Abstract
Cloud computing manages system resources such as processing, storage, and networking by providing users with multiple virtual machines (VMs) as needed. It is one of the rapidly growing fields that come with huge computational power for scientific workloads. Currently, the scientific community is ready to work over the cloud as it is considered as a resource-rich paradigm. The traditional way of executing scientific workloads on cloud computing is by using virtual machines. However, the latest emerging concept of containerization is growing more rapidly and gained popularity because of its unique features. Containers are treated as lightweight as compared to virtual machines in cloud computing. In this regard, a few VMs/containers-associated problems of performance and throughput are encountered because of middleware technologies such as virtualization or containerization. In this paper, we introduce the configurations of VMs and containers for cloud-based scientific workloads in order to utilize the technologies to solve scientific problems and handle their workloads. This paper also tackles throughput and efficiency problems related to VMs and containers in the cloud environment and explores efficient resource provisioning by combining four unique methods: hyperthreading (HT), vCPU cores selection, vCPU affinity, and isolation of vCPUs. The HEPSCPEC06 benchmark suite is used to evaluate the throughput and efficiency of VMs and containers. The proposed solution is to implement four basic techniques to reduce the effect of virtualization and containerization. Additionally, these techniques are used to make virtual machines and containers more effective and powerful for scientific workloads. The results show that allowing hyperthreading, isolation of CPU cores, proper numbering, and allocation of vCPU cores can improve the throughput and performance of virtual machines and containers.
Highlights
Cloud computing [1] has become the most promising computing paradigm that provides flexible and on-demand infrastructure to scientific workloads
In order to utilize virtualization technologies, we evaluated the different configurations of virtual machines (VMs) and containers, which are the main computing actors for scientific workloads
This study proposes that to achieve real-time performance, virtual machines or containers would operate with fewer vCPU core numbers
Summary
Cloud computing [1] has become the most promising computing paradigm that provides flexible and on-demand infrastructure to scientific workloads. It has evolved from grid and utility computing. Scientific workloads manipulated using high performance computing (HPC), high throughput computing (HTC), and many-task computing (MTC) [4] can be executed in virtualized computing environment. Multitask computing acts as a consensus solution to bridging the gap between HTC and HPC. It can perform many independent and dependent tasks using huge computing in shorter time.
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