WO2020237727A1 - 一种支持可靠性保障的冷热操模式虚拟机数量评估方法 - Google Patents

一种支持可靠性保障的冷热操模式虚拟机数量评估方法 Download PDF

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WO2020237727A1
WO2020237727A1 PCT/CN2019/090866 CN2019090866W WO2020237727A1 WO 2020237727 A1 WO2020237727 A1 WO 2020237727A1 CN 2019090866 W CN2019090866 W CN 2019090866W WO 2020237727 A1 WO2020237727 A1 WO 2020237727A1
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mode
reliability
cold
hot
operation mode
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PCT/CN2019/090866
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French (fr)
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郭军
刘文凤
张斌
刘晨
侯帅
侯凯
李薇
柳波
王嘉怡
王馨悦
张瀚铎
张娅杰
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东北大学
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F11/00—Error detection; Error correction; Monitoring
    • G06F11/07—Responding to the occurrence of a fault, e.g. fault tolerance
    • G06F11/14—Error detection or correction of the data by redundancy in operation
    • G06F11/1402—Saving, restoring, recovering or retrying
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F11/00—Error detection; Error correction; Monitoring
    • G06F11/07—Responding to the occurrence of a fault, e.g. fault tolerance
    • G06F11/14—Error detection or correction of the data by redundancy in operation
    • G06F11/1479—Generic software techniques for error detection or fault masking
    • G06F11/1482—Generic software techniques for error detection or fault masking by means of middleware or OS functionality
    • G06F11/1484—Generic software techniques for error detection or fault masking by means of middleware or OS functionality involving virtual machines
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management

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  • the invention belongs to the field of cloud computing, and specifically relates to a method for evaluating the number of virtual machines in cold and hot operation modes supporting reliability guarantee.
  • Cloud computing serves a large group of users.
  • the reliability of cloud systems has attracted much attention. Reliability is the fundamental guarantee for the normal operation of the system. In practical applications, various types of cloud service systems will inevitably have various types of failures. However, in order not to affect the normal use of users, the system still needs to maintain normal operation, so the reliability guarantee of the cloud system becomes particularly important.
  • Using redundancy technology can improve the reliability of the entire network system.
  • the backup mode is generally divided into three modes: cold backup, warm backup and hot backup.
  • the static hybrid backup mechanism cannot adapt to the dynamically changing service concurrency and service failure rate operating scenarios, it must adopt a dynamic optimization strategy.
  • adjusting the number of virtual machines in the operating mode according to the current service concurrency will lead to current The reliability of the system changes. Therefore, after determining the number of virtual machines in the operating mode, according to the user's requirements for system reliability, establish a set of adjustment algorithms for various modes that support reliability assurance to adjust the hot and cold backup sets.
  • the present invention proposes a method for evaluating the number of cold and hot operation mode virtual machines that support reliability guarantees, adjust the operation mode virtual machines to meet performance requirements, and adjust the number of cold and hot backup modes based on the reliability requirement R .
  • Step 1 Initialize the parameters: input the current operating mode virtual machine failure rate matrix M, the current system reliability R current , the current system virtual machine set of each mode, wherein each mode includes: cold mode, hot mode, and operation mode;
  • Step 2 Calculate the failure rate M o [i] of each operating mode virtual machine, and sort from largest to smallest to obtain M′, where M o [i] is the failure rate of the i-th operating mode virtual machine;
  • Step 3 If the reliability of the current system R current is greater than the upper reliability threshold R s , then convert a hot mode virtual machine into cold mode, update the set elements in each mode, and go to step 11 to output cold and hot operation mode virtual machines If the current system reliability R current is less than or equal to the upper threshold R s , go to step 4;
