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Vega LingCloud: A Resource Single Leasing Point System to Support Heterogeneous Application Modes on Shared Infrastructure

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4 Author(s)
Xiaoyi Lu ; Inst. of Comput. Technol., Chinese Acad. of Sci., Beijing, China ; Jian Lin ; Li Zha ; Zhiwei Xu

In large organizations or IDCs, different departments always occupy and maintain dedicated resources to satisfy their or their customers' heterogeneous application loads. This situation easily makes the infrastructure management a repeated and inefficient work. Even worse, it is difficult to share the resources owned by different departments even when they are idle, because the application modes on these resources are quite different. This paper introduces a live system, Vega LingCloud, which provides a Resource Single Leasing Point System for consolidated renting physical and virtual machines to support heterogeneous application modes on shared infrastructure. Furthermore, we present the asset-leasing model and the architecture of Vega LingCloud. According to the evaluation, Vega LingCloud is better than other systems like Open Nebula and Enomaly ECP in the aspects of uniformity, flexibility, security, usability, and efficiency. The experimental result of management overhead shows that the deployment speed of virtual machine in Vega LingCloud is 4.1 times of that in the Open Nebula and VIDA hybrid system for deploying 64 virtual machines concurrently. From a representative micro-cloud example in a research group, we show that the consolidated way of leasing physical and virtual machine in Vega LingCloud is approbatory. Up to now, Vega LingCloud has been deployed in real world environments, which include a private cloud of large organization in Beijing and a public cloud in the Dongguan IDC of China. The resource scale of the Dongguan cloud reaches about 504 cores, 625 GB memory, and 156 TB storage. The number of supported real applications in Beijing and Dongguan clouds has exceeded 35, and their modes involve high performance computing, large scale data processing, virtual machine leasing, data storage, and so on.

Published in:

2011 IEEE Ninth International Symposium on Parallel and Distributed Processing with Applications

Date of Conference:

26-28 May 2011