云服務(wù)系統(tǒng)中實(shí)時(shí)任務(wù)調(diào)度與資源動態(tài)調(diào)配方法研究
發(fā)布時(shí)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(yīng)用需求,云計(jì)算將為戰(zhàn)場信息服務(wù)模式提供了一條新的途徑。云計(jì)算作為分布式計(jì)算的最新發(fā)展趨勢,它借助先進(jìn)的虛擬化技術(shù),將云計(jì)算數(shù)據(jù)中心大規(guī)模的計(jì)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(wù)。在云計(jì)算模式下,用戶只需將任務(wù)提交到云服務(wù)系統(tǒng),云服務(wù)系統(tǒng)將自動分析任務(wù)特性、預(yù)測任務(wù)的資源需求,再根據(jù)云服務(wù)系統(tǒng)中底層資源的使用情況,將任務(wù)調(diào)度到相應(yīng)的資源上執(zhí)行,并在用戶指定的時(shí)間內(nèi)完成任務(wù)的執(zhí)行。即,用戶只需提交任務(wù)、服務(wù)質(zhì)量要求和接收任務(wù)執(zhí)行的結(jié)果,而中間的所有事情,云服務(wù)系統(tǒng)將自動完成。對于云服務(wù)系統(tǒng)而言,高效的任務(wù)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(wù)系統(tǒng)中任務(wù)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(wù)集合預(yù)先知道;2)任務(wù)的執(zhí)行時(shí)間是確定值,并且在調(diào)度前可以獲取;3)資源即時(shí)可用。然而,在實(shí)際的云服務(wù)系統(tǒng)中,存在大量動態(tài)、隨機(jī)性因素。比如,任務(wù)到達(dá)率劇烈變化,任務(wù)執(zhí)行時(shí)間具有隨機(jī)性,資源可動態(tài)伸縮和啟動資源需要時(shí)間開銷等。云服務(wù)系統(tǒng)中這些動態(tài)和隨機(jī)因素,往往使得預(yù)先生成的調(diào)度方案失去原有的優(yōu)勢或無法順利實(shí)施,甚至使得初始調(diào)度方案不再可行。因此,云服務(wù)系統(tǒng)中實(shí)時(shí)任務(wù)調(diào)度和資源動態(tài)調(diào)配方法研究極具理論和現(xiàn)實(shí)價(jià)值,且富有挑戰(zhàn)性。在實(shí)時(shí)任務(wù)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(wù)動態(tài)到達(dá)、任務(wù)執(zhí)行時(shí)間是隨機(jī)變量、主機(jī)和虛擬機(jī)啟動時(shí)間不可忽略。本文的主要工作和創(chuàng)新點(diǎn)包括以下四點(diǎn):(1)提出了一個可擴(kuò)展的主機(jī)組織模式。針對云服務(wù)系統(tǒng)中大規(guī)模主機(jī)對傳統(tǒng)主機(jī)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(jī)分為多個集群,每個集群都有一個獨(dú)立的調(diào)度器,每個調(diào)度器負(fù)責(zé)本集群的任務(wù)調(diào)度和資源調(diào)配,同時(shí)調(diào)度器之間相互協(xié)調(diào),共同調(diào)度任務(wù)和底層資源,從而提高云服務(wù)系統(tǒng)的可擴(kuò)展性。(2)提出一個隨機(jī)性感知的調(diào)度框架。針對云服務(wù)系統(tǒng)中實(shí)時(shí)任務(wù)的高動態(tài)、隨機(jī)性和高時(shí)效性要求的特征,為每個集群提出一個隨機(jī)性感知的調(diào)度框架,將大部分等待任務(wù)放置在全局等待隊(duì)列中,并控制虛擬機(jī)上等待任務(wù)的個數(shù),當(dāng)虛擬機(jī)完成任務(wù)之后,等待任務(wù)就立即執(zhí)行,然后優(yōu)先調(diào)度全局隊(duì)列中時(shí)效性要求較高的任務(wù)到虛擬機(jī)上等待,避免已經(jīng)完成任務(wù)的隨機(jī)性累加到當(dāng)前調(diào)度的任務(wù),從而提高調(diào)度的方案的穩(wěn)定性和保障實(shí)時(shí)任務(wù)時(shí)效性的能力。(3)提出了一個隨機(jī)性感知的調(diào)度算法PRS。