# Adaptation of Parallel SaaS to Heterogeneous Co-Located Cloud Resources
## 2 The Related Works
Rescale's cloud platform offers the commercial EDEM software for computations of particle systems by the DEM [44]. The open-source DEM code MercuryDPM [45] was also deployed on a cloud computing platform. [PERSON] et al. [46] developed parallel MPI-based DEM software for distributed-memory architectures and deployed SaaS on the OpenStack cloud. The presented performance analysis revealed MPI communication issues and overhead caused by processes of the OpenStack services Nova and Zun. However, the heterogeneity of cloud resources and co-location issues were not considered. DEM computations were rarely performed on heterogeneous resources, but heterogeneous architectures were often employed for computationally intensive applications in other research areas [47]. [PERSON] et al. [47] analyzed the performance of widely used applications, such as FFT, convolution, and N-body simulation on a multicore cluster node with or without GPUs. In plasma plume simulations with the particle-in-cell model, [PERSON] et al. [48] proposed a patch-based dynamic load balancing method with over-decomposition and a Hilbert space-filling curve. For microscopy image analysis, [PERSON] et al. [49] developed a cost-aware data partitioning strategy, minimizing load imbalance on hybrid CPU-GPU machines. [PERSON] et al. [50] optimally distributed the workload of data-parallel scientific applications between heterogeneous computing resources by using functional performance models of processing elements and relevant data partitioning algorithms. Meanwhile, [PERSON] et al. [51] explored the trade-off between execution time and consumed energy for aortic valve computations on a heterogeneous OpenStack cloud, comparing parallel speedups obtained through several partitioning techniques, but did not consider load imbalance and co-located resources. The related works are listed and compared in Table 1. The parallel applications that frequently communicate between nodes and must execute simultaneously are indicated in the column \"Parallel (internode)\". The parallelization software is provided in the next column. The performance studies on heterogeneous and co-located resources are indicated in the columns named \"Heterogeneous resources\" and \"Co-located resources\", respectively. It can be observed that parallel DEM computations are rarely performed on heterogeneous resources because of increased load imbalance or communication. In other research areas, the parallel frequently communicating applications are more often solved on heterogeneous cloud resources. In distributed cloud environments, parallel frequently communicating applications are rare because of their inherent limitations to achieve high parallel performance and scalability on heterogeneous or co-located resources. To the best of our knowledge, there are no articles on the adaptation of parallel frequently communicating applications to co-located cloud resources in the literature. Excessive usage of memory bandwidth by one virtual resource can substantially reduce the performance of parallel memory bandwidth bound DEM computations on the other resource co-located on the same physical machine.