  • Step 4 Determine whether the reliability R current of the current system meets the following conditions: R s > R current > R z where R z is the lower reliability threshold. If it is met, go to step 5, if not, go to step 6;
  • Step 5 Determine the failure rate of a single operating mode virtual machine and the failure limit condition size p;
  • Step 5.1 If the failure rate of a single operation mode virtual machine is greater than the failure rate limit p, go to step 5.2, if the failure rate of a single operation mode virtual machine is less than or equal to the failure rate limit p, go to step 5.3;
  • Step 5.2 If there is a virtual machine in the hot mode, switch from the hot mode to the operation mode, if there is a virtual machine in the operation mode, switch from the operation mode to the cold mode, and update the set elements in each mode;
  • Step 5.3 Determine whether to traverse all operation mode virtual machines. If all operation mode virtual machines have been traversed, go to step 11 to output the number of cold and hot operation mode virtual machines; if not traverse all operation mode virtual machines, then Traverse the next virtual machine in operation mode and go to step 5.1;
  • Step 6 Save the current collection, namely VM′ o ⁇ VM curo ,VM′ h ⁇ VM curh ,VM′ c ⁇ VM curc , where VM curo is the current system operation mode collection, and VM′ o is the current system operation mode after saving the collection Set, VM′ h is the current system hot mode set after saving the set, VM′ c is the current system cold mode set after saving the set;
  • Step 7 Add a hot mode virtual machine to the operating mode, and add a virtual machine with the highest failure rate to the cold mode;
  • Step 8 Invoke the MDD algorithm to re-evaluate the reliability R1 of the system, and determine whether the system reliability R1 is greater than the lower reliability threshold R z ; if the system reliability is greater than the lower reliability threshold R z , update the set elements in each mode and turn Go to step 11, output the number of virtual machines in hot and cold operation mode; if the system reliability is less than or equal to the lower reliability threshold R z , go to step 9;
  • Step 9 Determine whether all operation modes have been converted, if all operation mode virtual machines have not been converted, go to step 7; if all operation mode virtual machines have been converted, add a cold mode to the hot mode set , Update the collection elements in each mode, go to step 10;
  • Step 10 Determine whether all cold mode virtual machines are converted to hot mode. If all cold mode virtual machines are not converted to hot mode, then traverse the next virtual machine and go to step 6. If all cold mode virtual machines are converted to hot mode, Go to step 11;
  • Step 11 Output the number of virtual machines in hot and cold operation mode
  • the present invention adopts a method for evaluating the number of virtual machines in cold and hot operating modes that supports reliability guarantees. Since during system operation, the time for transferring from the hot mode to the operating mode can be ignored, but the replacement time from the cold mode to the hot mode is longer and cannot be ignored. So when we assign to virtual machines with more hot standby modes, the reliability is improved but the energy consumption increases. When the number of hot mode allocations is too small, in a short period of time, if multiple virtual machines fail, but because There are not enough hot standby virtual machines to replace, but to replace from cold standby virtual machines, which will seriously affect the normal operation of the system, because it takes a lot of time from cold mode to operation mode, which may cause system crash .
  • the present invention adjusts the number of cold and hot backup modes on the basis of meeting the reliability requirement R when the number of virtual machines in the operation mode changes, so that the system meets the reliability requirements and is reliable.
  • the level of performance is reflected by the average response time and average request failure rate. The higher the reliability, the lower the average response time and the lower the average request failure rate.
  • FIG. 1 is a flowchart of a method for evaluating the number of virtual machines in cold and hot operation modes supporting reliability guarantee according to an embodiment of the present invention
  • Fig. 2 is the average response time of the three methods according to the embodiment of the present invention.
  • FIG. 3 is the average request failure rate of the three methods in the embodiment of the present invention.
