Modern heuristic techniques for combinatorial problems
Modern heuristic techniques for combinatorial problems
Observations on Using Genetic Algorithms for Dynamic Load-Balancing
IEEE Transactions on Parallel and Distributed Systems
SNMP,SNMPV2,Snmpv3,and RMON 1 and 2
SNMP,SNMPV2,Snmpv3,and RMON 1 and 2
GridRM: A Resource Monitoring Architecture for the Grid
GRID '02 Proceedings of the Third International Workshop on Grid Computing
Grid resource management: state of the art and future trends
Grid resource management: state of the art and future trends
Dynamic Task Scheduling using Genetic Algorithms for Heterogeneous Distributed Computing
IPDPS '05 Proceedings of the 19th IEEE International Parallel and Distributed Processing Symposium (IPDPS'05) - Workshop 6 - Volume 07
Distributed computing in practice: the Condor experience: Research Articles
Concurrency and Computation: Practice & Experience - Grid Performance
Grid'5000: A Large Scale and Highly Reconfigurable Grid Experimental Testbed
GRID '05 Proceedings of the 6th IEEE/ACM International Workshop on Grid Computing
Integrating SNMP agents with XML-based management systems
IEEE Communications Magazine
| Hi-index | 0.00 |
This paper presents both, SNMP-based resource monitoring and heuristic resource scheduling systems targeted to manage large-scale Grids. This approach involves two phases: resource monitoring and resource scheduling. Resource monitoring (even discovery) phase is supported by the SNMP-based Balanced Load Monitoring Agents for Resource Scheduling (SBLOMARS). This resource monitoring and discovery approach is different from current distributed monitoring systems in three main areas. Firstly, it reaches a high level of generality by the integration of SNMP technology and thus, it is offering an alternative solution to handle heterogeneous operating platforms. Secondly, it solves the flexibility problem by the implementation of complex dynamic software structures, which are used to monitor from simple personal computers to robust multi-processor systems or clusters with even multiple hard disks and storage partitions. Finally, the scalability problem is covered by the distribution of the monitoring system into a set of submonitoring instances which are specific per each kind of computational resource to monitor (processor, memory, software, network and storage). Resource scheduling phase is supported by the Balanced Load Multi-Constrain Resource Scheduler (BLOMERS). This resource scheduler is implemented based on a Genetic Algorithm, as an alternative to solve the inherent NP-hard problem for resource scheduling in large-scale Grids. We show some graphical and textual snapshots of resource availability reports as well as a scheduling scenario in the Grid5000 platform. We have obtained a scalable scheduler with an extraordinary load balanced between all nodes participating in the Grid.