針對云服務(wù)系統(tǒng)中實(shí)時(shí)任務(wù)動態(tài)到達(dá)、執(zhí)行時(shí)間具有隨機(jī)性的問題,在隨機(jī)性感知調(diào)度框架的基礎(chǔ)上,巧妙集成前攝性和反應(yīng)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(wù)系統(tǒng)的實(shí)際運(yùn)行情況,不斷為云服務(wù)系統(tǒng)生成新的任務(wù)和虛擬機(jī)調(diào)度方案,從而在保證實(shí)時(shí)任務(wù)時(shí)效性要求的條件下,提高云服務(wù)系統(tǒng)中主機(jī)資源的有效利用和降低能量消耗。(4)提出了一個機(jī)器啟動時(shí)間感知的任務(wù)調(diào)度與資源動態(tài)調(diào)配算法STARS。在云服務(wù)系統(tǒng)中,實(shí)時(shí)任務(wù)的到達(dá)具有隨機(jī)性和突發(fā)性,當(dāng)云服務(wù)系統(tǒng)中的負(fù)載突增時(shí),啟動主機(jī)和創(chuàng)建虛擬機(jī)的過程會造成一定的時(shí)間開銷,使得某些任務(wù)不能及時(shí)開始,從而延誤了它們的截止期。針對以上問題,本文提出機(jī)器啟動時(shí)間感知的任務(wù)調(diào)度與資源動態(tài)調(diào)配算法STARS,借助單個虛擬機(jī)CPU能力可以動態(tài)伸縮的能力,通過轉(zhuǎn)移機(jī)器啟動時(shí)間對截止期較短任務(wù)的影響,以減緩機(jī)器啟動時(shí)間對突增任務(wù)時(shí)效性的影響,以提高云服務(wù)系統(tǒng)保障實(shí)時(shí)任務(wù)時(shí)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(xué)位授予單位】：國防科學(xué)技術(shù)大學(xué)
【學(xué)位級別】：碩士
【學(xué)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(xué)位授予單位】：國防科學(xué)技術(shù)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TP393.09
【參考文獻(xiàn)】
相關(guān)期刊論文 前8條
1 陳黃科;朱曉敏;祝江漢;;不確定云環(huán)境下基于滾動窗口的節(jié)能調(diào)度[J];系統(tǒng)工程理論與實(shí)踐;2014年S1期
2 殷小龍;李君;萬明祥;;云環(huán)境下基于改進(jìn)NSGA Ⅱ的虛擬機(jī)調(diào)度算法[J];計(jì)算機(jī)技術(shù)與發(fā)展;2014年08期
3 尚世鋒;姜進(jìn)磊;鄭緯民;;CWFlow:支持資源自適應(yīng)使用的云工作流框架[J];清華大學(xué)學(xué)報(bào)(自然科學(xué)版);2013年03期
4 張春艷;劉清林;孟珂;;基于蟻群優(yōu)化算法的云計(jì)算任務(wù)分配[J];計(jì)算機(jī)應(yīng)用;2012年05期
5 王凱;侯紫峰;;Xen虛擬機(jī)的虛擬CPU松弛協(xié)同調(diào)度方法[J];計(jì)算機(jī)研究與發(fā)展;2012年01期
6 李強(qiáng);郝沁汾;肖利民;李舟軍;;云計(jì)算中虛擬機(jī)放置的自適應(yīng)管理與多目標(biāo)優(yōu)化[J];計(jì)算機(jī)學(xué)報(bào);2011年12期
7 羅軍舟;金嘉暉;宋愛波;東方;;云計(jì)算:體系架構(gòu)與關(guān)鍵技術(shù)[J];通信學(xué)報(bào);2011年07期
8 王永貴;韓瑞蓮;;基于改進(jìn)蟻群算法的云環(huán)境任務(wù)調(diào)度研究[J];計(jì)算機(jī)測量與控制;2011年05期
相關(guān)博士學(xué)位論文 前2條
1 吳立華;不確定環(huán)境下模具制造車間前攝與反應(yīng)式調(diào)度方法研究[D];廣東工業(yè)大學(xué);2013年
2 唐小勇;異構(gòu)并行分布式系統(tǒng)可信調(diào)度理論與方法研究[D];湖南大學(xué);2013年
相關(guān)碩士學(xué)位論文 前1條
1 沈案;異構(gòu)分布式系統(tǒng)中基于DVS的節(jié)能調(diào)度算法研究與實(shí)現(xiàn)[D];湖南大學(xué);2013年
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