Robust Optimization Model for Primary and Backup Resource Allocation in Cloud Providers
Robust Optimization Model for Primary and Backup Resource Allocation in Cloud Providers
复制标题
DOI：
10.1109/tcc.2021.3051018
复制
发表时间：
2021-01
影响因子：
6.5
通讯作者：
Fujun He;Takehiro Sato;Bijoy Chand Chatterjee;T. Kurimoto;S. Urushidani;E. Oki
中科院分区：
文献类型：
--
作者：
Fujun He;Takehiro Sato;Bijoy Chand Chatterjee;T. Kurimoto;S. Urushidani;E. Oki
本文提出了一个主要和备份资源分配模型，该模型为虚拟机提供了概率的保护保证，以防止云提供商中物理机的多次故障，以最大程度地减少所需的总容量。物理机器为虚拟机分配了主要和备份计算资源。当发生任何故障时，带有预先计划的备份资源的幸存的物理机器恢复了故障物理机上的虚拟机并接管了工作负载。物理机器提供的保护无法成功的概率在给定的数字中保证。提供概率保护可以通过允许备份资源共享来降低所需的备份能力，但在一般能力案例中导致非线性编程问题，以防止多个故障。我们将强大的优化应用于广泛的数学操作，以制定主要和备份资源分配问题，作为混合整数线性编程问题，在抑制容量碎片的情况下。我们证明了被考虑的问题的NP坚持性。引入了启发式方法来解决优化问题。结果表明，所提出的模型在我们检查的情况下节省了总计的三分之一；它在阻止概率和资源利用方面都超过了传统模型。
This article proposes a primary and backup resource allocation model that provides a probabilistic protection guarantee for virtual machines against multiple failures of physical machines in a cloud provider to minimize the required total capacity. A physical machine allocates both primary and backup computing resources for virtual machines. When any failure occurs, the survived physical machines with preplanned backup resources recover the virtual machines on the failed physical machines and take over the workloads. The probability that the protection provided by a physical machine does not succeed is guaranteed within a given number. Providing the probabilistic protection can reduce the required backup capacity by allowing backup resource sharing, but it leads to a nonlinear programing problem in a general-capacity case against multiple failures. We apply robust optimization with extensive mathematical operations to formulate the primary and backup resource allocation problem as a mixed integer linear programming problem, where capacity fragmentation is suppressed. We prove the NP-hardness of considered problem. A heuristic is introduced to solve the optimization problem. The results reveal that the proposed model saves about one-third of the total capacity in our examined cases; it outperforms the conventional models in terms of both blocking probability and resource utilization.