Building a Private HPC Cloud for Compute and Data-Intensive Applications

Taifi, Moussa; Khreishah, Abdallah; Shi, Justin Y.

doi:10.5121/ijccsa.2013.3201

Cited by 7 publications

(2 citation statements)

References 16 publications

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“…Taifi et al [7] proposed architecture to build HPC clusters on top of a private cloud. High throughput connections using Infiniband switches are used between the compute VMs whereas an Ethernet is connecting the compute nodes to the Cloud controller.…”

Section: Related Workmentioning

confidence: 99%

ASETS: A SDN Empowered Task Scheduling System for HPCaaS on the Cloud

Jamalian¹,

Rajaei²

2015

2015 IEEE International Conference on Cloud Engineering

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With increasing demands for High Performance Computing (HPC), new ideas and methods are emerged to utilize computing resources more efficiently. Cloud Computing appears to provide benefits such as resource pooling, broad network access and cost efficiency for the HPC applications. However, moving the HPC applications to the cloud can face several key challenges, primarily, the virtualization overhead, multi-tenancy and network latency. Software-Defined Networking (SDN) as an emerging technology appears to pave the road and provide dynamic manipulation of cloud networking such as topology, routing, and bandwidth allocation. This paper presents a new scheme called ASETS which targets dynamic configuration and monitoring of cloud networking using SDN to improve the performance of HPC applications and in particular task scheduling for HPC as a service on the cloud (HPCaaS). Further, SETSA, (SDN-Empowered Task Scheduler Algorithm) is proposed as a novel task scheduling algorithm for the offered ASETS architecture. SETSA monitors the network bandwidth to take advantage of its changes when submitting tasks to the virtual machines. Empirical analysis of the algorithm in different case scenarios show that SETSA has significant potentials to improve the performance of HPCaaS platforms by increasing the bandwidth efficiency and decreasing task turnaround time. In addition, SETSAW, (SETSA Window) is proposed as an improvement of the SETSA algorithm.

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Section: Related Workmentioning

confidence: 99%

ASETS: A SDN Empowered Task Scheduling System for HPCaaS on the Cloud

Jamalian¹,

Rajaei²

2015

2015 IEEE International Conference on Cloud Engineering

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“…A feasible solution for transferring planet-size data is to copy the data into a big storage drive and then send the drive to the destination. In addition, the private cloud solution helps developers to construct cloud platforms for local use [ 72 ].…”

Section: Massive Computing In the Cloudmentioning

confidence: 99%

Enabling Large-Scale Biomedical Analysis in the Cloud

Lin

2013

BioMed Research International

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Recent progress in high-throughput instrumentations has led to an astonishing growth in both volume and complexity of biomedical data collected from various sources. The planet-size data brings serious challenges to the storage and computing technologies. Cloud computing is an alternative to crack the nut because it gives concurrent consideration to enable storage and high-performance computing on large-scale data. This work briefly introduces the data intensive computing system and summarizes existing cloud-based resources in bioinformatics. These developments and applications would facilitate biomedical research to make the vast amount of diversification data meaningful and usable.

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