Related papers: Octopus: Experiences with a Hybrid Event-Driven Ar…
The ATLAS experiment at CERN relies on a worldwide distributed computing Grid infrastructure to support its physics program at the Large Hadron Collider. ATLAS has integrated cloud computing resources to complement its Grid infrastructure…
Grids aim at exploiting synergies that result from cooperation of autonomous distributed entities. The synergies that result from grid cooperation include the sharing, exchange, selection, and aggregation of geographically distributed…
The field of edge computing has witnessed remarkable growth owing to the increasing demand for real-time processing of data in applications. However, challenges persist due to limitations in performance and power consumption. To overcome…
Cloud computing has fundamentally transformed application development, yet a gap remains between the serverless promise of simplified deployment and its practical realization due to fragmentation across function runtimes, state management,…
This work introduces a novel, modular, layered web based platform for managing machine learning experiments on grid-based High Performance Computing infrastructures. The coupling of the communication services offered by the grid, with an…
This is the second of a planned collection of four yearly volumes describing the deployment of a heterogeneous many-core platform for experiments on scalable custom interconnects and management of fault and critical events, applied to…
In distributed Complex Event Processing (CEP) applications with high load but limited resources, bottleneck operators in the operator graph can significantly slow down processing of event streams, thus compelling the need to shed load. A…
Microservices emerged as a popular architectural style over the last decade. Although microservices are designed to be self-contained, they must communicate to realize business capabilities, creating dependencies among their data and…
The Cloud-Edge continuum enhances application performance by bringing computation closer to data sources. However, it presents considerable challenges in managing resources and determining service placement, as these tasks require…
Enterprise Networks, over the years, have become more and more complex trying to keep up with new requirements that challenge traditional solutions. Just to mention one out of many possible examples, technologies such as Virtual LANs…
In recent years, there is an emerging trend that some computing services are moving from cloud to the edge of the networks. Compared to cloud computing, edge computing can provide services with faster response, lower expense, and more…
The advent of the Internet of Things (IoT) gives the opportunity to numerous devices to interact with their environment, collect and process data. Data are transferred, in an upwards mode, to the Cloud through the Edge Computing (EC)…
Distributed Complex Event Processing (DCEP) is a commonly used paradigm to detect and act on situational changes of many applications, including the Internet of Things (IoT). DCEP achieves this using a simple specification of analytical…
Distributed computing platforms provide a robust mechanism to perform large-scale computations by splitting the task and data among multiple locations, possibly located thousands of miles apart geographically. Although such distribution of…
We introduce and analyze the stability of a class of event-triggered extremum-seeking algorithms designed to solve resource-aware, model-free, optimization problems. Leveraging recent advances in Lie-Bracket Averaging for hybrid systems, we…
The emergence of "big data" offers unprecedented opportunities for not only accelerating scientific advances but also enabling new modes of discovery. Scientific progress in many disciplines is increasingly enabled by our ability to examine…
The Internet of Things comes along with new challenges for experimenting, testing, and operating decentralized socio-technical systems at large-scale. In such systems, autonomous agents interact locally with their users, and remotely with…
The rapid development of parallel and distributed computing paradigms has brought about great revolution in computing. Thanks to the intrinsic parallelism of evolutionary computation (EC), it is natural to implement EC on parallel and…
Science reproducibility is a cornerstone feature in scientific workflows. In most cases, this has been implemented as a way to exactly reproduce the computational steps taken to reach the final results. While these steps are often…
Edge computing can be defined as an emerging technology that uses cloud computing to leverage edge data centers to process, store, and analyze data close to the source. Traditional cloud computing architectures are not designed for…