474 research outputs found
Deep Reinforcement Learning (DRL)-based Methods for Serverless Stream Processing Engines: A Vision, Architectural Elements, and Future Directions
Streaming applications are becoming widespread across an extensive range of
business domains as an increasing number of sources continuously produce data
that need to be processed and analysed in real time. Modern businesses are
aggressively using streaming data to generate valuable knowledge that can be
used to automate processes, help decision-making, optimize resource usage, and
ultimately generate revenue for the organization. Despite their increased
adoption and tangible benefits, support for the automated deployment and
management of streaming applications is yet to emerge. Although a plethora of
stream management systems have flooded the open source community in recent
years, all of the existing frameworks demand a considerably challenging and
lengthy effort from human operators to manually and continuously tune their
configuration and deployment environment in order to reach and maintain the
desired performance goals. To address these challenges, this article proposes a
vision for creating Deep Reinforcement Learning (DRL)-based methods for
transforming stream processing engines into self-managed serverless solutions.
This will lead to an increase in productivity as engineers can focus on the
actual development process, an increase in application performance potentially
leading to reduced response times and more accurate and meaningful results, and
a considerable decrease in operational costs for organizations.Comment: 21 pages, 10 figure
Optimization and Prediction Techniques for Self-Healing and Self-Learning Applications in a Trustworthy Cloud Continuum
The current IT market is more and more dominated by the “cloud continuum”. In the “traditional” cloud, computing resources are typically homogeneous in order to facilitate economies of scale. In contrast, in edge computing, computational resources are widely diverse, commonly with scarce capacities and must be managed very efficiently due to battery constraints or other limitations. A combination of resources and services at the edge (edge computing), in the core (cloud computing), and along the data path (fog computing) is needed through a trusted cloud continuum. This requires novel solutions for the creation, optimization, management, and automatic operation of such infrastructure through new approaches such as infrastructure as code (IaC). In this paper, we analyze how artificial intelligence (AI)-based techniques and tools can enhance the operation of complex applications to support the broad and multi-stage heterogeneity of the infrastructural layer in the “computing continuum” through the enhancement of IaC optimization, IaC self-learning, and IaC self-healing. To this extent, the presented work proposes a set of tools, methods, and techniques for applications’ operators to seamlessly select, combine, configure, and adapt computation resources all along the data path and support the complete service lifecycle covering: (1) optimized distributed application deployment over heterogeneous computing resources; (2) monitoring of execution platforms in real time including continuous control and trust of the infrastructural services; (3) application deployment and adaptation while optimizing the execution; and (4) application self-recovery to avoid compromising situations that may lead to an unexpected failure.This research was funded by the European project PIACERE (Horizon 2020 research and innovation Program, under grant agreement no 101000162)
Edge AI for Internet of Energy: Challenges and Perspectives
The digital landscape of the Internet of Energy (IoE) is on the brink of a
revolutionary transformation with the integration of edge Artificial
Intelligence (AI). This comprehensive review elucidates the promise and
potential that edge AI holds for reshaping the IoE ecosystem. Commencing with a
meticulously curated research methodology, the article delves into the myriad
of edge AI techniques specifically tailored for IoE. The myriad benefits,
spanning from reduced latency and real-time analytics to the pivotal aspects of
information security, scalability, and cost-efficiency, underscore the
indispensability of edge AI in modern IoE frameworks. As the narrative
progresses, readers are acquainted with pragmatic applications and techniques,
highlighting on-device computation, secure private inference methods, and the
avant-garde paradigms of AI training on the edge. A critical analysis follows,
offering a deep dive into the present challenges including security concerns,
computational hurdles, and standardization issues. However, as the horizon of
technology ever expands, the review culminates in a forward-looking
perspective, envisaging the future symbiosis of 5G networks, federated edge AI,
deep reinforcement learning, and more, painting a vibrant panorama of what the
future beholds. For anyone vested in the domains of IoE and AI, this review
offers both a foundation and a visionary lens, bridging the present realities
with future possibilities
Joint multi-objective MEH selection and traffic path computation in 5G-MEC systems
Multi-access Edge Computing (MEC) is an emerging technology that allows to reduce the service latency and traffic congestion and to enable cloud offloading and context awareness. MEC consists in deploying computing devices, called MEC Hosts (MEHs), close to the user. Given the mobility of the user, several problems rise. The first problem is to select a MEH to run the service requested by the user. Another problem is to select the path to steer the traffic from the user to the selected MEH. The paper jointly addresses these two problems. First, the paper proposes a procedure to create a graph that is able to capture both network-layer and application-layer performance. Then, the proposed graph is used to apply the Multi-objective Dijkstra Algorithm (MDA), a technique used for multi-objective optimization problems, in order to find solutions to the addressed problems by simultaneously considering different performance metrics and constraints. To evaluate the performance of MDA, the paper implements a testbed based on AdvantEDGE and Kubernetes to migrate a VideoLAN application between two MEHs. A controller has been realized to integrate MDA with the 5G-MEC system in the testbed. The results show that MDA is able to perform the migration with a limited impact on the network performance and user experience. The lack of migration would instead lead to a severe reduction of the user experience.publishedVersio
AI meets CRNs : a prospective review on the application of deep architectures in spectrum management
The spectrum low utilization and high demand conundrum created a bottleneck towards
ful lling the requirements of next-generation networks. The cognitive radio (CR) technology was advocated
as a de facto technology to alleviate the scarcity and under-utilization of spectrum resources by exploiting
temporarily vacant spectrum holes of the licensed spectrum bands. As a result, the CR technology became
the rst step towards the intelligentization of mobile and wireless networks, and in order to strengthen
its intelligent operation, the cognitive engine needs to be enhanced through the exploitation of arti cial
intelligence (AI) strategies. Since comprehensive literature reviews covering the integration and application
of deep architectures in cognitive radio networks (CRNs) are still lacking, this article aims at lling the
gap by presenting a detailed review that addresses the integration of deep architectures into the intricacies
of spectrum management. This is a prospective review whose primary objective is to provide an in-depth
exploration of the recent trends in AI strategies employed in mobile and wireless communication networks.
The existing reviews in this area have not considered the relevance of incorporating the mathematical
fundamentals of each AI strategy and how to tailor them to speci c mobile and wireless networking
problems. Therefore, this reviewaddresses that problem by detailing howdeep architectures can be integrated
into spectrum management problems. Beyond reviewing different ways in which deep architectures can be
integrated into spectrum management, model selection strategies and how different deep architectures can
be tailored into the CR space to achieve better performance in complex environments are then reported in
the context of future research directions.The Sentech Chair in Broadband Wireless Multimedia Communications (BWMC) at the University of Pretoria.http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6287639am2022Electrical, Electronic and Computer Engineerin
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