Abstract
The pervasive use of cloud computing has led to many concerns, such as performance challenges in communication- and computation-intensive services on virtual cloud resources. Most evaluations of the infrastructural overhead are based on standard benchmarks. Therefore, the impact of communication issues and infrastructure services on the performance of parallel MPI-based computations remains unclear. This paper presents the performance analysis of communication- and computation-intensive software based on the discrete element method, which is deployed as a service (SaaS) on the OpenStack cloud. The performance measured on KVM-based virtual machines and Docker containers of the OpenStack cloud is compared with that obtained by using native hardware. The improved mapping of computations to multicore resources reduced the internode MPI communication by 34.4% and increased the parallel efficiency from 0.67 to 0.78, which shows the importance of communication issues. Increasing the number of parallel processes, the overhead of the cloud infrastructure increased to 13.7% and 11.2% of the software execution time on native hardware in the case of the Docker containers and KVM-based virtual machines of the OpenStack cloud, respectively. The observed overhead was mainly caused by OpenStack service processes that increased the load imbalance of parallel MPI-based SaaS.
Highlights
Rapid developments in computing and communication technologies have led to the emergence of a distributed computing paradigm called cloud computing, which, due to its on-demand nature, low cost, and offloaded management, has become a natural solution to the problem of expanding computational needs [1]
This study aimed to investigate the performance of the developed DEM Software as a Service (SaaS) for discrete element method computations of granular materials on Kernel Virtual Machine (KVM)-based virtual machines (VMs) and Docker containers managed by the OpenStack cloud infrastructure
The parallel performance of the developed DEM SaaS was evaluated by measuring the speedup Sp and the efficiency Ep: Sp where t1 is the program execution time for a single processor and tp is the wall clock time for a given job to be executed on p processors
Summary
Rapid developments in computing and communication technologies have led to the emergence of a distributed computing paradigm called cloud computing, which, due to its on-demand nature, low cost, and offloaded management, has become a natural solution to the problem of expanding computational needs [1]. Cloud providers offer different types of services, such as Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS). Cloud infrastructures provide platforms and tools for building IT services at more affordable prices compared to the prices of traditional computing techniques. OpenStack is an open-source cloud management platform that delivers an integrated foundation to create, deploy, and scale a secure and reliable public or private cloud [2]. The compute service Nova, object storage service Swift, and image service Glance are the main parts of OpenStack. Another open-source local cloud framework is Eucalyptus [3], provided by Eucalyptus Systems, Inc (Santa Barbara, CA, USA). A typical Eucalyptus cloud is composed of a front-end cloud controller, a persistent storage controller, a virtual machine image repository, a cluster controller, and many compute nodes
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