Who Limits the Resource Efficiency of My Datacenter: An Analysis of Alibaba Datacenter Traces
Who Limits the Resource Efficiency of My Datacenter: An Analysis of Alibaba Datacenter Traces
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DOI：
10.1145/3326285.3329074
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发表时间：
2019-06
期刊：
影响因子：
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通讯作者：
Jing Guo;Zihao Chang;Sa Wang;Haiyang Ding;Yihui Feng;Liang Mao;Yungang Bao
中科院分区：
文献类型：
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作者：
Jing Guo;Zihao Chang;Sa Wang;Haiyang Ding;Yihui Feng;Liang Mao;Yungang Bao
云平台为最终用户和云运营商提供了极大的灵活性和成本效益。然而，现代物流企业资源利用率低，造成了硬件资源和基础设施投资的巨大浪费。为了提高资源利用率，一个简单的方法是将不同的工作负载放在同一个硬件上。为了计算资源效率并了解协同定位集群中工作负载的关键特征，我们分析了阿里巴巴生产跟踪中的8天跟踪。我们揭示了以下三个关键发现。首先，内存成为新的瓶颈，限制了阿里巴巴数据中心的资源效率。其次，为了保护延迟关键型应用程序，批处理应用程序被视为二等公民，并限制使用有限的资源。第三，超过90%的延迟关键型应用程序是用Java应用程序编写的。大规模自包含的JVM进一步使资源管理复杂化，并限制了数据中心的资源效率。
Cloud platform provides great flexibility and cost-efficiency for end-users and cloud operators. However, low resource utilization in modern datacenters brings huge wastes of hardware resources and infrastructure investment. To improve resource utilization, a straightforward way is co-locating different workloads on the same hardware. To figure out the resource efficiency and understand the key characteristics of workloads in co-located cluster, we analyze an 8-day trace from Alibaba's production trace. We reveal three key findings as follows. First, memory becomes the new bottleneck and limits the resource efficiency in Alibaba's datacenter. Second, in order to protect latency-critical applications, batch-processing applications are treated as second-class citizens and restricted to utilize limited resources. Third, more than 90% of latency-critical applications are written in Java applications. Massive self-contained JVMs further complicate resource management and limit the resource efficiency in datacenters.