WISEFUSE: Workload Characterization and DAG Transformation for Serverless Workflows
WISEFUSE: Workload Characterization and DAG Transformation for Serverless Workflows
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WISEFUSE：无服务器工作流的工作负载特征和 DAG 转换
DOI：
10.1145/3489048.3530959
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发表时间：
2022
期刊：
影响因子：
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通讯作者：
Chaterji, Somali
中科院分区：
文献类型：
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作者：
Mahgoub, Ashraf;Yi, Edgardo Barsallo;Shankar, Karthick;Minocha, Eshaan;Elnikety, Sameh;Bagchi, Saurabh;Chaterji, Somali
我们描述了一家主要云提供商的无服务器DAG的生产工作负载。我们的分析强调了限制性能的两个主要因素：（a）DAG中的无服务器函数之间缺乏有效的通信方法，以及（B）当DAG阶段调用一组必须在开始下一个DAG阶段之前完成的并行函数时，会出现落伍者。为了解决这些限制，我们提出了WISEFUSE，这是一种自动化方法，可以为用户指定的延迟目标或预算生成无服务器DAG的优化执行计划。我们引入三个优化：（1）融合将串联功能组合在单个VM中以减少级联功能之间的通信开销。(2)捆绑在一个VM中执行一组函数的并行调用，以改善并行工作者之间的资源共享，从而减少偏斜。(3)资源分配将正确的VM大小分配给DAG中的每个功能或功能包，以减少E2E延迟和成本。我们实现WISEFUSE，使用三种流行的无服务器应用程序进行实验评估，这些应用程序具有不同的DAG结构，内存占用和中间数据大小。与竞争方法和其他替代方案相比，WISEFUSE在E2E延迟和成本方面有显着改善。具体来说，对于机器学习管道，WISEFUSE在不增加成本的情况下实现了P95延迟，比Photons低67%，比Faastlane低39%，比SONIC低90%。
We characterize production workloads of serverless DAGs at a major cloud provider. Our analysis highlights two major factors that limit performance: (a) lack of efficient communication methods between the serverless functions in the DAG, and (b) stragglers when a DAG stage invokes a set of parallel functions that must complete before starting the next DAG stage. To address these limitations, we propose WISEFUSE, an automated approach to generate an optimized execution plan for serverless DAGs for a user-specified latency objective or budget. We introduce three optimizations: (1) Fusion combines in-series functions together in a single VM to reduce the communication overhead between cascaded functions. (2) Bundling executes a group of parallel invocations of a function in one VM to improve resource sharing among the parallel workers to reduce skew. (3) Resource Allocation assigns the right VM size to each function or function bundle in the DAG to reduce the E2E latency and cost. We implement WISEFUSE to evaluate it experimentally using three popular serverless applications with different DAG structures, memory footprints, and intermediate data sizes. Compared to competing approaches and other alternatives, WISEFUSE shows significant improvements in E2E latency and cost. Specifically, for a machine learning pipeline, WISEFUSE achieves P95 latency that is 67% lower than Photons, 39% lower than Faastlane, and 90% lower than SONIC without increasing the cost.