Serverless computing and a function execution model, Function-as-a-Service (FaaS), are currently receiving considerable attention from both academia and industry. One of the reasons for the success of serverless computing is its straightforward interface that abstracts complex internals of cloud computing resource usage and configurations. However, this approach may result in hiding too much information about how underlying cloud resources would work, entailing that users cannot predict how their applications will perform, especially for IO-heavy ones. To address this issue, we evaluate several aspects of network and disk IO performance with realistic workloads using public FaaS systems. Our analysis reveals that current public FaaS systems do not provide appropriate levels of IO performance differentiation, and the ability to isolate network resource allocation during concurrent execution is rarely offered by service providers. Based on the results presented in this paper, we insist that it must be mandatory for network and disk IO resource performance of FaaS to be more visible and predictable, as is the case for memory and CPU, in order to expand serverless computing applications to data-intensive ones.

无服务器计算和功能执行模型，即服务即服务（FaaS），目前正受到学术界和行业的广泛关注。无服务器计算成功的原因之一是其简单的界面，该界面抽象了云计算资源使用和配置的复杂内部。但是，这种方法可能会隐藏太多有关底层云资源将如何工作的信息，这可能导致用户无法预测其应用程序的性能，尤其是对于IO繁重的应用程序。为解决此问题，我们使用公共FaaS系统评估具有实际工作负载的网络和磁盘IO性能的多个方面。我们的分析表明，当前的公共FaaS系统无法提供适当级别的IO性能差异，服务提供商很少提供在并发执行期间隔离网络资源分配的功能。根据本文提出的结果，我们坚持必须强制FaaS的网络和磁盘IO资源性能更加可见和可预测（如内存和CPU），以便将无服务器计算应用程序扩展到数据密集的。