云服務系統(tǒng)中實時任務調(diào)度與資源動態(tài)調(diào)配方法研究
發(fā)布時間：2019-02-13 21:47
【摘要】：面對信息化戰(zhàn)場中海量的戰(zhàn)場信息和高動態(tài)并發(fā)的作戰(zhàn)單元應用需求,云計算將為戰(zhàn)場信息服務模式提供了一條新的途徑。云計算作為分布式計算的最新發(fā)展趨勢,它借助先進的虛擬化技術(shù),將云計算數(shù)據(jù)中心大規(guī)模的計算、存儲、網(wǎng)絡(luò)等資源虛擬成巨大的資源池,為用戶提供按需服務。在云計算模式下,用戶只需將任務提交到云服務系統(tǒng),云服務系統(tǒng)將自動分析任務特性、預測任務的資源需求,再根據(jù)云服務系統(tǒng)中底層資源的使用情況,將任務調(diào)度到相應的資源上執(zhí)行,并在用戶指定的時間內(nèi)完成任務的執(zhí)行。即,用戶只需提交任務、服務質(zhì)量要求和接收任務執(zhí)行的結(jié)果,而中間的所有事情,云服務系統(tǒng)將自動完成。對于云服務系統(tǒng)而言,高效的任務調(diào)度和資源動態(tài)調(diào)配方法是提高其性能的關(guān)鍵技術(shù)之一。目前,已經(jīng)存在大量關(guān)于云服務系統(tǒng)中任務調(diào)度和資源動態(tài)調(diào)配的研究成果。但是,已有的研究大部分集中于理想的調(diào)度環(huán)境:1)被調(diào)度的任務集合預先知道;2)任務的執(zhí)行時間是確定值,并且在調(diào)度前可以獲取;3)資源即時可用。然而,在實際的云服務系統(tǒng)中,存在大量動態(tài)、隨機性因素。比如,任務到達率劇烈變化,任務執(zhí)行時間具有隨機性,資源可動態(tài)伸縮和啟動資源需要時間開銷等。云服務系統(tǒng)中這些動態(tài)和隨機因素,往往使得預先生成的調(diào)度方案失去原有的優(yōu)勢或無法順利實施,甚至使得初始調(diào)度方案不再可行。因此,云服務系統(tǒng)中實時任務調(diào)度和資源動態(tài)調(diào)配方法研究極具理論和現(xiàn)實價值,且富有挑戰(zhàn)性。在實時任務調(diào)度和資源動態(tài)調(diào)配過程中,本文主要針對以下三種典型的情況:任務動態(tài)到達、任務執(zhí)行時間是隨機變量、主機和虛擬機啟動時間不可忽略。本文的主要工作和創(chuàng)新點包括以下四點:(1)提出了一個可擴展的主機組織模式。針對云服務系統(tǒng)中大規(guī)模主機對傳統(tǒng)主機組織模式,比如,集中式、分層式和分布式,提出的挑戰(zhàn),提出協(xié)同式組織模式,將大規(guī)模主機分為多個集群,每個集群都有一個獨立的調(diào)度器,每個調(diào)度器負責本集群的任務調(diào)度和資源調(diào)配,同時調(diào)度器之間相互協(xié)調(diào),共同調(diào)度任務和底層資源,從而提高云服務系統(tǒng)的可擴展性。(2)提出一個隨機性感知的調(diào)度框架。針對云服務系統(tǒng)中實時任務的高動態(tài)、隨機性和高時效性要求的特征,為每個集群提出一個隨機性感知的調(diào)度框架,將大部分等待任務放置在全局等待隊列中,并控制虛擬機上等待任務的個數(shù),當虛擬機完成任務之后,等待任務就立即執(zhí)行,然后優(yōu)先調(diào)度全局隊列中時效性要求較高的任務到虛擬機上等待,避免已經(jīng)完成任務的隨機性累加到當前調(diào)度的任務,從而提高調(diào)度的方案的穩(wěn)定性和保障實時任務時效性的能力。(3)提出了一個隨機性感知的調(diào)度算法PRS。針對云服務系統(tǒng)中實時任務動態(tài)到達、執(zhí)行時間具有隨機性的問題,在隨機性感知調(diào)度框架的基礎(chǔ)上,巧妙集成前攝性和反應式調(diào)度思想,提出一個在線調(diào)度算法PRS,該調(diào)度算法根據(jù)云服務系統(tǒng)的實際運行情況,不斷為云服務系統(tǒng)生成新的任務和虛擬機調(diào)度方案,從而在保證實時任務時效性要求的條件下,提高云服務系統(tǒng)中主機資源的有效利用和降低能量消耗。(4)提出了一個機器啟動時間感知的任務調(diào)度與資源動態(tài)調(diào)配算法STARS。在云服務系統(tǒng)中,實時任務的到達具有隨機性和突發(fā)性,當云服務系統(tǒng)中的負載突增時,啟動主機和創(chuàng)建虛擬機的過程會造成一定的時間開銷,使得某些任務不能及時開始,從而延誤了它們的截止期。針對以上問題,本文提出機器啟動時間感知的任務調(diào)度與資源動態(tài)調(diào)配算法STARS,借助單個虛擬機CPU能力可以動態(tài)伸縮的能力,通過轉(zhuǎn)移機器啟動時間對截止期較短任務的影響,以減緩機器啟動時間對突增任務時效性的影響,以提高云服務系統(tǒng)保障實時任務時效性的能力。
[Abstract]:The cloud computing will provide a new way for battlefield information service mode in the face of the massive battlefield information in the information field and the application demand of the high-dynamic and concurrent operation unit. As the latest development trend of distributed computing, cloud computing, with the help of advanced virtualization technology, virtual computing, storage, network and other resources of the cloud computing data center into a huge resource pool, providing the user with the on-demand service. In the cloud computing mode, the user only needs to submit the task to the cloud service system, and the execution of the task is completed within the time specified by the user. That is, the user only needs to submit the task, the quality of service requirements, and the result of the execution of the receiving task, and all the things in the middle, the cloud service system will be automatically completed. For the cloud service system, the efficient task scheduling and resource dynamic allocation method is one of the key technologies to improve its performance. At present, there are a lot of research results on the task scheduling and the dynamic allocation of resources in the cloud service system. most of the existing studies, however, focus on the ideal scheduling environment: 1) the scheduled task set is known in advance; 2) the execution time of the task is a determination value and can be acquired prior to scheduling; and 3) the resources are available immediately. However, in the actual cloud service system, there are a lot of dynamic and random factors. For example, the task arrival rate changes dramatically, the task execution time is random, the resources can be dynamically expanded and the resources need time overhead, and the like. These dynamic and random factors in the cloud service