Adaptive Function Launching Acceleration in Serverless Computing Platforms Serverless computing has emerged as a new compelling paradigm for the deployment of applications and services, which enables developers to focus more on business logic rather than on infrastructure. Serverless computing platform enables the function container scales to zero, which results in a serious problem called cold start. Cold start severely affects the responsiveness of serverless computing platform and limits the use and adoption of serverless computing by a broader range of applications. The traditional strategies reduce the cold start latency at the expense of resources. How to simultaneously minimize the cold start latency and reduce the resources consumption of strategy implementation is a challenging problem. In this paper, we firstly propose an Adaptive WarmUp Strategy (AWUS) to predict the function invoking time and warm up the functions, thus reducing the cold start latency. We use the function chain model to improve the AWUS. We adopt a finegrained regression method to predict nonfirst functions in the function chain more accurately. Secondly, we propose an Adaptive Container Pool Scaling Strategy (ACPSS) to reduce the function launching time. We dynamically adjust the capacity of the container pool to reduce the resources waste. The AWUS and ACPSS work together to reduce the cold start latency and the resources waste. Finally, we implement a serverless computing platform and conduct extensive experiments to evaluate our strategy. The evaluation results demonstrate the effectiveness of our strategies.

Accelerated serverless computing based on GPU virtualization Abstract   This paper introduces a platform to support serverless computing for scalable eventdriven data processing that features a multilevel elasticity approach combined with virtualization of GPUs. The platform supports the execution of applications based on Docker containers in response to file uploads to a data storage in order to perform the data processing in parallel. This is managed by an elastic Kubernetes cluster whose size automatically grows and shrinks depending on the number of files to be processed. To accelerate the processing time of each file, several approaches involving virtualized access to GPUs, either locally or remote, have been evaluated. A use case that involves the inference based on deep learning techniques on transtoracic echocardiography imaging has been carried out to assess the benefits and limitations of the platform. The results indicate that the combination of serverless computing and GPU virtualization introduce an efficient and costeffective eventdriven accelerated computing approach that can be applied for a wide variety of scientific applications.