Real-time Serverless : Cloud Resource Management for Bursty , Real-time Workloads

Today’s cloud resource offerings provide no guarantees for resource allocation, so bursty application must reserve, and pay for resources they do not use to achieve real-time guarantees. We propose a new type of cloud resource, Realtime Serverless (RTS) with a new service-level objective – guaranteed allocation rate. This guarantee enables timely resource allocation, enabling applications to achieve real-time performance efficiently. With a simple burst model, we study real-time serverless analytically, exploring their effect on application quality, guarantees, and cost. Next, we simulate statistically varying bursts and higher loads (multi-application), to study the impact of real-time serverless. In both analytic and simulation studies, adding guaranteed allocation rate enables bursty, real-time applications to achieve guaranteed high quality cost-effectively. Specifically, for a desired application quality, the required allocation rates can be determined. In addition, for duty factors from 0.025 to 0.25, the value of real-time serverless to the application is > 4x than traditional. Further results show that multiple applications can share real-time serverless efficiently, supporting duty factor increases of 25x with only a 1.6x increase in allocation rate (provider resource cost). Finally, we present a case study of a traffic monitoring application. Despite more complex burst statistics, our results show major benefits for cost and application quality. Application benefit makes realtime serverless worth nearly 16x virtual machine resources for delivering application value.