  • the present invention proposes a method for evaluating the number of virtual machines in cold and hot operation modes supporting reliability guarantees, as shown in Figure 1, and the specific steps are as follows:
  • Step 1 Initialize the parameters: input the current operating mode virtual machine failure rate matrix M, the current system reliability R current , the current system virtual machine set of each mode, wherein each mode includes: cold mode, hot mode, and operation mode;
  • Step 2 Calculate the failure rate M o [i] of each operating mode virtual machine, and sort from largest to smallest to obtain M′, where M o [i] is the failure rate of the i-th operating mode virtual machine;
  • Step 3 If the reliability of the current system R current is greater than the upper reliability threshold R s , then convert a hot mode virtual machine into cold mode, update the set elements in each mode, and go to step 11 to output cold and hot operation mode virtual machines If the current system reliability R current is less than or equal to the upper threshold R s , go to step 4;
  • Step 4 Determine whether the reliability R current of the current system meets the following conditions: R s > R current > R z where R z is the lower reliability threshold. If it is met, go to step 5, if not, go to step 6;
  • Step 5 Determine the failure rate of a single operating mode virtual machine and the failure limit condition size p;
  • Step 5.1 If the failure rate of a single operation mode virtual machine is greater than the failure rate limit p, go to step 5.2, if the failure rate of a single operation mode virtual machine is less than or equal to the failure rate limit p, go to step 5.3;
  • Step 5.2 If there is a virtual machine in the hot mode, switch from the hot mode to the operation mode, if there is a virtual machine in the operation mode, switch from the operation mode to the cold mode, and update the set elements in each mode;
  • Step 5.3 Determine whether to traverse all operation mode virtual machines. If all operation mode virtual machines have been traversed, go to step 11 to output the number of cold and hot operation mode virtual machines; if not traverse all operation mode virtual machines, then Traverse the next virtual machine in operation mode and go to step 5.1;
  • Step 6 Save the current collection, namely VM′ o ⁇ VM curo ,VM′ h ⁇ VM curh ,VM′ c ⁇ VM curc , where VM curo is the current system operation mode collection, and VM′ o is the current system operation mode after saving the collection Set, VM′ h is the current system hot mode set after saving the set, VM′ c is the current system cold mode set after saving the set;
  • Step 7 Add a hot mode virtual machine to the operating mode, and add a virtual machine with the highest failure rate to the cold mode;
  • Step 8 Call the MDD algorithm (multi-value decision graph algorithm) to re-evaluate the reliability of the system R1, and determine whether the system reliability is greater than the lower reliability threshold R z ; if the system reliability is greater than the lower reliability threshold R z , update each mode Go to step 11 to output the number of virtual machines in hot and cold operation modes; if the system reliability is less than or equal to the lower reliability threshold R z , go to step 9;
  • MDD algorithm multi-value decision graph algorithm
  • Step 9 Determine whether all operation modes have been converted, if all operation mode virtual machines have not been converted, go to step 7; if all operation mode virtual machines have been converted, add a cold mode to the hot mode set , Update the collection elements in each mode, go to step 10;
  • Step 10 Determine whether all cold mode virtual machines are converted to hot mode. If all cold mode virtual machines are not converted to hot mode, then traverse the next virtual machine and go to step 6. If all cold mode virtual machines are converted to hot mode, Go to step 11;
  • Step 11 Output the number of virtual machines in hot and cold operation mode.
  • the present invention adjusts the number of cold and hot backup modes on the basis of meeting the reliability requirement R when the number of virtual machines in the operation mode changes, so that the system meets the reliability requirements and is reliable.
  • the level of performance is reflected by the average response time and average request failure rate. The higher the reliability, the lower the average response time and the lower the average request failure rate.
  • the first comparison method is the average response time of all systems in operating modes
  • the second comparison method is the average response time of the traditional backup mode.
  • the method in this paper is the average response time of the method of the present invention; from Figure 2 It can be seen that the average response time used in the present invention is the lowest, indicating that the system is the most reliable; as shown in Figure 3, the first comparison method is the average request failure rate when the system is all operating modes, and the second comparison method is the average request failure rate of the traditional backup mode.
  • Request failure rate the method herein is the average request failure rate of the method of the present invention; it can be seen from FIG. 3 that the average request failure rate used in the present invention is the lowest, indicating that the system is the most reliable.

