λ-NIC: Interactive Serverless Compute on SmartNICs
λ-NIC: Interactive Serverless Compute on SmartNICs
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λ-NIC：SmartNIC 上的交互式无服务器计算
DOI：
10.1145/3342280.3342341
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
M. Rosenblum
中科院分区：
文献类型：
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作者：
Sean Choi;M. Shahbaz;B. Prabhakar;M. Rosenblum
无服务器计算作为一种有吸引力的云计算模型正在兴起，它让开发人员只关注核心应用程序，将它们构建为小的、细粒度的工作负载（即lambda），而不必担心构建和/或管理它们运行的基础设施。云提供商动态地为这些工作负载提供、部署、修补和监控基础设施及其资源（例如，计算、存储、内存和网络）；租户只需以毫秒为单位为他们消耗的资源付费。云提供商通常严格限制单个工作负载可以消耗的计算时间和资源，以确保他们可以轻松部署和扩展每个工作负载，而不会影响其他工作负载的可用性。因此，工作负载的寿命很短，具有严格的计算时间和内存限制（对于Amazon Lambda[6]，分别最多为15分钟和3 GB），并且通常对延迟敏感。这些工作负载的一些示例包括实时流处理和通用API端点。今天，所有主要的云供应商都提供了某种形式的无服务器框架（图1），例如Amazon Lambda[3]、谷歌cloud Functions[9]和Microsoft Azure Functions[7]，以及OpenFaaS[13]和OpenWhisk[2]等开源开发。这些框架依赖于虚拟化和容器[10]来执行和扩展租户的lambda。这些技术旨在最大限度地利用提供商的物理基础设施，同时为每个租户提供完全隔离的机器的自己的视图。在无服务器计算中，服务器管理对租户是隐藏的，这些虚拟化技术变得冗余，不必要地增加了无服务器工作负载的代码大小，并导致处理延迟（数百毫秒）。
1 OVERVIEW Serverless compute is emerging as an attractive cloud computing model that lets developers focus only on the core applications, building them as small, fine-grained workloads (i.e., lambdas), without having to worry about building and/or managing the infrastructure they run on. Cloud providers dynamically provision, deploy, patch, and monitor the infrastructure and its resources (e.g., compute, storage, memory, and network) for these workloads; with tenants only paying for the resources they consume at millisecond increments. The cloud providers generally put a strict limit on the compute time and resource that can be consumed by a single workload, in order to ensure that they can easily deploy and scale each workload without impacting the availability of other workloads. Thus, the workloads are short-lived with strict compute time and memory limits (up to 15 minutes and 3 GB, respectively, for Amazon Lambda [6]) and are often latency sensitive. Some examples of these workloads include real-time stream processing and generic API endpoints. Today, all major cloud vendors offer some form of serverless frameworks (Figure 1), such as Amazon Lambda [3], Google Cloud Functions [9], and Microsoft Azure Functions [7], along with opensource developments like OpenFaaS [13] and OpenWhisk [2]. These frameworks rely on virtualization and containers [10] to execute and scale tenants’ lambdas. These technologies were designed to maximize utilization of the providers’ physical infrastructure, while presenting each tenant with its own view of a completely isolated machine. With serverless computing, where server management is hidden from tenants, these virtualization technologies become redundant, unnecessarily bloating the code size of serverless workloads, and causing processing delays (of hundreds of milliseconds)