TY - GEN
T1 - Towards Benchmarking IaaS and PaaS Clouds for Graph Analytics
AU - Iosup, Alexandru
AU - Capota, Mihai
AU - Hegeman, Tim
AU - Guo, Yong
AU - Ngai, Wing Lung
AU - Varbanescu, Ana Lucia
AU - Verstraaten, Merijn
PY - 2014
Y1 - 2014
N2 - Cloud computing is a new paradigm for using ICT services— only when needed and for as long as needed, and paying only for service actually consumed. Benchmarking the increasingly many cloud services is crucial for market growth and perceived fairness, and for service design and tuning. In this work, we propose a generic architecture for benchmarking cloud services. Motivated by recent demand for data-intensive ICT services, and in particular by processing of large graphs, we adapt the generic architecture to Graphalytics, a benchmark for distributed and GPU-based graph analytics platforms. Graphalytics focuses on the dependence of performance on the input dataset, on the analytics algorithm, and on the provisioned infrastructure. The benchmark provides components for platform configuration, deployment, and monitoring, and has been tested for a variety of platforms. We also propose a new challenge for the process of benchmarking data-intensive services, namely the inclusion of the data-processing algorithm in the system under test; this increases significantly the relevance of benchmarking results, albeit, at the cost of increased benchmarking duration.
AB - Cloud computing is a new paradigm for using ICT services— only when needed and for as long as needed, and paying only for service actually consumed. Benchmarking the increasingly many cloud services is crucial for market growth and perceived fairness, and for service design and tuning. In this work, we propose a generic architecture for benchmarking cloud services. Motivated by recent demand for data-intensive ICT services, and in particular by processing of large graphs, we adapt the generic architecture to Graphalytics, a benchmark for distributed and GPU-based graph analytics platforms. Graphalytics focuses on the dependence of performance on the input dataset, on the analytics algorithm, and on the provisioned infrastructure. The benchmark provides components for platform configuration, deployment, and monitoring, and has been tested for a variety of platforms. We also propose a new challenge for the process of benchmarking data-intensive services, namely the inclusion of the data-processing algorithm in the system under test; this increases significantly the relevance of benchmarking results, albeit, at the cost of increased benchmarking duration.
UR - http://www.scopus.com/inward/record.url?scp=84947999139&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84947999139&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-20233-4_11
DO - 10.1007/978-3-319-20233-4_11
M3 - Conference contribution
SN - 9783319202327
VL - 8991
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 109
EP - 131
BT - Big Data Benchmarking - 5th International Workshop, WBDB 2014, Potsdam, Germany, August 5-6, 2014, Revised Selected Papers
PB - Springer/Verlag
T2 - 5th International Workshop on Big Data Benchmarking, WBDB 2014
Y2 - 5 August 2014 through 6 August 2014
ER -