搜索广告系统的工作负载分析和缓存策略

Conglong Li, D. Andersen, Qiang Fu, S. Elnikety, Yuxiong He
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引用次数: 7

摘要

搜索广告依赖于对用户行为和兴趣的准确预测,目前使用复杂且计算成本高昂的机器学习算法来完成,这些算法可以估计每个搜索查询中数千个候选广告的潜在收入。这种估算的准确性对收入很重要,但这些计算的成本代表了一笔可观的费用,例如,占总收入的10%到30%。缓存以前的计算结果是减少这种开销的潜在途径,但是传统的领域不可知和收入不可知的方法会导致大量的收入损失。本文提出了三个领域特定的缓存机制,成功地针对这两个因素进行了优化。对必应广告系统的跟踪模拟显示,传统的缓存可以降低27.7%的成本,但对收入的负面影响高达-14.1%。另一方面,拟议的机制可以将成本降低20.6%,同时将收入影响限制在-1.3%至0%之间。根据微软2016财年第四季度的财报,传统缓存将使必应广告的净利润减少8490美元至1.661亿美元,而我们提议的缓存将使净利润增加111美元至7150万美元。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Workload analysis and caching strategies for search advertising systems
Search advertising depends on accurate predictions of user behavior and interest, accomplished today using complex and computationally expensive machine learning algorithms that estimate the potential revenue gain of thousands of candidate advertisements per search query. The accuracy of this estimation is important for revenue, but the cost of these computations represents a substantial expense, e.g., 10% to 30% of the total gross revenue. Caching the results of previous computations is a potential path to reducing this expense, but traditional domain-agnostic and revenue-agnostic approaches to do so result in substantial revenue loss. This paper presents three domain-specific caching mechanisms that successfully optimize for both factors. Simulations on a trace from the Bing advertising system show that a traditional cache can reduce cost by up to 27.7% but has negative revenue impact as bad as -14.1%. On the other hand, the proposed mechanisms can reduce cost by up to 20.6% while capping revenue impact between -1.3% and 0%. Based on Microsoft's earnings release for FY16 Q4, the traditional cache would reduce the net profit of Bing Ads by $84.9 to $166.1 million in the quarter, while our proposed cache could increase the net profit by $11.1 to $71.5 million.
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