E2E: embracing user heterogeneity to improve quality of experience on the web
E2E: embracing user heterogeneity to improve quality of experience on the web
复制标题
DOI：
10.1145/3341302.3342089
复制
发表时间：
2019-08
期刊：
影响因子：
--
通讯作者：
Xu Zhang;S. Sen;D. Kurniawan;Haryadi S. Gunawi;Junchen Jiang
中科院分区：
文献类型：
--
作者：
Xu Zhang;S. Sen;D. Kurniawan;Haryadi S. Gunawi;Junchen Jiang
传统观点认为，为了提高体验质量(QOE)，网络服务提供商应该减少服务器端延迟的中位数或其他百分比。这项工作表明，由于用户在延迟如何影响QOE方面的异质性，这样做可能是低效的。从QOE的角度来看，即使在到达服务的相同请求之间，请求对延迟的敏感度也可能有很大差异，因为它们在到达服务之前经历的广域网络延迟不同。换句话说，节省50ms的服务器端延迟会对不同的用户产生不同的影响。本文提出了第一个包含用户异构性的服务器端资源分配系统E2E，该系统能够以QOE感知的方式分配服务器端的资源。利用这种异构性面临着一个独特的挑战：与Web请求的其他应用级属性(例如，用户的订阅类型)不同，请求对服务器端延迟的QOE敏感性不能预先确定，因为它取决于延迟本身，而延迟本身是由资源分配决策和传入请求确定的。这种循环依赖使问题在计算上变得困难。我们做出了三项贡献：(1)基于来自微软云规模生产网络框架的端到端跟踪以及对Amazon MTurk的用户研究，利用用户异质性来改善QOE；(2)解决上述循环依赖的新资源分配策略；以及(3)以几乎可以忽略的开销实现高效的系统实现。我们将E2E应用于两个开源系统：Cassandra中的副本选择和RabbitMQ中的消息调度。使用TRACE和我们的试验台部署，我们表明，E2E可以在不降低QOE的情况下，将QOE(例如，用户参与度持续时间)提高28%，或服务40%以上的并发请求。
Conventional wisdom states that to improve quality of experience (QoE), web service providers should reduce the median or other percentiles of server-side delays. This work shows that doing so can be inefficient due to user heterogeneity in how the delays impact QoE. From the perspective of QoE, the sensitivity of a request to delays can vary greatly even among identical requests arriving at the service, because they differ in the wide-area network latency experienced prior to arriving at the service. In other words, saving 50ms of server-side delay affects different users differently. This paper presents E2E, the first resource allocation system that embraces user heterogeneity to allocate server-side resources in a QoE-aware manner. Exploiting this heterogeneity faces a unique challenge: unlike other application-level properties of a web request (e.g., a user's subscription type), the QoE sensitivity of a request to server-side delays cannot be pre-determined, as it depends on the delays themselves, which are determined by the resource allocation decisions and the incoming requests. This circular dependence makes the problem computationally difficult. We make three contributions: (1) a case for exploiting user heterogeneity to improve QoE, based on end-to-end traces from Microsoft's cloud-scale production web framework, as well as a user study on Amazon MTurk; (2) a novel resource allocation policy that addresses the circular dependence mentioned above; and (3) an efficient system implementation with almost negligible overhead. We applied E2E to two open-source systems: replica selection in Cassandra and message scheduling in RabbitMQ. Using traces and our testbed deployments, we show that E2E can increase QoE (e.g., duration of user engagement) by 28%, or serve 40% more concurrent requests without any drop in QoE.