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Cloud Computing researches involve a tremendous amount of entities such as users, applications, and virtual machines. Due to the limited access and often variable availability of such resources, researchers have their prototypes tested…
Distributed machine learning systems require strong privacy guarantees, verifiable compliance, and scalable deployment across heterogeneous and multi-cloud environments. This work introduces a cloud-native privacy-preserving architecture…
The widespread adoption of cloud computing has resulted in the proliferation of open source cloud computing frameworks that give more control to enterprises over their data and networks. Though the benefits of open source software are…
Orchestrating next gen applications over hterogeneous resources along the Cloud-IoT continuum calls for new strategies and tools to enable scalable and application-specific managements. Inspired by the self-organisation capabilities of…
Serverless computing is gaining traction as an attractive model for the deployment of a multitude of workloads in the cloud. Designing and building effective resource management solutions for any computing environment requires extensive…
Federated Learning (FL) is a distributed Machine Learning (ML) technique that can benefit from cloud environments while preserving data privacy. We propose Multi-FedLS, a framework that manages multi-cloud resources, reducing execution time…
Cloud computing systems promise to offer subscription-oriented, enterprise-quality computing services to users worldwide. With the increased demand for delivering services to a large number of users, they need to offer differentiated…
With the emergence of new methodologies and technologies it has now become possible to manage large amounts of environmental sensing data and apply new integrated computing models to acquire information intelligence. This paper advocates…
Multi-cloud computing is a promising paradigm to support very large scale world wide distributed applications. Multi-cloud computing is the usage of multiple, independent cloud environments, which assumed no priori agreement between cloud…
Big data analytics has gathered immense research attention lately because of its ability to harness useful information from heaps of data. Cloud computing has been adjudged as one of the best infrastructural solutions for implementation of…
Generative AI is transforming enterprise application development by enabling machines to create content, code, and designs. These models, however, demand substantial computational power and data management. Cloud computing addresses these…
Federated Learning (FL) is a decentralized machine learning (ML) paradigm in which models are trained on private data across several devices called clients and combined at a single node called an aggregator rather than aggregating the data…
Cloud-based software systems are increasingly becoming complex and operating in highly dynamic environments. Self-adaptivity and self-awareness have recently emerged to cope with such level of dynamicity and scalability. Meanwhile,…
Edge computing is a promising computing paradigm for pushing the cloud service to the network edge. To this end, edge infrastructure providers (EIPs) need to bring computation and storage resources to the network edge and allow edge service…
The cloud computing paradigm is being adopted by many organizations in different application domains as it is cost effective and offers a virtually unlimited pool of resources. Engineering critical systems can benefit from clouds in…
Cloud computing is becoming an increasingly lucrative branch of the existing information and communication technologies (ICT). Enabling a debate about cloud usage scenarios can help with attracting new customers, sharing best-practices, and…
Scientific research increasingly depends on robust and scalable IT infrastructures to support complex computational workflows. With the proliferation of services provided by research infrastructures, NRENs, and commercial cloud providers,…
This paper describes the use of a distributed cloud computing system for high-throughput computing (HTC) scientific applications. The distributed cloud computing system is composed of a number of separate Infrastructure-as-a-Service (IaaS)…
Cloud computing has become the de facto paradigm for delivering software to system users, with organizations and enterprises of all sizes making use of cloud services in some way. On the surface, adopting the cloud appears to be a very…
While services benefit from distributed cloud centers running in isolation, allowing multiple centers to cooperate on implementing services unlocks the full power of distributed cloud computing. Distributed cloud services are typically set…