system often make the pre-generated scheduling scheme lose the original advantage or can not be implemented smoothly, and even the initial scheduling scheme is no longer feasible. Therefore, the research of real-time task scheduling and resource dynamic allocation in the cloud service system is of great theoretical and practical value and is challenging. In the process of real-time task scheduling and resource dynamic allocation, this paper mainly focuses on three typical situations: the dynamic arrival of the task, the execution time of the task is a random variable, and the starting time of the host and the virtual machine is not negligible. The main work and innovation points of this paper include the following four points: (1) An extensible host organization model is proposed. in that light of the challenge of the large-scale host in the cloud service system to the traditional host organization mode, such as centralized, layered and distributed, propose cooperative organization mode, the large-scale host machine is divided into a plurality of clusters, each cluster has an independent scheduler, each scheduler is responsible for the task scheduling and resource allocation of the cluster, and meanwhile, the schedulers are in coordination with each other, and the tasks and the bottom layer resources are jointly dispatched, so that the expandability of the cloud service system is improved. (2) a random-sensing scheduling framework is proposed. aiming at the characteristics of high dynamic, random and high timeliness requirements of real-time tasks in a cloud service system, a random-sensing scheduling framework is proposed for each cluster, a large part of the waiting tasks are placed in a global waiting queue, and the number of waiting tasks on the virtual machine is controlled, after the virtual machine completes the task, the waiting task is executed immediately, and then the task of higher timeliness requirement in the global queue is preferentially dispatched to the virtual machine to wait, so that the randomness of the completed task is prevented from being accumulated to the currently scheduled task, so as to improve the stability of the scheduling scheme and the capability of ensuring the timeliness of the real-time task. (3) A stochastic perceptive scheduling algorithm (PRS) is proposed. Aiming at the problem of the dynamic arrival of real-time tasks in the cloud service system and the random problem of the execution time, on the basis of the random-aware scheduling framework, the proactive and reactive scheduling ideas are skillfully integrated, an on-line scheduling algorithm PRS is proposed, According to the actual operation condition of the cloud service system, the scheduling algorithm continuously generates a new task and a virtual machine scheduling scheme for the cloud service system so as to improve the effective utilization and the energy consumption of the host resources in the cloud service system under the condition of ensuring the timeliness requirement of the real-time task. (4) The task scheduling and resource dynamic allocation algorithm STARS is proposed. In the cloud service system, the arrival of real-time tasks is random and bursty, and when the load in the cloud service system suddenly increases, the process of starting the host and creating the virtual machine can lead to a certain amount of time overhead, so that some tasks can not start in time, thus delaying their cut-off period. In view of the above problems, this paper puts forward the task scheduling and resource dynamic allocation algorithm STARS of the machine start-up time perception, and the ability of dynamic expansion can be realized by the ability of the single virtual machine CPU, and the influence of the start time of the transfer machine on the short task of the cut-off period is achieved. so as to reduce the effect of the starting time of the machine on the timeliness of the sudden increase task so as to improve the capability of the cloud service system to guarantee the timeliness of the real-time task.
