39,192 research outputs found
A Taxonomy for Management and Optimization of Multiple Resources in Edge Computing
Edge computing is promoted to meet increasing performance needs of
data-driven services using computational and storage resources close to the end
devices, at the edge of the current network. To achieve higher performance in
this new paradigm one has to consider how to combine the efficiency of resource
usage at all three layers of architecture: end devices, edge devices, and the
cloud. While cloud capacity is elastically extendable, end devices and edge
devices are to various degrees resource-constrained. Hence, an efficient
resource management is essential to make edge computing a reality. In this
work, we first present terminology and architectures to characterize current
works within the field of edge computing. Then, we review a wide range of
recent articles and categorize relevant aspects in terms of 4 perspectives:
resource type, resource management objective, resource location, and resource
use. This taxonomy and the ensuing analysis is used to identify some gaps in
the existing research. Among several research gaps, we found that research is
less prevalent on data, storage, and energy as a resource, and less extensive
towards the estimation, discovery and sharing objectives. As for resource
types, the most well-studied resources are computation and communication
resources. Our analysis shows that resource management at the edge requires a
deeper understanding of how methods applied at different levels and geared
towards different resource types interact. Specifically, the impact of mobility
and collaboration schemes requiring incentives are expected to be different in
edge architectures compared to the classic cloud solutions. Finally, we find
that fewer works are dedicated to the study of non-functional properties or to
quantifying the footprint of resource management techniques, including
edge-specific means of migrating data and services.Comment: Accepted in the Special Issue Mobile Edge Computing of the Wireless
Communications and Mobile Computing journa
Generative AI-enabled Vehicular Networks: Fundamentals, Framework, and Case Study
Recognizing the tremendous improvements that the integration of generative AI
can bring to intelligent transportation systems, this article explores the
integration of generative AI technologies in vehicular networks, focusing on
their potential applications and challenges. Generative AI, with its
capabilities of generating realistic data and facilitating advanced
decision-making processes, enhances various applications when combined with
vehicular networks, such as navigation optimization, traffic prediction, data
generation, and evaluation. Despite these promising applications, the
integration of generative AI with vehicular networks faces several challenges,
such as real-time data processing and decision-making, adapting to dynamic and
unpredictable environments, as well as privacy and security concerns. To
address these challenges, we propose a multi-modality semantic-aware framework
to enhance the service quality of generative AI. By leveraging multi-modal and
semantic communication technologies, the framework enables the use of text and
image data for creating multi-modal content, providing more reliable guidance
to receiving vehicles and ultimately improving system usability and efficiency.
To further improve the reliability and efficiency of information transmission
and reconstruction within the framework, taking generative AI-enabled
vehicle-to-vehicle (V2V) as a case study, a deep reinforcement learning
(DRL)-based approach is proposed for resource allocation. Finally, we discuss
potential research directions and anticipated advancements in the field of
generative AI-enabled vehicular networks.Comment: 8 pages, 4 figure
- …