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vcluster: a framework for auto scalable virtual cluster system in heterogeneous clouds

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Abstract

Cloud computing is an emerging technology and is being widely considered for resource utilization in various research areas. One of the main advantages of cloud computing is its flexibility in computing resource allocations. Many computing cycles can be ready in very short time and can be smoothly reallocated between tasks. Because of this, there are many private companies entering the new business of reselling their idle computing cycles. Research institutes have also started building their own cloud systems for their various research purposes. In this paper, we introduce a framework for virtual cluster system called vcluster which is capable of utilizing computing resources from heterogeneous clouds and provides a uniform view in computing resource management. vcluster is an IaaS (Infrastructure as a Service) based cloud resource management system. It distributes batch jobs to multiple clouds depending on the status of queue and system pool. The main design philosophy behind vcluster is cloud and batch system agnostic and it is achieved through plugins. This feature mitigates the complexity of integrating heterogeneous clouds. In the pilot system development, we use FermiCloud and Amazon EC2, which are a private and a public cloud system, respectively. In this paper, we also discuss the features and functionalities that must be considered in virtual cluster systems.

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Notes

  1. There are many research groups that have been utilizing virtualization technology in HPC (High Performance Computing) [10, 11]. Fermilab also evaluated the MPI performance using virtual machines communicating via Infiniband [12] and found similar performance.

  2. In some applications like SAP, it shows up even better performance than bare metal. Despite the small overhead for virtualization, flexibility in computing resource allocation compensates for it and provides many advantages in resource management perspective.

  3. Transmission delay of virtual machine images is one of challenges. According to [18], it took 5 hours to boot virtual machine of 16 GB transferred from Ottawa to Victoria.

  4. The benchmark used in their evaluation is originally come from SPEC CPU2006 [22] and specific to HEP.

  5. We do not consider multiple plugin supports at the same time. Therefore, if current batch plugin is for HTCondor, it has to be unplugged and replaced with a plugin for Torque in order to communicate with Torque batch system. This approach simplifies our implementation because it is unreasonable to support multiple batch systems in a single cluster system in HEP applications.

  6. In our experiment system, we use HTCondor batch system, but not limited to a specific batch system.

  7. vcluster GUI, managing a virtual cluster system, has been demonstrated at Supercomputing Conference in 2012.

  8. Such modules are simplified version and including minimal functionalities to evaluate the concept of vcluster.

  9. OpenNebula provides three different types of accesses to the cloud system such as OCA, OCCI (Open Cloud Computing Interface) [31], and Amazon EC2 [32]. In our implementation, we directly use OCA API because as of writing time, OpenNebula’s OCA API functions are a more complete set of functions to manage the cloud system compared to the OCCI and EC2 API’s.

  10. In order to simplify our problem, we assume all virtual worker nodes have same virtual cores.

  11. It can be achieved by virtual worker node migration technology to specific host systems.

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Acknowledgement

This research was supported by the National Research Foundation (NRF) of Korea through contract N-13-NM-IR04.

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Authors and Affiliations

  1. National Institute of Supercomputing and Networking, Korea Institute of Science and Technology Information, Daejeon, 305-806, Korea

    Seo-Young Noh & Haengjin Jang

  2. Grid and Cloud Computing Department, Fermi National Accelerator Laboratory, Batavia, IL, 60510, USA

    Steven C. Timm

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  1. Seo-Young Noh
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Correspondence to Haengjin Jang.

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Noh, SY., Timm, S.C. & Jang, H. vcluster: a framework for auto scalable virtual cluster system in heterogeneous clouds. Cluster Comput 17, 741–749 (2014). https://doi.org/10.1007/s10586-013-0292-5

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