We use cookies to improve your experience with our site.
张胜, 钱柱中, 吴杰, 陆桑璐. 面向服务的云资源分配:追寻弹性与效率[J]. 计算机科学技术学报, 2015, 30(2): 421-436. DOI: 10.1007/s11390-015-1533-2
引用本文: 张胜, 钱柱中, 吴杰, 陆桑璐. 面向服务的云资源分配:追寻弹性与效率[J]. 计算机科学技术学报, 2015, 30(2): 421-436. DOI: 10.1007/s11390-015-1533-2
Sheng Zhang, Zhu-Zhong Qian, Jie Wu, Sang-Lu Lu. Service-Oriented Resource Allocation in Clouds: Pursuing Flexibility and Efficiency[J]. Journal of Computer Science and Technology, 2015, 30(2): 421-436. DOI: 10.1007/s11390-015-1533-2
Citation: Sheng Zhang, Zhu-Zhong Qian, Jie Wu, Sang-Lu Lu. Service-Oriented Resource Allocation in Clouds: Pursuing Flexibility and Efficiency[J]. Journal of Computer Science and Technology, 2015, 30(2): 421-436. DOI: 10.1007/s11390-015-1533-2

面向服务的云资源分配:追寻弹性与效率

Service-Oriented Resource Allocation in Clouds: Pursuing Flexibility and Efficiency

  • 摘要: 目前公有云所采用的网络无关的资源预留模式不能保证云用户的应用性能;为此,研究学者提出将云用户的计算和网络资源需求抽象为虚拟网络。本文提出了一种能够同时描述部署位置和动态资源需求的虚拟网络模型RLVN;在该模型基础上,本文研究了如何在云中高效并灵活地放置多个虚拟网络资源请求,并设计了分别侧重提升物理资源利用率和提供资源分配灵活性的部署算法MIPA和SAPA。算法MIPA通过引入额外的辅助节点,将部署问题转化为多商品流问题;基于容量约束、流约束和辅助节点约束等条件,建立混合整数规划模型,通过线性放松和随机取整生成部署方案。算法SAPA通过定义合理的邻居方案生成方法及能量计算方法,借助模拟退火框架给出部署方案,并允许基础设施提供商通过调整迭代次数来灵活地控制算法性能与运行时间之间的均衡。仿真结果表明了两种算法各自的特性与优势。

     

    Abstract: The networking-oblivious resource reservation model in today's public clouds cannot guarantee the performance of tenants' applications. Virtual networks that capture both computing and networking resource requirements of tenants have been proposed as better interfaces between cloud providers and tenants. In this paper, we propose a novel virtual network model that could specify not only absolute and relative location requirements but also time-varying resource demands. Building on top of our model, we study how to efficiently and flexibly place multiple virtual networks in a cloud, and we propose two algorithms, MIPA and SAPA, which focus on optimizing resource utilization and providing flexible placement, respectively. The mixed integer programming based MIPA transforms the placement problem into the multi-commodity flow problem through augmenting the physical network with shadow nodes and links. The simulated annealing-based SAPA achieves resource utilization efficiency through opportunistically sharing physical resources among multiple resource demands. Besides, SAPA allows cloud providers to control the trade-offs between performance guarantee and resource utilization, and between allocation optimality and running time, and allows tenants to control the trade-off between application performance and placement cost. Extensive simulation results confirm the efficiency of MIPA in resource utilization and the flexibility of SAPA in controlling trade-offs.

     

/

返回文章
返回