【學位授予單位】：國防科學技術(shù)大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TP393.09
本文編號：2421914
[Abstract]:The cloud computing will provide a new way for battlefield information service mode in the face of the massive battlefield information in the information field and the application demand of the high-dynamic and concurrent operation unit. As the latest development trend of distributed computing, cloud computing, with the help of advanced virtualization technology, virtual computing, storage, network and other resources of the cloud computing data center into a huge resource pool, providing the user with the on-demand service. In the cloud computing mode, the user only needs to submit the task to the cloud service system, and the execution of the task is completed within the time specified by the user. That is, the user only needs to submit the task, the quality of service requirements, and the result of the execution of the receiving task, and all the things in the middle, the cloud service system will be automatically completed. For the cloud service system, the efficient task scheduling and resource dynamic allocation method is one of the key technologies to improve its performance. At present, there are a lot of research results on the task scheduling and the dynamic allocation of resources in the cloud service system. most of the existing studies, however, focus on the ideal scheduling environment: 1) the scheduled task set is known in advance; 2) the execution time of the task is a determination value and can be acquired prior to scheduling; and 3) the resources are available immediately. However, in the actual cloud service system, there are a lot of dynamic and random factors. For example, the task arrival rate changes dramatically, the task execution time is random, the resources can be dynamically expanded and the resources need time overhead, and the like. These dynamic and random factors in the cloud service system often make the pre-generated scheduling scheme lose the original advantage or can not be implemented smoothly, and even the initial scheduling scheme is no longer feasible. Therefore, the research of real-time task scheduling and resource dynamic allocation in the cloud service system is of great theoretical and practical value and is challenging. In the process of real-time task scheduling and resource dynamic allocation, this paper mainly focuses on three typical situations: the dynamic arrival of the task, the execution time of the task is a random variable, and the starting time of the host and the virtual machine is not negligible. The main work and innovation points of this paper include the following four points: (1) An extensible host organization model is proposed. in that light of the challenge of the large-scale host in the cloud service system to the traditional host organization mode, such as centralized, layered and distributed, propose cooperative organization mode, the large-scale host machine is divided into a plurality of clusters, each cluster has an independent scheduler, each scheduler is responsible for the task scheduling and resource allocation of the cluster, and meanwhile, the schedulers are in coordination with each other, and the tasks and the bottom layer resources are jointly dispatched, so that the expandability of the cloud service system is improved. (2) a random-sensing scheduling framework is proposed. aiming at the characteristics of high dynamic, random and high timeliness requirements of real-time tasks in a cloud service system, a random-sensing scheduling framework is proposed for each cluster, a large part of the waiting tasks are placed in a global waiting queue, and the number of waiting tasks on the virtual machine is controlled, after the virtual machine completes the task, the waiting task is executed immediately, and then the task of higher timeliness requirement in the global queue is preferentially dispatched to the virtual machine to wait, so that the randomness of the completed task is prevented from being accumulated to the currently scheduled task, so as to improve the stability of the scheduling scheme and the capability of ensuring the timeliness of the real-time task. (3) A stochastic perceptive scheduling algorithm (PRS) is proposed. Aiming at the problem of the dynamic arrival of real-time tasks in the cloud service system and the random problem of the execution time, on the basis of the random-aware scheduling framework, the proactive and reactive scheduling ideas are skillfully integrated, an on-line scheduling algorithm PRS is proposed, According to the actual operation condition of the cloud service system, the scheduling algorithm continuously generates a new task and a virtual machine scheduling scheme for the cloud service system so as to improve the effective utilization and the energy consumption of the host resources in the cloud service system under the condition of ensuring the timeliness requirement of the real-time task. (4) The task scheduling and resource dynamic allocation algorithm STARS is proposed. In the cloud service system, the arrival of real-time tasks is random and bursty, and when the load in the cloud service system suddenly increases, the process of starting the host and creating the virtual machine can lead to a certain amount of time overhead, so that some tasks can not start in time, thus delaying their cut-off period. In view of the above problems, this paper puts forward the task scheduling and resource dynamic allocation algorithm STARS of the machine start-up time perception, and the ability of dynamic expansion can be realized by the ability of the single virtual machine CPU, and the influence of the start time of the transfer machine on the short task of the cut-off period is achieved. so as to reduce the effect of the starting time of the machine on the timeliness of the sudden increase task so as to improve the capability of the cloud service system to guarantee the timeliness of the real-time task.
【學位授予單位】：國防科學技術(shù)大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TP393.09
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