Network-aware summarisation for resource discovery in P2P-content networks
Future Generation Computer Systems
A task routing approach to large-scale scheduling
Future Generation Computer Systems
An efficient resource discovery framework for pure unstructured peer-to-peer systems
Computer Networks: The International Journal of Computer and Telecommunications Networking
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Resource discovery is an important process for finding suitable nodes that satisfy application requirements in large loosely coupled distributed systems. Besides internode heterogeneity, many of these systems also show a high degree of intranode 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 mainly 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 trace-driven 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, while achieving high scalability. We also show that resource bundles are ideally suited for identifying group-level characteristics (e.g., hot spots, total group capacity). To automatically parameterize the bundling algorithm, we present an adaptive algorithm that can detect online fluctuations in resource heterogeneity.