• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Reliability Engineering & System Safety >Quantitative approaches for optimization of user experience based on network resilience for wireless service provider networks
【24h】

Quantitative approaches for optimization of user experience based on network resilience for wireless service provider networks

机译：用于无线服务提供商网络的基于网络弹性的优化用户体验的量化方法

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Since the 1980's and in particular 1996, telecom operators and recently mobile operators have been facing increasingly fierce competition, combined with flat subscriber growth and increased data usage resulting in tremendous downward pressures on profitability, forcing operators to differentiate themselves by trying to offer network services with better customer experience at lower operational costs. Wireless operators are challenged with measuring user experience which in itself is subjective, in a manner that accurately reflects the functional and emotional aspects of perceived quality and linking to Network Resiliency which characterizes the network behavior as it responds to disruptions. Current network faults and alarms only consider device failures and do not consider actual impact to user experience. For instance a failed router may not impact the users experience due to built in redundancies in the network. Studies to date, have proposed methods and models that focus on specific aspects of user experience in wired and cellular networks. However, to the best of our knowledge, there is currently very little research that connects linking poor user network experience to root cause. Previous recent work in this area focus on identifying what and where measurements to gage subscriber OoE, modeling and high level concepts, but do not address realistic challenges and approaches that can be automated to materially impact improved customer experiences at lower operational expenses. There is a gap on how operators can automatically associate poor user experience, relevant network metrics and root causes with a suitable model that can be analyzed and optimized. We propose a general framework for a solution that links these entities together, with a quantified approach to optimize user network experience by optimizing network resilience using a model that can be analyzed and optimized using machine learning methods to improve resilience and hence user experience. Results of directly applying existing machine learning algorithms for identifying root causes to network telemetry data have proven to be ineffective in practice due to the fact that existing machine learning algorithms are designed for prediction, classification and ranking not for identifying causal relationships and further complicated by the fact that these algorithms have assumptions on the data and in reality the network data distributions vary wildly during network disturbances. The proposed general framework combines existing methods for anomaly detection and machine learning algorithms, however the novel contribution centers on improving the accuracy of finding associated root causes by dynamically selecting the optimal machine learning algorithm based on the network telemetry data features that are recomputed before, during and after network disturbances. The proposed approach then allows us to automate the time consuming manual tasks of network engineers that proactively monitor key performance metrics for anomalies, correlate with other data sources to ultimately determine actionable insights to maintain a certain acceptable level of user experience by dynamically selecting the appropriate machine learning algorithm for the given data characteristics or features. We describe an example case study specific to wireless provider environment, illustrating the potential viability with results from actual wireless(approx 8 million monthly subscribers) operations data showing promising results by applying the proposed approach.The prototype implementation was able to programmatically detect anomalies, identify potential root causes using different algorithms suitable for the given data and time frame, which dramatically increased the accuracy and efficiency of the small network engineering team, and hence improved the user experience by improving network resiliency.
机译：自1980年代以来，尤其是1996年以来，电信运营商和最近的移动运营商一直面临着日益激烈的竞争，再加上用户数量的增长和数据使用量的增加，导致盈利能力面临巨大的下行压力，迫使运营商通过提供网络服务来实现差异化。以更低的运营成本获得更好的客户体验。无线运营商面临的挑战是衡量用户体验本身就是主观的，其方式是准确反映所感知质量的功能和情感方面，并链接到表征网络行为（响应网络对中断的响应）的网络弹性。当前的网络故障和警报仅考虑设备故障，不考虑对用户体验的实际影响。例如，由于网络中内置的冗余，出现故障的路由器可能不会影响用户体验。迄今为止的研究已经提出了针对有线和蜂窝网络中用户体验的特定方面的方法和模型。但是，据我们所知，目前很少有将不良用户网络体验链接到根本原因的研究。该领域以前的最新工作重点是确定对量具订户OoE，建模和高级概念的测量方法和位置，但是并未解决现实的挑战和方法，这些方法和方法可以自动化地以较低的运营成本对改进的客户体验产生实质性的影响。运营商如何自动将不良的用户体验，相关的网络指标和根本原因与可以分析和优化的合适模型相关联，存在差距。我们提出了一种将这些实体链接在一起的解决方案的通用框架，并提出了一种量化方法来优化用户网络体验，方法是使用可通过使用机器学习方法进行分析和优化以提高弹性从而改善用户体验的模型来优化网络弹性。由于将现有的机器学习算法设计用于预测，分类和排序而不是确定因果关系，并且由于复杂的原因，直接将现有的机器学习算法用于识别网络遥测数据根本原因的结果在实践中被证明是无效的。这些算法对数据有假设，实际上，在网络干扰期间，网络数据分布会发生巨大变化。提出的通用框架将异常检测的现有方法与机器学习算法结合在一起，但是新的贡献集中在通过基于在遥测之前，过程中重新计算的网络遥测数据特征动态选择最佳机器学习算法来提高找到相关根本原因的准确性。和网络干扰之后。然后，所提出的方法使我们能够自动化耗时的网络工程师的手动任务，这些任务主动监控关键性能指标的异常情况，并与其他数据源相关联，从而最终确定可行的见解，以通过动态选择合适的机器来维持一定程度的用户体验。给定数据特征或特征的学习算法。我们描述了一个针对无线提供商环境的案例研究案例，通过应用所提出的方法，通过实际无线（每月约800万订户）运营数据的结果说明了潜在的可行性，这些数据显示了有希望的结果。原型实现能够以编程方式检测异常，识别使用适合给定数据和时间框架的不同算法的潜在根本原因，这极大地提高了小型网络工程团队的准确性和效率，并因此通过提高网络弹性来改善用户体验。

著录项

  • 来源
    《Reliability Engineering & System Safety》 |2020年第1期|106606.1-106606.12|共12页
  • 作者

    Kakadia Deepak; Ramirez-Marquez Jose Emmanuel;

  • 作者单位

    Stevens Inst Technol Sch Syst Engn Hoboken NJ 07030 USA|Google 1600 Amphitheatre Way Mountain View CA 94043 USA;

    Stevens Inst Technol Sch Syst Engn Hoboken NJ 07030 USA;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号