Distributed and heuristic policy-based resource management system for large-scale Grids

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.