# Performance of Communication- and Computation-Intensive SaaS on the OpenStack Cloud
## 1 Introduction
[PERSON] et al. [26] evaluated the performance of Docker containers and VMs using standard benchmark tools, such as Sysbench, Phoronix, and Apache benchmark, in terms of CPU performance, memory throughput, storage read/write performance, load test, and operation speed measurement. [PERSON] et al. [27] investigated the performance of the instantiation process of micro-virtualized network functions for open-source virtual infrastructure managers, such as OpenStack Nova and Nomad, on the Xen virtualization platform. The source codes of virtual infrastructure managers were modified to reduce instantiation times. [PERSON] et al. [28] evaluated the performance of VMs and containers for the HEPSCPEC06 benchmark. The results showed that hyperthreading, isolation of CPU cores, proper numbering, and allocation of vCPU cores improve the performance of VMs and containers on the OpenStack cloud. Most of these studies [18; 19; 20; 21; 22; 23; 24] have found negligible performance differences between a container and native hardware. However, none of the studies includes the performance analysis of the virtualized distributed memory architectures for communication- and computation-intensive MPI-based computations. [PERSON] et al. [29] performed MPI-based NAS benchmarks on Xen and found that the measured overhead increases when more cores are added. [PERSON] et al. [30] ran MPI-based applications and observed significantly degraded performance on EC2. Commercial testbeds are naturally realistic, but they cannot provide scientists with dependable experiments and enough control. A study [31] on the use of container-based virtualization in HPC revealed that Xen is slower than LXC by roughly a factor of 2, while a native server and LXC have near-identical performances. However, the influence of cloud or other infrastructure services on the load imbalance of MPI-based computations was not investigated. [PERSON] et al. [32] showed that the performance of Docker containers when using the system MPI library for a parallel solution of Poisson's equation carried out by EEniCS software is comparable to the native performance. However, the influence of the communication and domain decomposition issues on the speedup of parallel computations was not investigated. [PERSON] et al. [33] evaluated the High-Performance Linpack benchmark on cloud computing infrastructures managed by Amazon Web Services, Microsoft Azure, Rackspace, and IBM SoftLayer. The obtained results demonstrated that Microsoft Azure H-instances with a low-latency Infiniband interconnect network deliversthe highest speedup. Moreover, the performance per computing core on the public cloud could be comparable to modern traditional supercomputing systems. [PERSON] et al. [34] proposed a communication-aware worst-fit decreasing heuristic algorithm for the container placement problem, but MPI-based applications were not considered. [PERSON] and [PERSON] [35] formulated a bi-objective optimization problem for performance and energy for data-parallel applications on homogeneous clusters. [PERSON] et al. [36] investigated a trade-off between the computing speed and the consumed energy of a real-life hemodynamic application on a heterogeneous cloud. Parallel speedups obtained by using several domain decomposition methods were compared, but load balance and communication issues were not explored. The influence of communications on the speedup of parallel computations on clouds and the influence of cloud infrastructure services on the load imbalance and overall performance of parallel MPI-based computations have not been investigated in the discussed research. This paper describes the performance analysis of the communication- and computation-intensive discrete element method SaaS on virtual resources of the OpenStack cloud infrastructure. The research examined the influence of communication issues and infrastructure services on SaaS performance, which can be dependent on the considered software and algorithmic aspects.