E2E: Embracing User Heterogeneity to Improve Quality of Experience on the Web - Zhang, Xu; Sen, Siddhartha; Kurniawan, Daniar; Gunawi, Haryadi; Jiang, Junchen (ACM Special Interest Group on Data Communication (SIGCOMM), 2019)
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 dier 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.