Leveraging Cloud Heterogeneity for Cost-Efficient Execution of Parallel Applications

Public cloud providers offer a wide range of instance types, with different processing and interconnection speeds, as well as varying prices. Furthermore, the tasks of many parallel applications show different computational demands due to load imbalance. These differences can be exploited for improving the cost efficiency of parallel applications in many cloud environments by matching application requirements to instance types. In this paper, we introduce the concept of heterogeneous cloud systems consisting of different instance types to leverage the different computational demands of large parallel applications for improved cost efficiency. We present a mechanism that automatically suggests a suitable combination of instances based on a characterization of the application and the instance types. With such a heterogeneous cloud, we are able to improve cost efficiency significantly for a variety of MPI-based applications, while maintaining a similar performance.

[1]  Ahmed El-Mahdy,et al.  Network Topology Identification for Cloud Instances , 2013, 2013 International Conference on Cloud and Green Computing.

[2]  Laxmikant V. Kalé,et al.  Identifying the Culprits Behind Network Congestion , 2015, 2015 IEEE International Parallel and Distributed Processing Symposium.

[3]  Philippe Olivier Alexandre Navaux,et al.  High Performance Computing in the cloud: Deployment, performance and cost efficiency , 2012, 4th IEEE International Conference on Cloud Computing Technology and Science Proceedings.

[4]  S. Freitas,et al.  The Coupled Aerosol and Tracer Transport model to the Brazilian developments on the Regional Atmospheric Modeling System (CATT-BRAMS) – Part 1: Model description and evaluation , 2007 .

[5]  Philippe Olivier Alexandre Navaux,et al.  Evaluating High Performance Computing on the Windows Azure Platform , 2012, 2012 IEEE Fifth International Conference on Cloud Computing.

[6]  Dejan S. Milojicic,et al.  HPC-Aware VM Placement in Infrastructure Clouds , 2013, 2013 IEEE International Conference on Cloud Engineering (IC2E).

[7]  Stephen P. Crago,et al.  Heterogeneous Cloud Computing: The Way Forward , 2015, Computer.

[8]  Arnaldo Carvalho de Melo,et al.  The New Linux ’ perf ’ Tools , 2010 .

[9]  Boon Thau Loo,et al.  Exploiting cloud heterogeneity for optimized cost/performance MapReduce processing , 2014, CloudDP '14.

[10]  Guillaume Houzeaux,et al.  A massively parallel fractional step solver for incompressible flows , 2009, J. Comput. Phys..

[11]  Carreño Ed,et al.  Communication Optimization of Parallel Applications in the Cloud , 2016 .

[12]  Yang Wang,et al.  Budget-Driven Scheduling Algorithms for Batches of MapReduce Jobs in Heterogeneous Clouds , 2014, IEEE Transactions on Cloud Computing.

[13]  Azer Bestavros,et al.  Network-Constrained Packing of Brokered Workloads in Virtualized Environments , 2015, 2015 15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing.

[14]  Hsien-Hsin S. Lee,et al.  Using Mathematical Modeling in Provisioning a Heterogeneous Cloud Computing Environment , 2011, Computer.

[15]  George Bosilca,et al.  Open MPI: Goals, Concept, and Design of a Next Generation MPI Implementation , 2004, PVM/MPI.

[16]  David H. Bailey,et al.  The Nas Parallel Benchmarks , 1991, Int. J. High Perform. Comput. Appl..

[17]  Maurice Gagnaire,et al.  Performance and price analysis for cloud service providers , 2015, 2015 Science and Information Conference (SAI).

[18]  Philippe Olivier Alexandre Navaux,et al.  Automatic Communication Optimization of Parallel Applications in Public Clouds , 2016, 2016 16th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid).