Computer Science and Technology, Harbin Institute of Technology, Harbin, China.
Computer Science and Technology, Air Force Communication NCO Academy, DaLian, China.
PLoS One. 2019 Feb 6;14(2):e0211729. doi: 10.1371/journal.pone.0211729. eCollection 2019.
To flexibly meet users' demands in cloud computing, it is essential for providers to establish the efficient virtual mapping in datacenters. Accordingly, virtualization has become a key aspect of cloud computing. It is possible to consolidate resources based on the single objective of reducing energy consumption. However, it is challenging for the provider to consolidate resources efficiently based on a multiobjective optimization strategy. In this paper, we present a novel migration algorithm to consolidate resources adaptively using a two-level scheduling algorithm. First, we propose the grey relational analysis (GRA) and technique for order preference by similarity to the ideal solution (TOPSIS) policy to simultaneously determine the hotspots by the main selected factors, including the CPU and the memory. Second, a two-level hybrid heuristic algorithm is designed to consolidate resources in order to reduce costs and energy consumption, mainly depending on the PSO and ACO algorithms. The improved PSO can determine the migrating VMs quickly, and the proposed ACO can locate the positions. Extensive experiments demonstrate that the two-level scheduling algorithm performs the consolidation strategy efficiently during the dynamic allocation process.
为了灵活满足云计算用户的需求，提供商在数据中心中建立高效的虚拟映射至关重要。因此，虚拟化已成为云计算的关键方面。基于降低能耗这一单一目标，整合资源成为可能。然而，对于提供商而言，基于多目标优化策略高效地整合资源颇具挑战性。在本文中，我们提出了一种新的迁移算法，通过两级调度算法自适应地整合资源。首先，我们提出了灰色关联分析（GRA）和逼近理想解排序方法（TOPSIS）策略，同时通过主要选择因素（包括 CPU 和内存）来确定热点。其次，设计了两级混合启发式算法来整合资源，以降低成本和能耗，主要取决于粒子群优化（PSO）和蚁群优化（ACO）算法。改进的 PSO 可以快速确定迁移的虚拟机，而提出的 ACO 可以定位位置。大量实验表明，两级调度算法在动态分配过程中高效地执行了整合策略。
