Self-correlating predictive information tracking for large-scale production systems
ICAC '09 Proceedings of the 6th international conference on Autonomic computing
Nebulas: using distributed voluntary resources to build clouds
HotCloud'09 Proceedings of the 2009 conference on Hot topics in cloud computing
OLIC: online information compression for scalable hosting infrastructure monitoring
Proceedings of the Nineteenth International Workshop on Quality of Service
Bringing introspection into BlobSeer: Towards a self-adaptive distributed data management system
International Journal of Applied Mathematics and Computer Science - SPECIAL SECTION: Efficient Resource Management for Grid-Enabled Applications
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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 inter-node heterogeneity, many of these systems also show a high degree of intra-node dynamism, so that selecting nodes based only on their recently observed resource capacities for scalability reasons can lead to poor deployment decisions resulting in application failures or migration overheads. 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 Planet Lab 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 hot spots and estimating total group capacity (within 8% of actual values).