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Abstract

本发明提出一种支持可靠性保障的冷热操模式虚拟机数量评估方法，包括：初始化参数；虚拟机的失效率排序；当前系统的可靠性与可靠性上限阈值比较，进行模式转换；前系统的可靠性与可靠性下限阈值比较，进行模式转换；单个操作模式虚拟机失效率与失效限定条件比较，进行模式转换；调用MDD算法重新评估系统的可靠性R1，系统可靠性R1与可靠性下限阈值比较，进行模式转换。为了使虚拟机冷热模式之间的数量分配达到平衡，本发明在操作模式虚拟机数量变化时，在满足可靠性要求R的基础上，调整冷热备份模式数量使系统满足可靠性要求，可靠性的高低通过平均响应时间和平均请求失败率来反映，实验证明本发明可以使得系统可靠性提高。

Description

一种支持可靠性保障的冷热操模式虚拟机数量评估方法 技术领域
本发明属于云计算领域，具体涉及一种支持可靠性保障的冷热操模式虚拟机数量评估方法。
背景技术
云计算服务于庞大的用户群体，随着云计算技术的发展，云系统可靠性问题备受人们关注。可靠性是系统正常运作的根本保障。在实际应用中，各种类型的云服务系统都不可避免的会出现各种类型的失效情况。但为了不影响用户的正常使用，系统仍需保持正常运行，因此云系统可靠性保障问题变得尤为重要。用冗余技术可以提高整个网络系统的可靠性。备份模式一般分为冷备份、暖备份和热备份三种模式。
由于静态的混合备用机制无法适应动态变化的服务并发量和服务失效率的运行场景，故必须采用动态优化策略，但由于根据当前的服务并发量对操作模式虚拟机的数量进行调整，会导致当前系统的可靠性发生变化，因此，在确定操作模式虚拟机的数量以后，根据用户对系统可靠性的要求建立支持可靠性保障的各模式集合调整算法调整冷热备份集合。
发明内容
基于以上技术问题，本发明提出一种支持可靠性保障的冷热操作模式虚拟机数量评估方法，为满足性能要求调整操作模式虚拟机，在满足可靠性要求R的基础上调整冷热备份模式数量。
一种支持可靠性保障的冷热操作模式虚拟机数量评估方法，具体步骤如下：
步骤1：初始化参数：输入当前操作模式虚拟机失效率矩阵M，当前系统可靠性R current，当前系统各模式虚拟机集合，其中所述各模式包括：冷模式、热模式、操作模式；
步骤2：计算每台操作模式虚拟机的失效率M o[i]，并从大到小进行排序得到M′，其中，M o[i]为第i台操作模式虚拟机的失效率；
步骤3：若当前系统的可靠性R current大于可靠性上限阈值R s，则转换一台热模式虚拟机进入冷模式，更新各模式中的集合元素，转到步骤11，输出冷热操模式虚拟机数量；若当前系统的可靠性R current小于等于上限阈值R s，转到步骤4；
步骤4：判断当前系统的可靠性R current是否满足如下条件：R s>R current>R z其中，R z为可靠性下限阈值，若满足则转到步骤5，若不满足，则转到步骤6；
步骤5：判断单个操作模式虚拟机失效率与失效限定条件大小p；
步骤5.1：若单个操作模式虚拟机失效率大于失效率限定条件p，则转到步骤5.2，若单个操作模式虚拟机失效率小于等于失效率限定条件p，则转到步骤5.3；
步骤5.2：若存在有虚拟机在热模式状态，则从热模式转到操作模式，若存在虚拟机在操作模式，则从操作模式转换到冷模式，更新各模式中的集合元素；
步骤5.3：判断是否遍历完所有的操作模式虚拟机，若已经遍历完所有操作模式虚拟机，则转到步骤11，输出冷热操模式虚拟机数量；若没有遍历完所有操作模式虚拟机，则遍历下一台操作模式虚拟机，转到步骤5.1；
步骤6：保存当前集合，即VM′ o←VM curo,VM′ h←VM curh,VM′ c←VM curc，其中VM curo为当前系统操作模式集合，VM′ o为保存集合后当前系统操作模式集合，VM′ h为保存集合后当前系统热模式集合，VM′ c为保存集合后当前系统冷模式集合；
步骤7：将一台热模式虚拟机添加到操作模式中，一台失效率最高的操作模式虚拟机添加到冷模式中；
步骤8：调用MDD算法重新评估系统的可靠性R1，判断系统可靠性R1是否大于可靠性下限阈值R z；若系统可靠性大于可靠性下限阈值R z，则更新各模式中的集合元素，转到步骤11，输出冷热操模式虚拟机数量；若系统可靠性小于等于可靠性下限阈值R z，则转到步骤9；
