云環(huán)境下的資源調(diào)度算法研究
發(fā)布時(shí)間：2019-01-08 10:03
【摘要】：云計(jì)算是一種新的商業(yè)計(jì)算模型和服務(wù)模式,它將計(jì)算任務(wù)分布在大量計(jì)算機(jī)構(gòu)成的不同數(shù)據(jù)中心使各種應(yīng)用能夠根據(jù)需要獲取計(jì)算能力、存儲(chǔ)空間和信息服務(wù)。云計(jì)算數(shù)據(jù)中心利用虛擬化技術(shù)將各種軟硬件資源抽象為虛擬化資源,形成虛擬化資源池,再通過資源調(diào)度技術(shù)以“按需使用,按量付費(fèi)”的原則將這些資源提供給用戶使用。隨著現(xiàn)代數(shù)據(jù)中心的規(guī)模和用戶數(shù)量急劇增大,如何快速高效地動(dòng)態(tài)部署數(shù)據(jù)中心的這些資源成為云計(jì)算資源調(diào)度的重要問題。因此如何在保證用戶服務(wù)質(zhì)量,不違反服務(wù)水平協(xié)議(Service Level Agreement, SLA)的情況下提高數(shù)據(jù)中心資源的使用效率是云環(huán)境下資源調(diào)度需要研究的主要問題。 云系統(tǒng)的負(fù)載均衡和最小化數(shù)據(jù)中心運(yùn)營(yíng)成本是云環(huán)境下的資源調(diào)度面臨的性能優(yōu)化和成本控制的兩大關(guān)鍵問題。針對(duì)系統(tǒng)負(fù)載不均衡導(dǎo)致的資源浪費(fèi)和系統(tǒng)瓶頸等問題,本文提出了基于改進(jìn)模擬退火的云環(huán)境下虛擬機(jī)資源的負(fù)載平衡調(diào)度算法(Simulated Annealing Load Balancing:SALB),通過最小化物理主機(jī)負(fù)載的標(biāo)準(zhǔn)差來達(dá)到系統(tǒng)的負(fù)載平衡。區(qū)別于傳統(tǒng)的SA算法中隨機(jī)選取初始解和鄰域解的方式,本文根據(jù)系統(tǒng)的實(shí)時(shí)負(fù)載情況來選取合適的初始解和產(chǎn)生新的鄰域解。利用虛擬機(jī)遷移技術(shù),將負(fù)載過高的物理機(jī)上運(yùn)行的虛擬機(jī)遷移到負(fù)載低的物理主機(jī)上,在遷移的過程中利用模擬退火的思想以一定的概率接受劣質(zhì)解從而避免陷入局部最優(yōu)解。在擴(kuò)展后的CloudSim平臺(tái)上實(shí)現(xiàn)了負(fù)載平衡調(diào)度算法SALB的仿真,實(shí)驗(yàn)結(jié)果表明SALB能夠取得優(yōu)于傳統(tǒng)的模擬退火算法和輪詢調(diào)度算法更好的系統(tǒng)負(fù)載平衡。 針對(duì)數(shù)據(jù)中心運(yùn)營(yíng)成本控制的問題,本文提出了基于模擬退火思想的改進(jìn)遺傳算法(Simulated Annealing combined Genetic Algorithm:SACGA)用于虛擬機(jī)資源分配來降低數(shù)據(jù)中心的運(yùn)營(yíng)成本。通過在傳統(tǒng)遺傳算法的交叉和變異過程中加入模擬退火的思想,在進(jìn)化過程中以一定的概率接受劣質(zhì)解,使得遺傳算法能夠避免過早地陷入局部最優(yōu)解和早熟現(xiàn)象的發(fā)生。仿真結(jié)果表明SACGA能夠在保證客戶服務(wù)水平協(xié)議的基礎(chǔ)上節(jié)省數(shù)據(jù)中心的操作代價(jià),使得系統(tǒng)操作代價(jià)低于使用傳統(tǒng)的遺傳算法作為資源調(diào)度策略。最后總結(jié)全文并說明下一步的研究?jī)?nèi)容。
[Abstract]:Cloud computing is a new business computing model and service model. It distributes computing tasks in different data centers composed of a large number of computers so that various applications can acquire computing power, store space and information services according to their needs. Cloud computing data center abstracts all kinds of software and hardware resources into virtualized resources by using virtualization technology to form virtualized resource pool, and then provides these resources to users by the principle of "on demand, according to payment" through resource scheduling technology. With the rapid increase of the scale and the number of users in the modern data center, how to deploy these resources quickly and efficiently becomes an important issue of cloud computing resource scheduling. Therefore, how to improve the efficiency of data center resources in the case of guaranteeing the quality of service of users and not violating (Service Level Agreement, SLA) is the main problem of resource scheduling in cloud environment. Load balancing and minimizing the operating cost of data center are the two key problems of resource scheduling in cloud environment, such as performance optimization and cost control. Aiming at the problem of resource waste and system bottleneck caused by system load imbalance, this paper proposes a load balancing scheduling algorithm (Simulated Annealing Load Balancing:SALB) based on improved simulated annealing for virtual machine resources in cloud environment. The system load balance is achieved by minimizing the standard deviation of the physical host load. Different from the traditional SA algorithm in which the initial solution and the neighborhood solution are randomly selected, this paper selects the appropriate initial solution and produces a new neighborhood solution according to the real-time load of the system. Using the technology of virtual machine migration, the virtual machine running on the overloaded physical machine is migrated to the low-load physical host. In the process of migration, the idea of simulated annealing is used to accept the inferior solution with a certain probability so as to avoid falling into the local optimal solution. The simulation of load balancing scheduling algorithm SALB is implemented on the extended CloudSim platform. The experimental results show that SALB can achieve better load balancing than the traditional simulated annealing algorithm and polling scheduling algorithm. In this paper, an improved genetic algorithm (Simulated Annealing combined Genetic Algorithm:SACGA) based on simulated annealing (SA) is proposed to reduce the operating cost of the data center by allocating virtual machine resources in order to control the operating cost of the data center. By adding the