Reproducible Performance Optimization of Complex Applications on the Edge-to-Cloud Continuum

Abstract

In more and more application areas, we are witnessing the emergence of complex workflows that combine computing, analytics and learning. They often require a hybrid execution infrastructure with IoT devices interconnected to cloud/HPC systems (aka Computing Continuum). Such workflows are subject to complex constraints and requirements in terms of performance, resource usage, energy consumption and financial costs. This makes it challenging to optimize their configuration and deployment.We propose a methodology to support the optimization of reallife applications on the Edge-to-Cloud Continuum. We implement it as an extension of E2Clab, a previously proposed framework supporting the complete experimental cycle across the Edge-to-Cloud Continuum. Our approach relies on a rigorous analysis of possible configurations in a controlled testbed environment to understand their behaviour and related performance tradeoffs. We illustrate our methodology by optimizing Pl@ntNet, a world-wide plant identification application. Our methodology can be generalized to other applications in the Edge-to-Cloud Continuum.

Enabling Reproducible Analysis of Complex Workflows on the Edge-to-Cloud Continuum

“…We illustrate [4] E2Clab usage with a real-life Smart Surveillance System deployed on the Grid'5000 testbed, showing that our framework allows one to understand how the Cloud-centric and the hybrid Edge-Cloud processing approaches impact performance metrics such as latency and throughput. Besides, we validate [7] E2Clab with Pl@ntNet, another real-life use case. We demonstrate that E2Clab guides on the optimization of the Pl@ntNet performance based on the analysis of the parameter settings and correlation to processing time and resource usage.…”

“…A large scale experimental validation on the G5K [5] testbed with Pl@ntNet [6], a real-life use case. E2Clab allows optimizing the Pl@ntNet's performance based on the analysis of the parameter settings and correlation to processing time and resource usage [7].…”

Distributed intelligence on the Edge-to-Cloud Continuum: A systematic literature review

“…In [123], the authors propose a novel framework that allows the deployment and optimization [121] of Big Data Analytics applications on the Edge-to-Cloud continuum. They illustrate and validate the framework with a smart surveillance application composed by data processing frameworks such as Edgent (on the Edge) and Apache Flink and Kafka (on the Cloud).…”

“…E2Clab [123] is a framework that implements a rigorous methodology for designing experiments with real-world workloads on the Edge-to-Cloud Continuum. E2Clab provides guidelines to move from real-world use cases to the design of relevant testbed setups for reproducible experiments enabling researchers to understand and optimize [121] the performance of applications. The key features provided by E2Clab are [122]: (1) reproducible experiments; (2) the mapping of applications parts executed across the computing continuum with the physical testbed; (3) the support for experiment variation and transparent scaling of the scenario; (4) network emulation to define Edge-to-Cloud communication constraints; (5) experiment deployment, monitoring and backup of results; and (6) the application optimization.…”

“…The testbed allows IoT native experimentation (e.g. wireless sensor network experi- small scale [169,77,31,86,167,67,151,39,128,37,60,170,97,5,57,3,161,62,80,50,76,43,12,99], medium scale [38,111,127,129,36,56,157], and large scale [66,123,121] ments) and service provision experiments (e.g. applications using real-time real-world sensor data).…”