Relational Grid Monitoring Architecture (R-GMA)
Rob Byrom, Brian Coghlan, Andrew W Cooke, Roney Cordenonsi, Linda Cornwall, Abdeslem Djaoui, Laurence Field, Steve Fisher, Steve Hicks, Stuart Kenny, Jason Leake, James Magowan, Werner Nutt, David O'Callaghan, Norbert Podhorszki, John Ryan, Manish Soni, Paul Taylor, Antony J Wilson
Abstract
We describe R-GMA (Relational Grid Monitoring Architecture) which has been developed within the European DataGrid Project as a Grid Information and Monitoring System. Is is based on the GMA from GGF, which is a simple Consumer-Producer model. The special strength of this implementation comes from the power of the relational model. We offer a global view of the information as if each Virtual Organisation had one large relational database. We provide a number of different Producer types with different characteristics; for example some support streaming of information. We also provide combined Consumer/Producers, which are able to combine information and republish it. At the heart of the system is the mediator, which for any query is able to find and connect to the best Producers for the job. We have developed components to allow a measure of inter-working between MDS and R-GMA. We have used it both for information about the grid (primarily to find out about what services are available at any one time) and for application monitoring. R-GMA has been deployed in various testbeds; we describe some preliminary results and experiences of this deployment.
Create a lesson
Related papers
PixelFlow: Token-Level Workload Management for Efficient Distributed DiT Serving
Zhexiang Zhang, Minchen Yu, Yifan Sun et al.
A Kubernetes-Native Request Router for Quality-Aware Inference Serving in the Computing Continuum
Ignjat Karanovic, Pantelis A. Frangoudis, Ivan Čilić et al.
Accelerating Sharded Data Parallelism at Scale with Federated Learning
Gianluca Mittone, Marco Aldinucci
Distributed Edge Inference: an Experimental Study on Multiview Detection
Gianluca Mittone, Giulio Malenza, Marco Aldinucci et al.
P-GADMM: Parallel Group-Based ADMM for Asynchronous Optimization in Heterogeneous Edge Networks
Gaiguo Wei, Qingying Zhang, Heqiang Wang et al.
VERA: Reinforcement Learning for Dynamic Memory Scaling of HPC Workloads in Kubernetes
Ade Pramono, Jie Ren, Ivy Peng