Deploying distributed training workloads of deep learning models atop serverless architecture alleviates the burden of managing servers from deep learning practitioners. However, when supporting deep model training, the current serverless architecture faces the challenges of inefficient communication patterns and rigid resource configuration that incur subpar and unpredictable training performance. In this paper, we proposeFasDL, an efficient serverless-based deep learning training architecture to solve these two challenges.FasDLadopts a novel training frameworkK-REDUCEto release the communication overhead and accelerate the training. Additionally, FasDL builds a lightweight mathematical model forK-REDUCEtraining, offering predictable performance and supporting subsequent resource configuration. It achieves the optimal resource configuration by formulating an optimization problem related to system-level and application-level parameters and solving it with a pruning-based heuristic search algorithm. Extensive experiments on AWS Lambda verify a prediction accuracy over 94% and demonstrate performance and cost advantages over the state-of-art architecture LambdaML by up to 16.8% and 28.3% respectively.

With the advance of smart manufacturing and information technologies, the volume of data to process is increasing accordingly. Current solutions for big data processing resort to distributed stream processing systems, such as Apache Flink and Spark. However, such frameworks face challenges of resource underutilization and high latency in big data application scenarios. In this article, we propose SPSC, a serverless-based stream computing framework where events are discretized into the atomic stream and stateless Lambda functions are taken as context-irrelevant operators, achieving task parallelism and inherent data parallelism in processing. Also, we implement a prototype of the framework on Amazon Web service (AWS) using AWS Lambda, AWS simple queue service, and AWS DynamoDB. The evaluation shows that compared with Alibaba's real-time computing Flink version, SPSC outperforms by 10.12% when the overhead is close.