Distributed intelligence on the Edge-to-Cloud Continuum: A systematic
literature review
- URL: http://arxiv.org/abs/2205.01081v1
- Date: Fri, 29 Apr 2022 08:06:05 GMT
- Title: Distributed intelligence on the Edge-to-Cloud Continuum: A systematic
literature review
- Authors: Daniel Rosendo (KerData), Alexandru Costan (KerData), Patrick
Valduriez (ZENITH), Gabriel Antoniu (KerData)
- Abstract summary: This review aims at providing a comprehensive vision of the main state-of-the-art libraries and frameworks for machine learning and data analytics available today.
The main simulation, emulation, deployment systems, and testbeds for experimental research on the Edge-to-Cloud Continuum available today are also surveyed.
- Score: 62.997667081978825
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The explosion of data volumes generated by an increasing number of
applications is strongly impacting the evolution of distributed digital
infrastructures for data analytics and machine learning (ML). While data
analytics used to be mainly performed on cloud infrastructures, the rapid
development of IoT infrastructures and the requirements for low-latency, secure
processing has motivated the development of edge analytics. Today, to balance
various trade-offs, ML-based analytics tends to increasingly leverage an
interconnected ecosystem that allows complex applications to be executed on
hybrid infrastructures where IoT Edge devices are interconnected to Cloud/HPC
systems in what is called the Computing Continuum, the Digital Continuum, or
the Transcontinuum.Enabling learning-based analytics on such complex
infrastructures is challenging. The large scale and optimized deployment of
learning-based workflows across the Edge-to-Cloud Continuum requires extensive
and reproducible experimental analysis of the application execution on
representative testbeds. This is necessary to help understand the performance
trade-offs that result from combining a variety of learning paradigms and
supportive frameworks. A thorough experimental analysis requires the assessment
of the impact of multiple factors, such as: model accuracy, training time,
network overhead, energy consumption, processing latency, among others.This
review aims at providing a comprehensive vision of the main state-of-the-art
libraries and frameworks for machine learning and data analytics available
today. It describes the main learning paradigms enabling learning-based
analytics on the Edge-to-Cloud Continuum. The main simulation, emulation,
deployment systems, and testbeds for experimental research on the Edge-to-Cloud
Continuum available today are also surveyed. Furthermore, we analyze how the
selected systems provide support for experiment reproducibility. We conclude
our review with a detailed discussion of relevant open research challenges and
of future directions in this domain such as: holistic understanding of
performance; performance optimization of applications;efficient deployment of
Artificial Intelligence (AI) workflows on highly heterogeneous infrastructures;
and reproducible analysis of experiments on the Computing Continuum.
Related papers
- Data Analysis in the Era of Generative AI [56.44807642944589]
This paper explores the potential of AI-powered tools to reshape data analysis, focusing on design considerations and challenges.
We explore how the emergence of large language and multimodal models offers new opportunities to enhance various stages of data analysis workflow.
We then examine human-centered design principles that facilitate intuitive interactions, build user trust, and streamline the AI-assisted analysis workflow across multiple apps.
arXiv Detail & Related papers (2024-09-27T06:31:03Z) - CityGPT: Towards Urban IoT Learning, Analysis and Interaction with Multi-Agent System [4.612237040042468]
CityGPT employs three agents to accomplish thetemporal analysis of IoT data.
We have agnentized the framework, facilitated by a large language model (LLM), to increase the data comprehensibility.
Our evaluation results on real-world data with different time show that the CityGPT framework can guarantee robust performance in computing.
arXiv Detail & Related papers (2024-05-23T15:27:18Z) - Multivariate Time Series characterization and forecasting of VoIP
traffic in real mobile networks [9.637582917616703]
Predicting the behavior of real-time traffic (e.g., VoIP) in mobility scenarios could help the operators to better plan their network infrastructures.
This work proposes a forecasting analysis of crucial/QoE descriptors of VoIP traffic in a real mobile environment.
arXiv Detail & Related papers (2023-07-13T09:21:39Z) - SOLIS -- The MLOps journey from data acquisition to actionable insights [62.997667081978825]
In this paper we present a unified deployment pipeline and freedom-to-operate approach that supports all requirements while using basic cross-platform tensor framework and script language engines.
This approach however does not supply the needed procedures and pipelines for the actual deployment of machine learning capabilities in real production grade systems.
arXiv Detail & Related papers (2021-12-22T14:45:37Z) - Reproducible Performance Optimization of Complex Applications on the
Edge-to-Cloud Continuum [55.6313942302582]
We propose a methodology to support the optimization of real-life applications on the Edge-to-Cloud Continuum.
Our approach relies on a rigorous analysis of possible configurations in a controlled testbed environment to understand their behaviour.
Our methodology can be generalized to other applications in the Edge-to-Cloud Continuum.
arXiv Detail & Related papers (2021-08-04T07:35:14Z) - A Procedural World Generation Framework for Systematic Evaluation of
Continual Learning [2.599882743586164]
We introduce a computer graphics simulation framework that repeatedly renders only upcoming urban scene fragments.
At its core lies a modular parametric generative model with adaptable generative factors.
arXiv Detail & Related papers (2021-06-04T16:31:43Z) - FENXI: Deep-learning Traffic Analytics at the Edge [69.34903175081284]
We present FENXI, a system to run complex analytics by leveraging TPU.
FENXI decouples operations and traffic analytics which operates at different granularities.
Our analysis shows that FENXI can sustain forwarding line rate traffic processing requiring only limited resources.
arXiv Detail & Related papers (2021-05-25T08:02:44Z) - Reliable Fleet Analytics for Edge IoT Solutions [0.0]
We propose a framework for facilitating machine learning at the edge for AIoT applications.
The contribution is an architecture that includes services, tools, and methods for delivering fleet analytics at scale.
We present a preliminary validation of the framework by performing experiments with IoT devices on a university campus's rooms.
arXiv Detail & Related papers (2021-01-12T11:28:43Z) - Edge-assisted Democratized Learning Towards Federated Analytics [67.44078999945722]
We show the hierarchical learning structure of the proposed edge-assisted democratized learning mechanism, namely Edge-DemLearn.
We also validate Edge-DemLearn as a flexible model training mechanism to build a distributed control and aggregation methodology in regions.
arXiv Detail & Related papers (2020-12-01T11:46:03Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.