TY - GEN
T1 - Resource bundles
T2 - 28th International Conference on Distributed Computing Systems, ICDCS 2008
AU - Cardosa, Michael
AU - Chandra, Abhishek
PY - 2008
Y1 - 2008
N2 - 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 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 hot spots and estimating total group capacity (within 8% of actual values).
AB - 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 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 hot spots and estimating total group capacity (within 8% of actual values).
UR - http://www.scopus.com/inward/record.url?scp=51849131928&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=51849131928&partnerID=8YFLogxK
U2 - 10.1109/ICDCS.2008.37
DO - 10.1109/ICDCS.2008.37
M3 - Conference contribution
AN - SCOPUS:51849131928
SN - 9780769531724
T3 - Proceedings - The 28th International Conference on Distributed Computing Systems, ICDCS 2008
SP - 760
EP - 768
BT - Proceedings - The 28th International Conference on Distributed Computing Systems, ICDCS 2008
Y2 - 17 July 2008 through 20 July 2008
ER -