction is modeling the navigation intention efficiently. We propose a novel model called Environment-Aware Vessel Trajectory Prediction Network (EA-VTP), which introduces the environment feature from the vessel density map. The vessel density map records the number of vessels of each position and therefore indicates the conventional tracks. A convolutional neural network is applied to the vessel density map to extract the information, which is then utilized by the recurrent module for the prediction. In addition, we introduce higher-order differentials and the residual prediction, which exploits the continuity of trajectory and balances the gradient of all steps. Besides, EA-VTP also provides the confidence range of position distribution, thus increases the reliability. The experiment shows that our EA-VTP outperforms baselines, and the environment feature effectively improves both short-term and long-term accuracy.