169 research outputs found
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)
Rise of the Planet of Serverless Computing: A Systematic Review
Serverless computing is an emerging cloud computing paradigm, being adopted to develop a wide range of software applications.
It allows developers to focus on the application logic in the granularity of function, thereby freeing developers from tedious and
error-prone infrastructure management. Meanwhile, its unique characteristic poses new challenges to the development and deployment
of serverless-based applications. To tackle these challenges, enormous research efforts have been devoted. This paper provides a
comprehensive literature review to characterize the current research state of serverless computing. Specifically, this paper covers 164
papers on 17 research directions of serverless computing, including performance optimization, programming framework, application
migration, multi-cloud development, testing and debugging, etc. It also derives research trends, focus, and commonly-used platforms
for serverless computing, as well as promising research opportunities
Zero-touch realization of Pervasive Artificial Intelligence-as-a-service in 6G networks
The vision of the upcoming 6G technologies, characterized by ultra-dense
network, low latency, and fast data rate is to support Pervasive AI (PAI) using
zero-touch solutions enabling self-X (e.g., self-configuration,
self-monitoring, and self-healing) services. However, the research on 6G is
still in its infancy, and only the first steps have been taken to conceptualize
its design, investigate its implementation, and plan for use cases. Toward this
end, academia and industry communities have gradually shifted from theoretical
studies of AI distribution to real-world deployment and standardization. Still,
designing an end-to-end framework that systematizes the AI distribution by
allowing easier access to the service using a third-party application assisted
by a zero-touch service provisioning has not been well explored. In this
context, we introduce a novel platform architecture to deploy a zero-touch
PAI-as-a-Service (PAIaaS) in 6G networks supported by a blockchain-based smart
system. This platform aims to standardize the pervasive AI at all levels of the
architecture and unify the interfaces in order to facilitate the service
deployment across application and infrastructure domains, relieve the users
worries about cost, security, and resource allocation, and at the same time,
respect the 6G stringent performance requirements. As a proof of concept, we
present a Federated Learning-as-a-service use case where we evaluate the
ability of our proposed system to self-optimize and self-adapt to the dynamics
of 6G networks in addition to minimizing the users' perceived costs.Comment: IEEE Communications Magazin
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