42 research outputs found
EISim: A Platform for Simulating Intelligent Edge Orchestration Solutions
To support the stringent requirements of the future intelligent and
interactive applications, intelligence needs to become an essential part of the
resource management in the edge environment. Developing intelligent
orchestration solutions is a challenging and arduous task, where the evaluation
and comparison of the proposed solution is a focal point. Simulation is
commonly used to evaluate and compare proposed solutions. However, the
currently existing, openly available simulators are lacking in terms of
supporting the research on intelligent edge orchestration methods. To address
this need, this article presents a simulation platform called Edge Intelligence
Simulator (EISim), the purpose of which is to facilitate the research on
intelligent edge orchestration solutions. EISim is extended from an existing
fog simulator called PureEdgeSim. In its current form, EISim supports
simulating deep reinforcement learning based solutions and different
orchestration control topologies in scenarios related to task offloading and
resource pricing on edge. The platform also includes additional tools for
creating simulation environments, running simulations for agent training and
evaluation, and plotting results
How Can AI be Distributed in the Computing Continuum? Introducing the Neural Pub/Sub Paradigm
This paper proposes the neural publish/subscribe paradigm, a novel approach
to orchestrating AI workflows in large-scale distributed AI systems in the
computing continuum. Traditional centralized broker methodologies are
increasingly struggling with managing the data surge resulting from the
proliferation of 5G systems, connected devices, and ultra-reliable
applications. Moreover, the advent of AI-powered applications, particularly
those leveraging advanced neural network architectures, necessitates a new
approach to orchestrate and schedule AI processes within the computing
continuum. In response, the neural pub/sub paradigm aims to overcome these
limitations by efficiently managing training, fine-tuning and inference
workflows, improving distributed computation, facilitating dynamic resource
allocation, and enhancing system resilience across the computing continuum. We
explore this new paradigm through various design patterns, use cases, and
discuss open research questions for further exploration
Energy-Sustainable IoT Connectivity: Vision, Technological Enablers, Challenges, and Future Directions
Technology solutions must effectively balance economic growth, social equity,
and environmental integrity to achieve a sustainable society. Notably, although
the Internet of Things (IoT) paradigm constitutes a key sustainability enabler,
critical issues such as the increasing maintenance operations, energy
consumption, and manufacturing/disposal of IoT devices have long-term negative
economic, societal, and environmental impacts and must be efficiently
addressed. This calls for self-sustainable IoT ecosystems requiring minimal
external resources and intervention, effectively utilizing renewable energy
sources, and recycling materials whenever possible, thus encompassing energy
sustainability. In this work, we focus on energy-sustainable IoT during the
operation phase, although our discussions sometimes extend to other
sustainability aspects and IoT lifecycle phases. Specifically, we provide a
fresh look at energy-sustainable IoT and identify energy provision, transfer,
and energy efficiency as the three main energy-related processes whose
harmonious coexistence pushes toward realizing self-sustainable IoT systems.
Their main related technologies, recent advances, challenges, and research
directions are also discussed. Moreover, we overview relevant performance
metrics to assess the energy-sustainability potential of a certain technique,
technology, device, or network and list some target values for the next
generation of wireless systems. Overall, this paper offers insights that are
valuable for advancing sustainability goals for present and future generations.Comment: 25 figures, 12 tables, submitted to IEEE Open Journal of the
Communications Societ
Distributed AI in Zero-touch Provisioning for Edge Networks: Challenges and Research Directions
Zero-touch network is anticipated to inaugurate the generation of intelligent
and highly flexible resource provisioning strategies where multiple service
providers collaboratively offer computation and storage resources. This
transformation presents substantial challenges to network administration and
service providers regarding sustainability and scalability. This article
combines Distributed Artificial Intelligence (DAI) with Zero-touch Provisioning
(ZTP) for edge networks. This combination helps to manage network devices
seamlessly and intelligently by minimizing human intervention. In addition,
several advantages are also highlighted that come with incorporating
Distributed AI into ZTP in the context of edge networks. Further, we draw
potential research directions to foster novel studies in this field and
overcome the current limitations
Machine Learning Meets Communication Networks: Current Trends and Future Challenges
The growing network density and unprecedented increase in network traffic, caused by the massively expanding number of connected devices and online services, require intelligent network operations. Machine Learning (ML) has been applied in this regard in different types of networks and networking technologies to meet the requirements of future communicating devices and services. In this article, we provide a detailed account of current research on the application of ML in communication networks and shed light on future research challenges. Research on the application of ML in communication networks is described in: i) the three layers, i.e., physical, access, and network layers; and ii) novel computing and networking concepts such as Multi-access Edge Computing (MEC), Software Defined Networking (SDN), Network Functions Virtualization (NFV), and a brief overview of ML-based network security. Important future research challenges are identified and presented to help stir further research in key areas in this direction
Autonomy and Intelligence in the Computing Continuum: Challenges, Enablers, and Future Directions for Orchestration
Future AI applications require performance, reliability and privacy that the
existing, cloud-dependant system architectures cannot provide. In this article,
we study orchestration in the device-edge-cloud continuum, and focus on AI for
edge, that is, the AI methods used in resource orchestration. We claim that to
support the constantly growing requirements of intelligent applications in the
device-edge-cloud computing continuum, resource orchestration needs to embrace
edge AI and emphasize local autonomy and intelligence. To justify the claim, we
provide a general definition for continuum orchestration, and look at how
current and emerging orchestration paradigms are suitable for the computing
continuum. We describe certain major emerging research themes that may affect
future orchestration, and provide an early vision of an orchestration paradigm
that embraces those research themes. Finally, we survey current key edge AI
methods and look at how they may contribute into fulfilling the vision of
future continuum orchestration.Comment: 50 pages, 8 figures (Revised content in all sections, added figures
and new section
6G White Paper on Edge Intelligence
In this white paper we provide a vision for 6G Edge Intelligence. Moving towards 5G and beyond the future 6G networks, intelligent solutions utilizing data-driven machine learning and artificial intelligence become crucial for several real-world applications including but not limited to, more efficient manufacturing, novel personal smart device environments and experiences, urban computing and autonomous traffic settings. We present edge computing along with other 6G enablers as a key component to establish the future 2030 intelligent Internet technologies as shown in this series of 6G White Papers. In this white paper, we focus in the domains of edge computing infrastructure and platforms, data and edge network management, software development for edge, and real-time and distributed training of ML/AI algorithms, along with security, privacy, pricing, and end-user aspects. We discuss the key enablers and challenges and identify the key research questions for the development of the Intelligent Edge services. As a main outcome of this white paper, we envision a transition from Internet of Things to Intelligent Internet of Intelligent Things and provide a roadmap for development of 6G Intelligent Edge