Cioppino: Multi-Tenant Crowd Management
Cioppino: Multi-Tenant Crowd Management
复制标题
Cioppino：多租户人群管理
DOI：
--
复制
发表时间：
2017
期刊：
影响因子：
--
通讯作者：
M. Franklin
中科院分区：
文献类型：
--
作者：
D. Haas;M. Franklin
在数据分析系统中嵌入人工计算可以提高分析质量，但会显著影响系统的端到端成本和性能。众包系统中最近的工作试图优化性能，但重点是运行同质任务的单个应用程序。在这项工作中，我们介绍Cioppino，一个系统，占人为因素，可以影响性能时，并行运行多个应用程序。Cioppino使用了一个扩展模型来表示工作池，并利用云计算中使用的自动缩放技术来自适应地调整池的大小。它的模型还考虑了工人放弃，并自动在应用程序之间转移工人，以提高性能，并将工人与他们最喜欢的任务相匹配。我们对Cioppino在模拟中的评估从亚马逊的Mechanical Turk上运行的实时人群系统中提取的痕迹表明，与最先进的人群管理策略相比，成本降低了19倍，吞吐量增加了20%，工人对分配任务的偏好增加了2倍。
Embedding human computation in systems for data analysis improves the quality of the analysis, but can significantly impact the end-to-end cost and performance of the system. Recent work in crowdsourcing systems attempts to optimize for performance, but focuses on single applications running homogeneous tasks. In this work, we introduce Cioppino, a system that accounts for human factors that can affect performance when running multiple applications in parallel. Cioppino uses a queueing model to represent the pool of workers, and leverages techniques for autoscaling used in cloud computing to adaptively adjust the pool size. Its model also accounts for worker abandonment, and automatically shifts workers between applications to improve performance and match workers with tasks they enjoy most. Our evaluation of Cioppino in simulation on traces extracted from a realtime crowd system running on Amazon’s Mechanical Turk demonstrates a 19X reduction in cost, a 20% increase in throughput, and a 2X increase in worker preference for assigned tasks as compared to state-of-the-art crowd management strategies.