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High performance computing (HPC) and cloud have traditionally been separate, and presented in an adversarial light. The conflict arises from disparate beginnings that led to two drastically different cultures, incentive structures, and…
Edge computing is an emerging technology which places computing at the edge of the network to provide an ultra-low latency. Computation offloading, a paradigm that migrates computing from mobile devices to remote servers, can now use the…
Edge Computing emerges as a promising alternative of Cloud Computing, with scalable compute resources and services deployed in the path between IoT devices and Cloud. Since virtualization techniques can be applied on Edge compute nodes,…
A critical limitation in large-scale multi-agent systems is the cascading of errors. And without intermediate verification, downstream agents exacerbate upstream inaccuracies, resulting in significant quality degradation. To bridge this…
To enhance straggler resilience in federated learning (FL) systems, a semi-decentralized approach has been recently proposed, enabling collaboration between clients. Unlike the existing semi-decentralized schemes, which adaptively adjust…
Kubernetes (k8s) has the potential to coordinate distributed edge resources and centralized cloud resources, but currently lacks a specialized scheduling framework for edge-cloud networks. Besides, the hierarchical distribution of…
In this paper we introduce our vision of a Cognitive Computing Continuum to address the changing IT service provisioning towards a distributed, opportunistic, self-managed collaboration between heterogeneous devices outside the traditional…
Clustered federated learning (CFL) addresses the performance challenges posed by data heterogeneity in federated learning (FL) by organizing edge devices with similar data distributions into clusters, enabling collaborative model training…
This book chapter considers how Edge deployments can be brought to bear in a global context by federating them across multiple geographic regions to create a global Edge-based fabric that decentralizes data center computation. This is…
In an edge-cloud multi-tier network, datacenters provide services to mobile users, with each service having specific latency constraints and computational requirements. Deploying such a variety of services while matching their requirements…
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 explosive increase in volume, velocity, variety, and veracity of data generated by distributed and heterogeneous nodes such as IoT and other devices, continuously challenge the state of art in big data processing platforms and mining…
Federated machine learning has great promise to overcome the input privacy challenge in machine learning. The appearance of several projects capable of simulating federated learning has led to a corresponding rapid progress on algorithmic…
Different departments of a large organization often run dedicated cluster systems for different computing loads, like HPC (high performance computing) jobs or Web service applications. In this paper, we have designed and implemented a cloud…
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…
With the proliferation of distributed data sources, Federated Learning (FL) has emerged as a key approach to enable collaborative intelligence through decentralized model training while preserving data privacy. However, conventional FL…
Federated Learning (FL) is a well-known framework for successfully performing a learning task in an edge computing scenario where the devices involved have limited resources and incomplete data representation. The basic assumption of FL is…
A key challenge for supporting elastic behaviour in cloud systems is to achieve a good performance in automated (de-)provisioning and scheduling of computing resources. One of the key aspects that can be significant is the overheads…
Modern cyber-physical systems (CPS) such as Cooperative Intelligent Transport Systems (C-ITS) are increasingly defined by the software which operates these systems. In practice, microservice architectures can be employed, which may consist…
There is a growing need for low latency for many devices and users. The traditional cloud computing paradigm can not meet this requirement, legitimizing the need for a new paradigm. Edge computing proposes to move computing capacities to…