Resource discovery is an important process for finding suitable nodes that satisfy application requirements in large loosely-coupled distributed systems. Besides inter-node heterogeneity, many of these systems also show high degree of intra-node dynamism, so that selecting nodes based only on their recently observed resource capacities can lead to poor deployment decisions resulting in application failures or migration overheads. However, most existing resource discovery mechanisms rely only on recent observations to achieve scalability in large systems. In this paper, we propose the notion of a resource bundle - a representative resource usage distribution for a group of nodes with similar resource usage patterns - that employs two complementary techniques to overcome the limitations of existing techniques: resource usage histograms to provide statistical guarantees for resource capacities, and clustering-based resource aggregation to achieve scalability. Using tracedriven simulations and data analysis of a month-long PlanetLab trace, we show that resource bundles are able to provide high accuracy for statistical resource discovery (up to 56% better precision than using only recent values), while achieving high scalability (up to 55% fewer messages than a non-aggregation algorithm). We also show that resource bundles are ideally suited for identifying group-level characteristics such as finding load hotspots and estimating total group capacity (within 8% of actual values).
Cardosa, Michael; Chandra, Abhishek.
Resource Bundles: Using Aggregation for Statistical Wide-Area Resource Discovery and Allocation.
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