TrIMS: Transparent and Isolated Model Sharing for Low Latency Deep LearningInference in Function as a Service Environments

Deep neural networks (DNNs) have become core computation components within low latency Function as a Service (FaaS) prediction pipelines: including image recognition, object detection, natural language processing, speech synthesis, and personalized recommendation pipelines. Cloud computing, as the de-facto backbone of modern computing infrastructure for both enterprise and consumer applications, has to be able to handle user-defined pipelines of diverse DNN inference workloads while maintaining isolation and latency guarantees, and minimizing resource waste. The current solution for guaranteeing isolation within FaaS is suboptimal -- suffering from "cold start" latency. A major cause of such inefficiency is the need to move large amount of model data within and across servers. We propose TrIMS as a novel solution to address these issues. Our proposed solution consists of a persistent model store across the GPU, CPU, local storage, and cloud storage hierarchy, an efficient resource management layer that provides isolation, and a succinct set of application APIs and container technologies for easy and transparent integration with FaaS, Deep Learning (DL) frameworks, and user code. We demonstrate our solution by interfacing TrIMS with the Apache MXNet framework and demonstrate up to 24x speedup in latency for image classification models and up to 210x speedup for large models. We achieve up to 8x system throughput improvement.Comment: In Proceedings CLOUD 201

Does the Concept of “Community of Practice” Show New Trajectories for the Evolution of Industrial Districts?

The aim of this paper is to find a framework that could be useful to evaluate the utility of the concept of “Community of Practice” (CoP) for understanding the dynamics of knowledge creation and sharing in Industrial Districts (IDs). The CoP concept stems from the managerial experience of large corporations, which have found in it a kind of “living repository” of knowledge. The source of the concept of agglomeration of firms in ID is completely different. Anyway, many similarities can be found between the concepts of ID and CoP, as well then some differences. The paper proceeds as follows. First, it explains the three main concepts useful for understanding further argumentations: knowledge, ID, CoP. Next, it offers a framework to put in comparison the two concepts of ID and CoP. In the end, an example of how the applications of tools, coming from the CoP concept, can be useful to formulate some hypotheses on the evolutionary behaviour of IDs is shown.Community of Practice, Industrial District, Knowledge, Learning