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Training deep learning (DL) models in the cloud has become a norm. With the emergence of serverless computing and its benefits of true pay-as-you-go pricing and scalability, systems researchers have recently started to provide support for serverless-based training. However, the ability to train DL models on serverless platforms is hindered by the resource limitations of today's serverless infrastructure and DL models' explosive requirement for memory and bandwidth. This paper describes FuncPipe, a novel pipelined training framework specifically designed for serverless platforms that enable fast and low-cost training of DL models. FuncPipe is designed with the key insight that model partitioning can be leveraged to bridge both memory and bandwidth gaps between the capacity of serverless functions and the requirement of DL training. Conceptually simple, we have to answer several design questions, including how to partition the model, configure each serverless function, and exploit each function's uplink/downlink bandwidth. In particular, we tailor a micro-batch scheduling policy for the serverless environment, which serves as the basis for the subsequent optimization. Our Mixed-Integer Quadratic Programming formulation automatically and simultaneously configures serverless resources and partitions models to fit within the resource constraints. Lastly, we improve the bandwidth efficiency of storage-based synchronization with a novel pipelined scatter-reduce algorithm. We implement FuncPipe on two popular cloud serverless platforms and show that it achieves 7%-77% cost savings and 1.3X-2.2X speedup compared to state-of-the-art serverless-based frameworks. 

Video streaming commonly uses Dynamic Adaptive Streaming over HTTP (DASH) to deliver good Quality of Experience (QoE) to users. Videos used in DASH are predominantly encoded by single-layered video coding such as H.264/AVC. In comparison, multi-layered video coding such as H.264/SVC provides more flexibility for up- grading the quality of buffered video segments and has the potential to further improve QoE. However, there are two challenges for us- ing SVC in DASH: (i) the complexity in designing ABR algorithms; and (ii) the negative impact of SVC’s coding overhead. In this work, we propose a deep reinforcement learning method called Grad for designing ABR algorithms that take advantage of the quality up- grade mechanism of SVC. Additionally, we quantify the impact of coding overhead on the achievable QoE of SVC in DASH, and propose jump-enabled hybrid coding (HYBJ) to mitigate the impact. Through emulation, we demonstrate that Grad-HYBJ, an ABR algo- rithm for HYBJ learned by Grad, outperforms the best performing state-of-the-art ABR algorithm by 17% in QoE.