scholarly journals Container orchestration on HPC systems through Kubernetes

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Naweiluo Zhou ◽  
Yiannis Georgiou ◽  
Marcin Pospieszny ◽  
Li Zhong ◽  
Huan Zhou ◽  
...  
Keyword(s):  
Cloud Computing ◽  
High Performance ◽  
Hybrid Architecture ◽  
Service Support ◽  
Cloud Clusters ◽  
Container Management ◽  
Application Deployment ◽  
Workload Manager ◽  
Container Orchestration ◽  
Performance Computing

AbstractContainerisation demonstrates its efficiency in application deployment in Cloud Computing. Containers can encapsulate complex programs with their dependencies in isolated environments making applications more portable, hence are being adopted in High Performance Computing (HPC) clusters. Singularity, initially designed for HPC systems, has become their de facto standard container runtime. Nevertheless, conventional HPC workload managers lack micro-service support and deeply-integrated container management, as opposed to container orchestrators. We introduce a Torque-Operator which serves as a bridge between HPC workload manager (TORQUE) and container orchestrator (Kubernetes). We propose a hybrid architecture that integrates HPC and Cloud clusters seamlessly with little interference to HPC systems where container orchestration is performed on two levels.

scholarly journals A Manifestation of Cloud Computing Environments for Application Clusters, High-Performance Clusters and Allocation strategies for Virtual Machines

2019 ◽  
pp. 596-612
Author(s):  
Kranthi Kumar. K ◽  
R. Rindha Reddy ◽  
Kurumaddali Sushmitha
Keyword(s):  
Cloud Computing ◽  
High Performance ◽  
Virtual Machines ◽  
Low Cost ◽  
Shared Resources ◽  
Topology Mapping ◽  
Cloud Clusters ◽  
Computing Environments ◽  
Remote Association ◽  
Performance Computing

Cloud Computing (CC) is the advancement of the Grid Computing (GC) worldview in the direction of administration arranged structures. The phrasing connected to this sort of handling, while portraying shared resources, alludes to the idea of Service of X. Such assets are accessible on interest and at an altogether low cost contrasted with self-conveyance of individual segments. CC is found everywhere in current situations, from vast scale associations to a just little scale business, everybody is equipping themselves cloud. Due to its effortlessness, observing and support over remote association, expansive territory inclusion. Cloud can be any sort Software as an administration, stage as an administration, foundation as an administration dependent on its use. High Performance Computing (HIPECO) implies the accumulation of computational capacity to build the capacity of handling substantial issues in science, designing, and business. HIPECO on the cloud permits performing on interest HIPECO errands by superior clusters in a cloud atmosphere. Currently, CC arrangements (e.g., Microsoft Azure, Amazon EC2) enable the users to make use of only the fundamental storage and computational utilities. They prevent the allowance of custom adjustments of the topology designs or parameters of the system. The associations structures of the nodes in HIPECO clusters ought to give a quick bury node correspondence. It is vital that adaptability is safeguarded also. In a foundation, as an administration, virtualization viably maps virtual machines to the physical machines. In spite of the fact that it is difficult, undertaking for hypervisor to choose fitting host to serve up and coming virtual machine is a must requirement. In this paper, our main aim is to examine different techniques/types of cluster topology mapping and their necessities in numerous Cloud situations to accomplish higher dependability along with adaptability of utilization which is executed inside Cloud resources (CR), HIPECO resource allocation (RA) on the cloud clusters and Cluster based designation procedure.

Cloud Computing for Scientific Simulation and High Performance Computing

2013 ◽  
pp. 51-70
Author(s):  
Adrian Jackson ◽  
Michèle Weiland
Keyword(s):  
Cloud Computing ◽  
High Performance Computing ◽  
High Performance ◽  
Large Scale ◽  
Parallel Programs ◽  
Small Scale ◽  
Cloud Infrastructure ◽  
Scientific Simulations ◽  
Cloud Infrastructures ◽  
Performance Computing

This chapter describes experiences using Cloud infrastructures for scientific computing, both for serial and parallel computing. Amazon’s High Performance Computing (HPC) Cloud computing resources were compared to traditional HPC resources to quantify performance as well as assessing the complexity and cost of using the Cloud. Furthermore, a shared Cloud infrastructure is compared to standard desktop resources for scientific simulations. Whilst this is only a small scale evaluation these Cloud offerings, it does allow some conclusions to be drawn, particularly that the Cloud can currently not match the parallel performance of dedicated HPC machines for large scale parallel programs but can match the serial performance of standard computing resources for serial and small scale parallel programs. Also, the shared Cloud infrastructure cannot match dedicated computing resources for low level benchmarks, although for an actual scientific code, performance is comparable.