步骤9：判断所有的操作模式是否转换完，若所有的操作模式虚拟机没有转换完，则转到步骤7；若所有的操作模式虚拟机转换完，则将一台冷模式添加到热模式集合中，更新各模式中的集合元素，转到步骤10；
步骤10：判断冷模式虚拟机是否全部转换为热模式，若冷模式虚拟机没有全部转换为热模式，则遍历下一台虚拟机转到步骤6，若冷模式虚拟机全部转换为热模式，则转到步骤11；
步骤11：输出冷热操模式虚拟机数量；
有益技术效果：
本发明采用支持可靠性保障的冷热操作模式虚拟机数量评估方法，由于在系统运行中，热模式转移到操作模式时间可以忽略，但是冷模式到热模式替换时间较长不可忽略。所以当我们分配给热备用模式较多的虚拟机时，可靠性提高了但能耗增加，当热模式分配的数量过少时，在很短的时间内如果有多台虚拟机发生失效，但是因为没有足够的热备用虚拟机去替换，而是要从冷备用的虚拟机去替换，这样就会严重影响系统的正常运行，因为冷模式到操 作模式要花费大量的时间，很有可能导致系统崩溃。为了使虚拟机冷热模式之间的数量分配达到平衡，本发明在操作模式虚拟机数量变化时，在满足可靠性要求R的基础上，调整冷热备份模式数量使系统满足可靠性要求，可靠性的高低通过平均响应时间和平均请求失败率来反映，可靠性越高，则平均响应时间越低，平均请求失败率越低。
附图说明
图1为本发明实施例的一种支持可靠性保障的冷热操模式虚拟机数量评估方法流程图；
图2为本发明实施例的三种方法的平均响应时间。
图3为本发明实施例的三种方法的平均请求失败率。
具体实施方式
下面结合附图和具体实施实例对发明做进一步说明，本发明提出一种支持可靠性保障的冷热操模式虚拟机数量评估方法，如图1所示，具体步骤如下：
步骤1：初始化参数：输入当前操作模式虚拟机失效率矩阵M，当前系统可靠性R current，当前系统各模式虚拟机集合，其中所述各模式包括：冷模式、热模式、操作模式；
当前系统操作模式数量为k，热模式虚拟机个数为m 1；OM虚拟机k在下一时刻的失效率矩阵为M o[k]；当前系统操作模式集合为VM curo＝{VM 1，VM 2，…，VM k}；当前系统热模式集合为VM curh＝{VM c1,VM c2,…,VM cm1}，VM curc＝{VM c1,VM c2,…,VM ci1}为冷模式集合，当前系统可靠性R current；
步骤2：计算每台操作模式虚拟机的失效率M o[i]，并从大到小进行排序得到M′，其中，M o[i]为第i台操作模式虚拟机的失效率；
步骤3：若当前系统的可靠性R current大于可靠性上限阈值R s，则转换一台热模式虚拟机进入冷模式，更新各模式中的集合元素，转到步骤11，输出冷热操模式虚拟机数量；若当前系统的可靠性R current小于等于上限阈值R s，转到步骤4；
步骤4：判断当前系统的可靠性R current是否满足如下条件：R s>R current>R z其中，R z为可靠性下限阈值，若满足则转到步骤5，若不满足，则转到步骤6；
步骤5：判断单个操作模式虚拟机失效率与失效限定条件大小p；
步骤5.1：若单个操作模式虚拟机失效率大于失效率限定条件p，则转到步骤5.2，若单个操作模式虚拟机失效率小于等于失效率限定条件p，则转到步骤5.3；
步骤5.2：若存在有虚拟机在热模式状态，则从热模式转到操作模式，若存在虚拟机在操作模式，则从操作模式转换到冷模式，更新各模式中的集合元素；
步骤5.3：判断是否遍历完所有的操作模式虚拟机，若已经遍历完所有操作模式虚拟机，则转到步骤11，输出冷热操模式虚拟机数量；若没有遍历完所有操作模式虚拟机，则遍历下一台操作模式虚拟机，转到步骤5.1；
步骤6：保存当前集合，即VM′ o←VM curo,VM′ h←VM curh,VM′ c←VM curc，其中VM curo为当前系统操作模式集合，VM′ o为保存集合后当前系统操作模式集合，VM′ h为保存集合后当前系统热模式集合，VM′ c为保存集合后当前系统冷模式集合；
步骤7：将一台热模式虚拟机添加到操作模式中，一台失效率最高的操作模式虚拟机添加到冷模式中；
步骤8：调用MDD算法(多值决策图算法)重新评估系统的可靠性R1，判断系统可靠性是否大于可靠性下限阈值R z；若系统可靠性大于可靠性下限阈值R z，则更新各模式中的集合元素，转到步骤11，输出冷热操模式虚拟机数量；若系统可靠性小于等于可靠性下限阈值R z，则转到步骤9；
