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基于模擬退火算法的粒子群優(yōu)化算法在容器調度中的應用
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貴州大學(xué) 計算機科學(xué)與技術(shù)學(xué)院

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TP3

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貴州省科技計劃資助項目(黔科合基礎[2017]1051)。


Application of Particle Swarm Optimization Algorithm Based on Simulated Annealing Algorithm in Container Scheduling
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    摘要:

    隨著(zhù)互聯(lián)網(wǎng)產(chǎn)業(yè)的發(fā)展,虛擬機創(chuàng )建速度慢、不易擴展、靈活性不足等缺點(diǎn)越來(lái)越凸顯,容器技術(shù)的出現為這些問(wèn)題提出了一種新的解決思路。而現有的調度算法僅考慮容器云集群中工作節點(diǎn)的內存、CPU等物理資源,沒(méi)有考慮對容器云調度后的鏡像分發(fā)過(guò)程有明顯影響的網(wǎng)絡(luò )負載率,導致容器調度任務(wù)等待時(shí)間過(guò)長(cháng),造成數據中心的資源浪費。鑒于粒子群優(yōu)化算法在局部開(kāi)采能力和全局探測方面有較強的優(yōu)勢,提出了一種基于模擬退火算法的粒子群優(yōu)化算法(Simulated annealing particle swarm optimization algorithm,SA-PSO)的容器調度算法,通過(guò)使用模擬退火優(yōu)化粒子群算法使其在算法初期跳出局部最優(yōu)情況,提升算法性能。在Kubernetes平臺實(shí)驗過(guò)程中,SA-PSO調度算法相比Kubernetes的BalancedQosPriority算法,提升了整體節點(diǎn)資源利用率,顯著(zhù)減少任務(wù)最少等待時(shí)間;同時(shí)與標準PSO算法以及動(dòng)態(tài)慣性權重PSO算法進(jìn)行對比,不僅收斂能力有顯著(zhù)提升,并且相較標準PSO算法全局最優(yōu)節點(diǎn)命中率提升近60%。

    Abstract:

    With the development of the Internet industry, shortcomings such as slow creation of virtual machines, difficult expansion, and insufficient flexibility have become more and more prominent. The emergence of container technology has proposed a new solution to these problems. The existing scheduling algorithm only considers the memory, CPU and other physical resources of the working nodes in the container cloud cluster, and does not consider the network load rate that has a significant impact on the image distribution process after the container cloud scheduling, resulting in too long waiting time for container scheduling tasks. Cause a waste of resources in the data center. Considering that the particle swarm optimization algorithm has strong advantages in local mining capabilities and global detection, a simulated annealing algorithm-based particle swarm optimization algorithm (Simulated annealing particle swarm optimization algorithm, SA-PSO) container scheduling algorithm is proposed. Using simulated annealing to optimize the particle swarm algorithm makes it jump out of the local optimal situation in the early stage of the algorithm, and improves the performance of the algorithm. In the process of the Kubernetes platform experiment, the SA-PSO scheduling algorithm compared to Kubernetes" BalancedQosPriority algorithm improves the overall node resource utilization and significantly reduces the minimum waiting time of tasks; at the same time, it is compared with the standard PSO algorithm and the dynamic inertia weight PSO algorithm, which not only converges The ability has been significantly improved, and compared with the standard PSO algorithm, the global optimal node hit rate has increased by nearly 60%.

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引用本文

劉哲源,呂曉丹,蔣朝惠.基于模擬退火算法的粒子群優(yōu)化算法在容器調度中的應用計算機測量與控制[J].,2021,29(12):177-183.

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  • 收稿日期:2021-04-29
  • 最后修改日期:2021-05-20
  • 錄用日期:2021-05-21
  • 在線(xiàn)發(fā)布日期: 2021-12-24
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