idea of simulated annealing in the process of crossover and mutation of traditional genetic algorithm, we can accept the inferior solution with a certain probability in the evolution process, so that the genetic algorithm can avoid falling into the local optimal solution and premature phenomenon prematurely. The simulation results show that SACGA can save the operation cost of the data center on the basis of guaranteeing the customer service level protocol, which makes the operating cost of the system lower than that of using the traditional genetic algorithm as the resource scheduling strategy. Finally, the paper summarizes the full text and explains the next research content.
【學(xué)位授予單位】：北京交通大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TP393.01
本文編號(hào)：2404453
[Abstract]:Cloud computing is a new business computing model and service model. It distributes computing tasks in different data centers composed of a large number of computers so that various applications can acquire computing power, store space and information services according to their needs. Cloud computing data center abstracts all kinds of software and hardware resources into virtualized resources by using virtualization technology to form virtualized resource pool, and then provides these resources to users by the principle of "on demand, according to payment" through resource scheduling technology. With the rapid increase of the scale and the number of users in the modern data center, how to deploy these resources quickly and efficiently becomes an important issue of cloud computing resource scheduling. Therefore, how to improve the efficiency of data center resources in the case of guaranteeing the quality of service of users and not violating (Service Level Agreement, SLA) is the main problem of resource scheduling in cloud environment. Load balancing and minimizing the operating cost of data center are the two key problems of resource scheduling in cloud environment, such as performance optimization and cost control. Aiming at the problem of resource waste and system bottleneck caused by system load imbalance, this paper proposes a load balancing scheduling algorithm (Simulated Annealing Load Balancing:SALB) based on improved simulated annealing for virtual machine resources in cloud environment. The system load balance is achieved by minimizing the standard deviation of the physical host load. Different from the traditional SA algorithm in which the initial solution and the neighborhood solution are randomly selected, this paper selects the appropriate initial solution and produces a new neighborhood solution according to the real-time load of the system. Using the technology of virtual machine migration, the virtual machine running on the overloaded physical machine is migrated to the low-load physical host. In the process of migration, the idea of simulated annealing is used to accept the inferior solution with a certain probability so as to avoid falling into the local optimal solution. The simulation of load balancing scheduling algorithm SALB is implemented on the extended CloudSim platform. The experimental results show that SALB can achieve better load balancing than the traditional simulated annealing algorithm and polling scheduling algorithm. In this paper, an improved genetic algorithm (Simulated Annealing combined Genetic Algorithm:SACGA) based on simulated annealing (SA) is proposed to reduce the operating cost of the data center by allocating virtual machine resources in order to control the operating cost of the data center. By adding the idea of simulated annealing in the process of crossover and mutation of traditional genetic algorithm, we can accept the inferior solution with a certain probability in the evolution process, so that the genetic algorithm can avoid falling into the local optimal solution and premature phenomenon prematurely. The simulation results show that SACGA can save the operation cost of the data center on the basis of guaranteeing the customer service level protocol, which makes the operating cost of the system lower than that of using the traditional genetic algorithm as the resource scheduling strategy. Finally, the paper summarizes the full text and explains the next research content.
【學(xué)位授予單位】：北京交通大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TP393.01
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，本文編號(hào)：2404453
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