Green Computing

2014 ◽  
pp. 248-260
Keyword(s):  
Climate Change ◽  
Cloud Computing ◽  
High Performance Computing ◽  
High Performance ◽  
Large Scale ◽  
Green Computing ◽  
Research Topic ◽  
The Other ◽  
Cloud Infrastructures ◽  
Performance Computing

Green computing is a contemporary research topic to address climate and energy challenges. In this chapter, the authors envision the duality of green computing with technological trends in other fields of computing such as High Performance Computing (HPC) and cloud computing on one hand and economy and business on the other hand. For instance, in order to provide electricity for large-scale cloud infrastructures and to reach exascale computing, we need huge amounts of energy. Thus, green computing is a challenge for the future of cloud computing and HPC. Alternatively, clouds and HPC provide solutions for green computing and climate change. In this chapter, the authors discuss this proposition by looking at the technology in detail.

High Performance Computing on Mobile Devices

2016 ◽  
pp. 334-348
Author(s):  
Atta ur Rehman Khan ◽  
Abdul Nasir Khan
Keyword(s):  
Cloud Computing ◽  
High Performance Computing ◽  
Mobile Devices ◽  
High Performance ◽  
Mobile Cloud Computing ◽  
Computation Offloading ◽  
Mobile Cloud ◽  
Computing Technology ◽  
Wide Range ◽  
Performance Computing

Mobile devices are gaining high popularity due to support for a wide range of applications. However, the mobile devices are resource constrained and many applications require high resources. To cater to this issue, the researchers envision usage of mobile cloud computing technology which offers high performance computing, execution of resource intensive applications, and energy efficiency. This chapter highlights importance of mobile devices, high performance applications, and the computing challenges of mobile devices. It also provides a brief introduction to mobile cloud computing technology, its architecture, types of mobile applications, computation offloading process, effective offloading challenges, and high performance computing application on mobile devises that are enabled by mobile cloud computing technology.

A Hybrid Resource Reservation Method for Workflows in Clouds

2012 ◽  
Vol 4 (4) ◽  
pp. 1-21
Author(s):  
Tyng-Yeu Liang ◽  
Fu-Chun Lu ◽  
Jun-Yao Chiu
Keyword(s):  
Cloud Computing ◽  
Energy Consumption ◽  
High Performance ◽  
Resource Reservation ◽  
Scientific Workflows ◽  
Service Oriented ◽  
Time And Energy ◽  
Gpu Architecture ◽  
Performance Computing ◽  
Oriented System

QoS and energy consumption are two important issues for Cloud computing. In this paper, the authors propose a hybrid resource reservation method to address these two issues for scientific workflows in the high-performance computing Clouds built on hybrid CPU/GPU architecture. As named, this method reserves proper CPU or GPU for executing different jobs in the same workflow based on the profile of execution time and energy consumption of each resource-to-program pair. They have implemented the proposed resource reservation method on a real service-oriented system. The experimental results show that the proposed resource reservation method can effectively maintain the QoS of workflows while simultaneously minimizing the energy consumption of executing the workflows.

scholarly journals Viability of Cloud Computing for Real-Time Numerical Weather Prediction

2016 ◽  
Vol 31 (6) ◽  
pp. 1985-1996 ◽  
Author(s):  
David Siuta ◽  
Gregory West ◽  
Henryk Modzelewski ◽  
Roland Schigas ◽  
Roland Stull
Keyword(s):  
Cloud Computing ◽  
High Performance Computing ◽  
Real Time ◽  
Numerical Weather Prediction ◽  
High Performance ◽  
Virtual Machines ◽  
Weather Prediction ◽  
Cloud Platform ◽  
Numerical Weather ◽  
Performance Computing

Abstract As cloud-service providers like Google, Amazon, and Microsoft decrease costs and increase performance, numerical weather prediction (NWP) in the cloud will become a reality not only for research use but for real-time use as well. The performance of the Weather Research and Forecasting (WRF) Model on the Google Cloud Platform is tested and configurations and optimizations of virtual machines that meet two main requirements of real-time NWP are found: 1) fast forecast completion (timeliness) and 2) economic cost effectiveness when compared with traditional on-premise high-performance computing hardware. Optimum performance was found by using the Intel compiler collection with no more than eight virtual CPUs per virtual machine. Using these configurations, real-time NWP on the Google Cloud Platform is found to be economically competitive when compared with the purchase of local high-performance computing hardware for NWP needs. Cloud-computing services are becoming viable alternatives to on-premise compute clusters for some applications.

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