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As data and computational grids grow in size and complexity, the crucial task of identifying, monitoring and utilizing available resources in an efficient manner is becoming increasingly difficult. The design of monitoring systems that are scalable both in the number of sources being monitored and in the number of clients served is a challenging issue. In this paper we investigate the trade-offs of different polling strategies that can be used to monitor resource availability on machines in a distributed environment. We show how adaptive polling protocols can substantially increase scalability with a less than proportional loss of precision, and how these protocols can be personalized for different types of resource usage patterns.