Interactive app traffic: An action-based model and data-driven analysis
Interactive app traffic: An action-based model and data-driven analysis
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交互式应用程序流量：基于操作的模型和数据驱动的分析
DOI：
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
A. Sabharwal
中科院分区：
文献类型：
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作者：
John Tadrous;A. Sabharwal
许多流行的智能手机应用程序在整个会话中涉及人机交互;例如用于网页浏览、预订和在线游戏的应用程序。在这项工作中，我们的特点是双向互动的应用程序流量在秒的时间尺度，这是由人类互动的形状。我们收集并分析了一个包含1500个交互式应用程序会话的数据集。组合的上行链路和下行链路流量突发是用户-服务器交互的结果，我们将其标记为动作。在每个动作中，我们发现上行链路和下行链路数据包的数量之间的高度相关性达到0.98。我们的研究表明，动作持续时间和到达间隔的分布可以用指数或伽玛分布来近似。该分析提供了在应用会话期间与动作相关联的双向分组突发的时间特性的见解。我们发现，基于行动的服务在接入点（AP），行动构成的服务单元，而不是数据包，可以减少50%的服务延迟。
Many popular smartphone apps involve human interaction through the entire session; e.g. apps for web browsing, making reservations and online gaming. In this work, we characterize bi-directional interactive app traffic in the timescale of seconds, that is shaped by the human interaction. We collect and analyze a dataset comprising 1500 interactive app sessions. The combined uplink and downlink traffic bursts are the outcome of user-server interactions, which we label as actions. Within each action, we discover high correlation between the number of uplink and downlink packets reaching 0.98. Our study reveals that the distribution of action duration and interarrival can be approximated with exponential or gamma distributions. The analysis provides insights on the temporal characteristics of bi-directional packet bursts associated with actions, during an app session. We show that action-based service at access points (APs), where actions constitute the service units rather than packets, can reduce service delay by 50%.