一句话：分享构建可扩展、容错且具QoS保障的数据密集型数据迁移系统的经验与挑战。
随着医疗、公共管理等行业越来越依赖数据驱动决策，数据密集型（DI）应用变得日益关键。这类应用必须容错、随数据量扩展，并能弹性利用新增资源；在突发过载时还要避免数据丢失，并提供服务质量（QoS）保证。论文标题和摘要指向数据迁移场景，声称分享构建此类系统的经验并讨论挑战。然而摘要未描述具体系统架构、迁移机制或实现技术，也未给出实验结果、性能指标或评估方法，因此主要贡献是问题与需求层面的经验性总结和挑战刻画，而非可复现的技术方案或实证结论。
Recent analyses[2, 4, 5] report that many sectors of our economy and society are more and more guided by data-driven decision processes (e.g., health care, public administrations, etc.). As such, Data Intensive (DI) applications are becoming more and more important and critical. They must be fault-tolerant, they should scale with the amount of data, and be able to elastically leverage additional resources as and when these last ones are provided [3]. Moreover, they should be able to avoid data drops introduced in case of sudden overloads and should offer some Quality of Service (QoS) guarantees.
DOI 原文 ·
@inproceedings{paperbot4435,
title = {Experiences and challenges in building a data intensive system for data migration},
author = {Marco Scavuzzo and Elisabetta Di Nitto and Danilo Ardagna},
booktitle = {ICSE},
year = {2018},
doi = {10.1145/3180155.3182534}
}