ORCID Profile
0000-0002-1347-1974
Current Organisation
University of the Chinese Academy of Sciences
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Publisher: arXiv
Date: 2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2023
Publisher: Wiley
Date: 04-2023
DOI: 10.1002/CPE.7709
Abstract: As the capacity of Solid‐State Drives (SSDs) is constantly being optimised and boosted with gradually reduced cost, the SSD cluster is now widely deployed as part of the hybrid storage system in various scenarios such as cloud computing and big data processing. However, despite its rapid developments, the performance of the SSD cluster remains largely under‐investigated, leaving its sub‐optimal applications in reality. To address this issue, in this paper we conduct extensive empirical studies for a comprehensive understanding of the SSD cluster in erse settings. To this end, we configure a real SSD cluster and gather the generated trace data based on some often‐used benchmarks, then adopt analytical methods to analyse the performance of the SSD cluster with different configurations. In particular, regression models are built to provide better performance predictability under broader configurations, and the correlations between influential factors and performance metrics with respect to different numbers of nodes are investigated, which reveal the high scalability of the SSD cluster. Additionally, the cluster's network bandwidth is inspected to explain the performance bottleneck. Finally, the knowledge gained is summarised to benefit the SSD cluster deployment in practice.
Publisher: Springer Nature Switzerland
Date: 2023
Publisher: Elsevier BV
Date: 10-2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 15-06-2023
Publisher: arXiv
Date: 2020
Publisher: Wiley
Date: 11-04-2022
DOI: 10.1002/CPE.6996
Abstract: As one of the most useful online processing techniques, the theta‐join operation has been utilized by many applications to fully excavate the relationships between data streams in various scenarios. As such, constant research efforts have been put to optimize its performance in the distributed environment, which is typically characterized by reducing the number of Cartesian products as much as possible. In this article, we design and implement a novel fast theta‐join algorithm, called Prefap , by developing two distinct techniques— prefiltering and amalgamated partitioning —based on the state‐of‐the‐art FastThetaJoin algorithm to optimize the efficiency of the theta‐join operation. Firstly, we develop a prefiltering strategy before data streams are partitioned to reduce the amount of data to be involved and benefit a more fine‐grained partitioning. Secondly, to avoid the data streams being partitioned in a coarse‐grained isolated manner and improve the quality of the partition‐level filtering, we introduce an amalgamated partitioning mechanism that can amalgamate the partitioning boundaries of two data streams to assist a fine‐grained partitioning. With the integration of these two techniques into the existing FastThetaJoin algorithm, we design and implement a new framework to achieve a decreased number of Cartesian products and a higher theta‐join efficiency. By comparing with existing algorithms, FastThetaJoin in particular, we evaluate the performance of Prefap on both synthetic and real data streams from two‐way to multiway theta‐join to demonstrate its superiority.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 15-02-2023
Publisher: Wiley
Date: 12-07-2022
DOI: 10.1002/CPE.7163
Abstract: With the fast growing quantity of data generated by smart devices and the exponential surge of processing demand in the Internet of Things (IoT) era, the resource‐rich cloud centers have been utilized to tackle these challenges. To relieve the burden on cloud centers, edge‐cloud computation offloading becomes a promising solution since shortening the proximity between the data source and the computation by offloading computation tasks from the cloud to edge devices can improve performance and quality of service. Several optimization models of edge‐cloud computation offloading have been proposed that take computation costs and heterogeneous communication costs into account. However, several important factors are not jointly considered, such as heterogeneities of tasks, load balancing among nodes and the profit yielded by computation tasks, which lead to the profit and cost‐oriented computation offloading optimization model PECCO proposed in this article. Considering that the model is hard in nature and the optimization objective is not differentiable, we propose an improved Moth‐flame optimizer PECCO‐MFI which addresses some deficiencies of the original Moth‐flame optimizer and integrate it under the edge‐cloud environment. Comprehensive experiments are conducted to verify the superior performance of the proposed method when optimizing the proposed task offloading model under the edge‐cloud environment.
Publisher: arXiv
Date: 2022
Publisher: Springer Science and Business Media LLC
Date: 10-09-2021
DOI: 10.1038/S41598-021-97570-3
Abstract: Current research on DNA storage usually focuses on the improvement of storage density by developing effective encoding and decoding schemes while lacking the consideration on the uncertainty in ultra-long-term data storage and retention. Consequently, the current DNA storage systems are often not self-contained, implying that they have to resort to external tools for the restoration of the stored DNA data. This may result in high risks in data loss since the required tools might not be available due to the high uncertainty in far future. To address this issue, we propose in this paper a self-contained DNA storage system that can bring self-explanatory to its stored data without relying on any external tool. To this end, we design a specific DNA file format whereby a separate storage scheme is developed to reduce the data redundancy while an effective indexing is designed for random read operations to the stored data file. We verified through experimental data that the proposed self-contained and self-explanatory method can not only get rid of the reliance on external tools for data restoration but also minimise the data redundancy brought about when the amount of data to be stored reaches a certain scale.
Publisher: Tsinghua University Press
Date: 12-2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2023
No related grants have been discovered for Jiashu Wu.