步骤9：判断所有的操作模式是否转换完，若所有的操作模式虚拟机没有转换完，则转到步骤7；若所有的操作模式虚拟机转换完，则将一台冷模式添加到热模式集合中，更新各模式中的集合元素，转到步骤10；
步骤10：判断冷模式虚拟机是否全部转换为热模式，若冷模式虚拟机没有全部转换为热模式，则遍历下一台虚拟机转到步骤6，若冷模式虚拟机全部转换为热模式，则转到步骤11；
步骤11：输出冷热操模式虚拟机数量。
实验说明；
为了使虚拟机冷热模式之间的数量分配达到平衡，本发明在操作模式虚拟机数量变化时，在满足可靠性要求R的基础上，调整冷热备份模式数量使系统满足可靠性要求，可靠性的高低通过平均响应时间和平均请求失败率来反映，可靠性越高，则平均响应时间越低，平均请求失败率越低。如图2与图3所示，对照方法一为系统全部都是操作模式的平均响应时间，对照方法二为传统备份模式的平均响应时间，本文方法即本发明方法的平均响应时间；从图2中可以看出，本发明所用的平均响应时间最低，说明系统最可靠；如图3所示，对照方法一为系统全部都是操作模式的平均请求失败率，对照方法二为传统备份模式的平均请求失败率，本文方法即本发明方法的平均请求失败率；从图3中可以看出，本发明所用的平均请求 失败率最低，说明系统最可靠。

Claims (2)

  1. 一种支持可靠性保障的冷热操模式虚拟机数量评估方法，其特征在于，具体步骤如下：
    步骤1：初始化参数：输入当前操作模式虚拟机失效率矩阵M，当前系统可靠性R current，当前系统各模式虚拟机集合；
    步骤2：计算每台操作模式虚拟机的失效率M o[i]，并从大到小进行排序得到M′，其中，M o[i]为第i台操作模式虚拟机的失效率；
    步骤3：若当前系统的可靠性R current大于可靠性上限阈值R s，则转换一台热模式虚拟机进入冷模式，更新各模式中的集合元素，转到步骤11，输出冷热操模式虚拟机数量；若当前系统的可靠性R current小于等于上限阈值R s，转到步骤4；
    步骤4：判断当前系统的可靠性R current是否满足如下条件：R s>R current>R z其中，R z为可靠性下限阈值，若满足则转到步骤5，若不满足，则转到步骤6；
    步骤5：判断单个操作模式虚拟机失效率与失效限定条件大小p；
    步骤5.1：若单个操作模式虚拟机失效率大于失效率限定条件p，则转到步骤5.2，若单个操作模式虚拟机失效率小于等于失效率限定条件p，则转到步骤5.3；
    步骤5.2：若存在有虚拟机在热模式状态，则从热模式转到操作模式，若存在虚拟机在操作模式，则从操作模式转换到冷模式，更新各模式中的集合元素；
    步骤5.3：判断是否遍历完所有的操作模式虚拟机，若已经遍历完所有操作模式虚拟机，则转到步骤11，输出冷热操模式虚拟机数量；若没有遍历完所有操作模式虚拟机，则遍历下一台操作模式虚拟机，转到步骤5.1；
    步骤6：保存当前集合，即VM′ o←VM curo,VM′ h←VM curh,VM′ c←VM curc，其中VM curo为当前系统操作模式集合，VM′ o为保存集合后当前系统操作模式集合，VM′ h为保存集合后当前系统热模式集合，VM′ c为保存集合后当前系统冷模式集合；
    步骤7：将一台热模式虚拟机添加到操作模式中，一台失效率最高的操作模式虚拟机添加到冷模式中；
    步骤8：调用MDD算法重新评估系统的可靠性R1，判断系统可靠性是否大于可靠性下限阈值R z；若系统可靠性大于可靠性下限阈值R z，则更新各模式中的集合元素，转到步骤11，输出冷热操模式虚拟机数量；若系统可靠性小于等于可靠性下限阈值R z，则转到步骤9；
    步骤9：判断所有的操作模式是否转换完，若所有的操作模式虚拟机没有转换完，则转到步骤7；若所有的操作模式虚拟机转换完，则将一台冷模式添加到热模式集合中，更新各模式中的集合元素，转到步骤10；
    步骤10：判断冷模式虚拟机是否全部转换为热模式，若冷模式虚拟机没有全部转换为热模式，则遍历下一台虚拟机转到步骤6，若冷模式虚拟机全部转换为热模式，则转到步骤11；
    步骤11：输出冷热操模式虚拟机数量。
  2. 根据权利要求1所述支持可靠性保障的冷热操模式虚拟机数量评估方法，其特征在于，所述各模式包括：冷模式、热模式、操作模式。
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