# Performance of Communication- and Computation-Intensive SaaS on the OpenStack Cloud
## 5 Conclusions  
The paper presents a performance analysis of the communication- and computation-intensive DEM SaaS on the OpenStack cloud. The following observations and conclusions may be drawn:  
* The performance of the communication- and computation-intensive DEM SaaS highly depends on MPI communication issues, load mapping to virtual resources based on the multicore architecture, and the overhead of the cloud infrastructure.
* Casual mapping of particle subsets to multicore hardware resources can increase MPI communication time and decrease the parallel speedup. In the case of the benchmark with the thinnest ghost layer, improved mapping based on spatially connected subsets reduced the internode data transfer by 34.4% of the data transfer required by the casual mapping, decreased the communication time by 2.47 times, and raised the parallel efficiency from 0.67 to 0.78 for 12 processes.
* The performance analysis revealed that interprocess MPI communication highly influences the parallel performance of the DEM SaaS. A three-fold increase in the ghost layer thickness and the subsequent increase in transferred data decreased the parallel speedup from 12.3 to 9.4 for 16 processes. Significantly, the communication-to-computation ratio increased from 0.08 to 0.29.
* The virtualization layer reduced the computational performance of the developed parallel DEM SaaS by 2.4% and 2.0% in the case of Docker containers and KVM-based VMs without OpenStack services, respectively.
* The overall overhead of the cloud infrastructure increased significantly when the number of parallel processes increased. The software execution time increased by up  
Figure 10: Load imbalance measured on the native hardware and the OpenStack cloud.  
to 13.7% and 11.2% of the execution time on the native hardware in the case of Docker containers and KVM-based VMs with the OpenStack cloud, respectively.
* The large overhead was mainly caused by OpenStack processes that increased the load imbalance of the parallel DEM SaaS based on MPI communication. The processes of the OpenStack service Zun for Docker containers consumed more CPU time and produced a larger load imbalance than those of the OpenStack service Nova for KVM-based VMs, which resulted in a larger overall overhead of the cloud infrastructure. On average, the difference in overhead was about 2.5% of the execution time on the native hardware.
* The study revealed that standard benchmarks can hardly provide the comprehensive information required for efficient scheduling of parallel DEM computations. Preliminary specific benchmarks are required to evaluate the parallel performance of the developed SaaS and the overhead of the cloud infrastructure.  
Conceptualization, methodology, formal analysis, and investigation, O.B., R.P. and A.K.; software, O.B. and R.P.; writing--original draft preparation, A.K.; writing--review and editing, O.B. and R.P. All authors have read and agreed to the published version of the manuscript.  
This study is part of project no. 09.3.3-LMT-K-712-02-0131, funded under the European Social Fund measure \"Strengthening the Skills and Capacities of Public Sector Researchers for Engaging in High Level R&D Activities,\" administered by the Research Council of Lithuania.  
Not applicable.  
The authors declare no conflict of interest.