Related papers: Towards trusted volunteer grid environments
Discovery Systems (DS) can be considered as entry points for global loosely coupled distributed systems. An efficient Discovery System in essence increases the performance, reliability and decision making capability of distributed systems.…
Volunteer Computing, sometimes called Public Resource Computing, is an emerging computational model that is very suitable for work-pooled parallel processing. As more complex grid applications make use of work flows in their design and…
The emergence of agentic Artificial Intelligence (AI), which can operate autonomously, demonstrate goal-directed behavior, and adaptively learn, indicates the onset of a massive change in today's computing infrastructure. This study…
Cooperative driving at isolated intersections attracted great interest and had been well discussed in recent years. However, cooperative driving in multi-intersection road networks remains to be further investigated, because many algorithms…
Federated learning enables machine learning algorithms to be trained over a network of multiple decentralized edge devices without requiring the exchange of local datasets. Successfully deploying federated learning requires ensuring that…
Grid and cloud computing systems have been extensively used to solve large and complex problems in science and engineering areas. These systems include powerful computing resources connected through high-speed networks. Due to recent…
Foundation models (FMs) have shown remarkable capabilities in generalized intelligence, multimodal understanding, and adaptive learning across a wide range of domains. However, their deployment in harsh or austere environments --…
Cyber-physical systems increasingly rely on distributed computing platforms where sensing, computing, actuation, and communication resources are shared by a multitude of applications. Such `cyber-physical cloud computing platforms' present…
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,…
Several interesting problems in multi-robot systems can be cast in the framework of distributed optimization. Examples include multi-robot task allocation, vehicle routing, target protection, and surveillance. While the theoretical analysis…
Globally distributed groups require collaborative systems to support their work. Besides being able to support the teamwork, these systems also should promote well-being and maximize the human potential that leads to an engaging system and…
Computational Grids, coupling geographically distributed resources such as PCs, workstations, clusters, and scientific instruments, have emerged as a next generation computing platform for solving large-scale problems in science,…
The smart grid utilizes many Internet of Things (IoT) applications to support its intelligent grid monitoring and control. The requirements of the IoT applications vary due to different tasks in the smart grid. In this paper, we propose a…
Federated Inference (FI) studies how independently trained and privately owned models can collaborate at inference time without sharing data or model parameters. While recent work has explored secure and distributed inference from disparate…
Developments in pervasive computing introduced a new world of computing where networked processors embedded and distributed in everyday objects communicating with each other over wireless links. Computers in such environments work in the…
Restrictive rules for data sharing in many industries have led to the development of federated learning. Federated learning is a machine-learning technique that allows distributed clients to train models collaboratively without the need to…
Deployment of distributed applications on large systems, and especially on grid infrastructures, becomes a more and more complex task. Grid users spend a lot of time to prepare, install and configure middleware and application binaries on…
Motivated by applications such as on-device collaborative neural network inference, this work investigates edge-facilitated collaborative fog computing - in which edge-devices collaborate with each other and with the edge of the network to…
The idle computers on a local area, campus area, or even wide area network represent a significant computational resource---one that is, however, also unreliable, heterogeneous, and opportunistic. This type of resource has been used…
Grids allow users flexible on-demand usage of computing resources through remote communication networks. A remarkable example of a Grid in High Energy Physics (HEP) research is used in the ALICE experiment at European Organization for…