Sun 14 Apr 2019 16:25 - 16:50 at Garden Room - Session3

Cloud-based Web services are shifting to the event-driven, scripting language-based programming model to achieve productivity, flexibility, and scalability. Implementations of this model, however, generally suffer from long tail latencies, which we measure using Node.js as a case study. Unlike in traditional thread-based systems, reducing long tails is difficult in event-driven systems due to their inherent asynchronous programming model. We propose a framework to identify and optimize tail latency sources in scripted event-driven Web services. We introduce profiling that allows us to gain deep insights into not only how asynchronous event-driven execution impacts application tail latency but also how the managed runtime system overhead exacerbates the tail latency issue further. Using the profiling framework, we propose an event-driven execution runtime design that orchestrates the hardware’s boosting capabilities to reduce tail latency. We achieve higher tail latency reductions with lower energy overhead than prior techniques that are unaware of the underlying event-driven program execution model. The lessons we derive from Node.js apply to other event-driven services based on scripting language frameworks.

Sun 14 Apr

16:00 - 18:05: Research Papers - Session3 at Garden Room
vee-2019-papers16:00 - 16:25
Thomas ShullUniversity of Illinois at Urbana-Champaign, Jian HuangUniversity of Illinois at Urbana-Champaign, Josep TorrellasUniversity of Illinois at Urbana-Champaign
vee-2019-papers16:25 - 16:50
Wenzhi CuiGoogle, Daniel RichinsThe University of Texas at Austin, Yuhao ZhuUniversity of Rochester, Vijay Janapa ReddiHarvard University
vee-2019-papers16:50 - 17:15
Juan FumeroUniversity of Manchester, UK, Michail PapadimitriouUniversity of Manchester, UK, Foivos S. ZakkakUniversity of Manchester, UK, Maria XekalakiUniversity of Manchester, UK, James ClarksonUniversity of Manchester, UK, Christos KotselidisUniversity of Manchester, UK
vee-2019-papers17:15 - 17:40
Dongyang WangUniversity of Science and Technology of China, China, Binzhang FuHuawei Technologies, n.n., Gang LuHuawei Technologies, n.n., Kun TanHuawei Technologies, n.n., Bei HuaHuawei Technologies, n.n. / University of Science and Technology of China, China
vee-2019-papers17:40 - 18:05
Li LiuGeorge Mason University, USA, Haoliang WangAdobe Research, USA, An WangCase Western Reserve University, USA, Mengbai XiaoOhio State University, USA, Yue ChengGeorge Mason University, USA, Songqing ChenGeorge Mason University, USA