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Data-driven Management Solution for Microservice-based Deep Learning Applications

Abstract : We live in a new era of Big Data, the era of insights. While our capacity to collect real-time data has grown significantly over the past decade, our ability to analyze that data to turn it into knowledge has not kept pace. With new generations of devices and network technologies, the focus of Big Data is shifting toward the design of tools and applications able to extract information from collected data. The majority of emerging applications present expectations of near-real-time processing to maintain the validity of their results. However, guaranteeing their performance requirements is hampered by the traditional Cloud system designs and management strategies. Current systems for Big Data applications rely on heterogeneous resources distributed across the constrained Edge and the powerful Cloud. In addition, the applications are now created as a set of self-contained microservices, developed by independent teams following the DevOps practices. This evolution of systems designs has introduced extreme heterogeneity and uncertainty into emerging applications, highlighting the limitations of traditional management strategies.In this thesis, we focus on designing a system for Big Data applications that rethinks existing management strategies with a particular emphasis on the heterogeneity of incoming data, applications, and resources. We first study the decoupling of data producers and consumers in emerging microservice-based applications as the entry point to effectively leverage available services, even newly published ones. Accordingly, we propose a data-driven service discovery framework based on data-centric service descriptions and rely on a Peer-to-Peer data-driven architecture. In addition, we present an adaptation scheme that scales deployed microservices to tackle the impact of fluctuating load on real-time performance. Second, we investigate the trade-off between the quality and urgency of the results in Big Data applications as a promising strategy to overcome the limited and heterogeneous capacity of system resources. In particular, we present a data-driven workflow scheduling approach to distribute microservices across the edge of the network, the core, and along the data path. Additionally, we propose a data adaptation strategy that reduces the quality of incoming data when potential quality-latency trade-off optimizations are available. We then apply the proposed approaches in the context of Deep Learning applications.
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Submitted on : Tuesday, March 1, 2022 - 4:17:08 PM
Last modification on : Thursday, May 12, 2022 - 5:08:02 PM
Long-term archiving on: : Monday, May 30, 2022 - 7:42:39 PM


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  • HAL Id : tel-03593003, version 1


Zeina Houmani. Data-driven Management Solution for Microservice-based Deep Learning Applications. Other [cs.OH]. Université de Lyon, 2021. English. ⟨NNT : 2021LYSEN092⟩. ⟨tel-03593003⟩



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