Microservices promise the benefits of services with an efficient granularity using dynamically allocated resources. In the current evolving architectures, data producers and consumers are created as decoupled components that support different data objects and quality of service. Actual implementations of service meshes lack support for data-driven paradigms, and focus on goal-based approaches designed to fulfill the general system goal. This diversity of available components demands the integration of users requirements and data products into the discovery mechanism. This paper proposes a data-driven service discovery framework based on profile matching using data-centric service descriptions. We have designed and evaluated a microservices architecture for providing service meshes with a standalone set of components that manages data profiles and resources allocations over multiple geographical zones. Moreover, we demonstrated an adaptation scheme to provide quality of service guarantees. Evaluation of the implementation on a real life testbed shows effectiveness of this approach with stable and fluctuating request incoming rates.
Deep Learning has shifted the focus of traditional batch workflows to data-driven feature engineering on streaming data. In particular, the execution of Deep Learning workflows presents expectations of near-real-time results with user-defined acceptable accuracy. Meeting the objectives of such applications across heterogeneous resources located at the edge of the network, the core, and in-between requires managing trade-offs between the accuracy and the urgency of the results. However, current data analysis rarely manages the entire Deep Learning pipeline along the data path, making it complex for developers to implement strategies in realworld deployments. Driven by an object detection use case, this paper presents an architecture for time-critical Deep Learning workflows by providing a data-driven scheduling approach to distribute the pipeline across Edge to Cloud resources. Furthermore, it adopts a data management strategy that reduces the resolution of incoming data when potential trade-off optimizations are available. We illustrate the system's viability through a performance evaluation of the object detection use case on the Grid'5000 testbed. We demonstrate that in a multi-user scenario, with a standard frame rate of 25 frames per second, the system speed-up data analysis up to 54.4% compared to a Cloud-only-based scenario with an analysis accuracy higher than a fixed threshold.
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