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The three traditional cloud delivery models -- IaaS, PaaS, and SaaS -- constrain access to cloud resources by hiding their raw functionality and forcing us to use them indirectly via a restricted set of actions. Can we introduce a new…
Cloud-based services with resources to be provisioned for consumers are increasingly the norm, especially with respect to Big data, spatiotemporal data mining and application services that impose a user's agreed Quality of Service (QoS)…
A large number of cloud middleware platforms and tools are deployed to support a variety of Internet of Things (IoT) data analytics tasks. It is a common practice that such cloud platforms are only used by its owners to achieve their…
While the requirements of enterprise and web applications have driven the development of Cloud computing, some of its key features, such as customized environments and rapid elasticity, could also benefit scientific applications. However,…
This paper presents a theoretical discussion for environmentally-conscious job deployment and migration in cloud environments, aiming to minimize the environmental impact of resource provisioning while incorporating sustainability…
This chapter presents: (i) a layered peer-to-peer Cloud provisioning architecture; (ii) a summary of the current state-of-the-art in Cloud provisioning with particular emphasis on service discovery and load-balancing; (iii) a classification…
In cloud computing management, the dynamic adaptation of computing resource allocations under time-varying workload is an active domain of investigation. Several control strategies were already proposed. Here the model-free control setting…
Can cloud computing infrastructures provide HPC-competitive performance for scientific applications broadly? Despite prolific related literature, this question remains open. Answers are crucial for designing future systems and democratizing…
High Performance Computing (HPC), Artificial Intelligence (AI)/Machine Learning (ML), and Quantum Computing (QC) and communications offer immense opportunities for innovation and impact on society. Researchers in these areas depend on…
Serverless computing has emerged as a prominent paradigm, with a significant adoption rate among cloud customers. While this model offers advantages such as abstraction from the deployment and resource scheduling, it also poses limitations…
Serverless computing is a widely adopted cloud execution model composed of Function-as-a-Service (FaaS) and Backend-as-a-Service (BaaS) offerings. The increased level of abstraction makes vendor lock-in inherent to serverless computing,…
Deep neural networks have established themselves as the state-of-the-art methodology in almost all computer vision tasks to date. But their application to processing data lying on non-Euclidean domains is still a very active area of…
Virtual clusters are widely used computing platforms than can be deployed in multiple cloud platforms. The ability to dynamically grow and shrink the number of nodes has paved the way for customised elastic computing both for High…
The variety of existing cloud services creates a challenge for service providers to enforce reasonable Software Level Agreements (SLA) stating the Quality of Service (QoS) and penalties in case QoS is not achieved. To avoid such penalties…
Distributed digital infrastructures for computation and analytics are now evolving towards an interconnected ecosystem allowing complex applications to be executed from IoT Edge devices to the HPC Cloud (aka the Computing Continuum, the…
Cloud computing is considered as the one of the most dominant paradigm in field of information technology which offers on demand cost effective services such as Software as a service (SAAS), Infrastructure as a service (IAAS) and Platform…
Modern DevOps practices have accelerated software delivery through automation, CI/CD pipelines, and observability tooling,but these approaches struggle to keep pace with the scale and dynamism of cloud-native systems. As telemetry volume…
Over the Eight decades, computing paradigms have shifted from large, centralized systems to compact, distributed architectures, leading to the rise of the Distributed Computing Continuum (DCC). In this model, multiple layers such as cloud,…
Deep Learning approaches for real, large, and complex scientific data sets can be very challenging to design. In this work, we present a complete search for a finely-tuned and efficiently scaled deep learning classifier to identify usable…
Distributed acoustic sensing (DAS) technology represents an innovative fiber-optic-based sensing methodology that enables real-time acoustic signal monitoring through the detection of minute perturbations along optical fibers. This sensing…