Abstract
This paper presents a distributed and heuristic policy-based system for resource management in large-scale Grids. This approach involves three phases: resource discovery, scheduling and allocation. The resource discovery phase is supported by the SNMP-based Balanced Load Monitoring Agents for Resource Scheduling (SBLOMARS). In this approach, network and computational resources are monitored by autonomous monitoring agents, offering a pure decentralized monitoring system. The resource scheduling phase is supported by the Balanced Load Multi-Constrained Resource Scheduler (BLOMERS). It is a heuristic resource scheduler, which includes an implementation of a Genetic Algorithm (GA), as an alternative to solve the inherent NP-hard problem for resource scheduling in large-scale Grids. Allocation phase is supported by means of a Policy-based Grid Management Architecture (PbGMA). This architecture integrates different sources of service necessities such as requirements demanded by customers, applications requirements and network conditions. It interfaces with Globus middleware to allocate services into the selected resources with certain levels of QoS.
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References
Magaña, E., Lefevre, L., Serrat. J.: Autonomic Management Architecture for Flexible Grid Services Deployment Based on Policies. ARCS’07, Zurich, Switzerland (2007)
Magaña, E., Lefevre, L., Serrat, J.: SBLOMARS: SNMP-based Balanced Load Monitoring Agents for Resource Scheduling in Large-scale Grids. In: The 8th IEEE/ACM Conference in Grid Computing (September 19-21st 2007) Austin, Texas, USA (submitted)
Magaña, E., Lefevre, L., Serrat, J.: BLOMERS: Balanced Load Multi-Constrained Resource Scheduler. ICNS 2007, Athens, Greece (June 19-25th, 2007)
Massie, M., Chun, B., Culler, D.: The Ganglia Distributed Monitoring System: Design, Implementation, and Experience. Parallel Computing 30(7) (July 2004)
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Magaña, E., Serrat, J. (2007). Distributed and Heuristic Policy-Based Resource Management System for Large-Scale Grids. In: Bandara, A.K., Burgess, M. (eds) Inter-Domain Management. AIMS 2007. Lecture Notes in Computer Science, vol 4543. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72986-0_21
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DOI: https://doi.org/10.1007/978-3-540-72986-0_21
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-72985-3
Online ISBN: 978-3-540-72986-0
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