WO2017045211A1 - 一种多服务质量约束的云计算任务调度方法 - Google Patents
一种多服务质量约束的云计算任务调度方法 Download PDFInfo
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for program control, e.g. control units
- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
- G06F9/46—Multiprogramming arrangements
- G06F9/50—Allocation of resources, e.g. of the central processing unit [CPU]
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/50—Network services
- H04L67/60—Scheduling or organising the servicing of application requests, e.g. requests for application data transmissions using the analysis and optimisation of the required network resources
- H04L67/61—Scheduling or organising the servicing of application requests, e.g. requests for application data transmissions using the analysis and optimisation of the required network resources taking into account QoS or priority requirements
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for program control, e.g. control units
- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
- G06F9/46—Multiprogramming arrangements
- G06F9/50—Allocation of resources, e.g. of the central processing unit [CPU]
- G06F9/5083—Techniques for rebalancing the load in a distributed system
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- the invention relates to the field of cloud computing technologies, in particular to a cloud computing task scheduling method with multiple service quality constraints.
- Cloud computing is receiving extensive attention from academia and industry.
- Cloud computing technology is a further development of distributed computing, parallel computing and grid computing.
- Virtualization technology is used to virtualize computing resources, storage resources and bandwidth resources into a dynamically scalable virtualized resource pool, using the Internet as a carrier to serve The form is available to different users on demand.
- Data centers in a cloud computing environment can effectively reduce the difficulty of operation, maintenance, and management, while also improving data security and reliability.
- users do not have to purchase additional hardware resources at the peak of the network, which not only saves the cost of purchasing hardware, but also saves maintenance and management problems.
- Task scheduling is a very critical and complex problem in cloud computing technology.
- the quality of the problem not only affects user satisfaction, but also greatly affects the overall performance of the system. Therefore, how to reasonably and efficiently schedule tasks and improve user satisfaction in the cloud environment has become the focus and difficulty of cloud computing research.
- task scheduling is an NP-complete problem.
- Cloud computing is in distributed computing, utility computing, grid computing It is generated on the basis of the above, and there are many task scheduling algorithms in cloud computing based on grid computing.
- One of the main goals of the task scheduling algorithm in grid computing is to minimize the time required for all tasks to complete.
- Most task scheduling algorithms optimize task scheduling with this goal.
- the cost, time spent, and load balancing required for task execution are hot spots that are not studied, and the resources for different computing capabilities are also different. For time-sensitive applications, resources that provide greater processing power make the task run less time-consuming; for cost-sensitive user applications, resources that provide lower processing costs make the task run less expensive. And the satisfaction of these different needs requires the use of cloud computing task scheduling algorithms.
- Load balancing is a commonly used method in cloud computing task scheduling. It has the following two forms: First, in static equilibrium, using tasks and execution time to allocate tasks and resources through mathematical methods. . The shortcoming of this strategy is that the resource usage rate is not high, and such a balancing strategy cannot dynamically change for the change of virtual machine information. Second, in dynamic balancing, it is generally achieved through prediction. The algorithm performs prediction based on the current and historical information of the virtual machine, and then performs task scheduling based on the predicted result. For this algorithm, a reasonable standard is very important. Amazon's product EC2 and Yahoo's product-improved HDFS system as well as Google's products In GFS, the actual research and application of load balancing are carried out, so it is of great significance to propose a better cloud computing task scheduling algorithm.
- QoS Quality of Service
- cloud computing systems have developed platforms that can provide different services in order to meet the different needs of different users. These platforms can meet different types of user needs, such as ensuring system reliability, consuming less cost, providing higher data transfer rates, and so on.
- the technical problem solved by the present invention is to provide a cloud computing task scheduling method with multiple quality of service constraints; to propose a task scheduling strategy with multi-dimensional QoS constraints.
- the allocation t1 is performed to the resource; otherwise, the QoS distance of each resource in the t1 to RR set is calculated, and the resource t1 is allocated to the resource with the smallest QoS distance;
- the task of allocating resources is deleted, and then the allocation of the resources is performed according to the method of the previous step; until all tasks are assigned execution resources;
- the unexecuted task is scheduled to the idle resource with the highest QoS satisfaction until the task is completed;
- , which is the number of user tasks, and ti represents the i-th task of the user, i ⁇ [1,n]; ⁇ tID,tLen,tQoS,tSta ⁇ ;
- tID indicates the unique identifier of the task
- tLen indicates the length of the task, unit: MI (Million Instruction);
- tQoS ⁇ QoS 1 , QoS 2 , ..., QoS k ⁇ represents the multi-dimensional QoS requirement of the task, and k represents the QoS dimension;
- , which is the number of physical hosts; Ph ⁇ pID, pType, pSta ⁇ .
- PID indicates the unique identifier of the physical host
- Ptype indicates the type of physical host, such as workstation, mainframe or minicomputer
- Psta indicates the status of the physical host
- Psta ⁇ pFree, pRun ⁇ , pFre indicates that the physical host is idle, that is, no virtual machine is deployed or the virtual machine does not perform the task, and pRun indicates that the physical host is in the working state;
- the resource is represented in the form of a virtual machine.
- , the number of resources (virtual machines) provided for the cloud data center, and rj represents the jth Resources, j ⁇ [1,m];rj ⁇ rID,rCap,rQoS,rSta,rLoc ⁇ ;
- rID indicates the unique identifier of the resource
- rCap indicates the computing power of the resource, in MIPS (Million Instructin Per Second);
- rQoS ⁇ QoS 1 , QoS 2 , ..., QoS k ⁇ represents the multi-dimensional QoS service capability of the resource, and k represents the QoS dimension.
- rLoc indicates the physical host where the resource is located.
- the QoS dimension provided by the resource is k and the number of resources is m
- the QoS provided by the m resources is an m ⁇ k matrix, which is expressed as:
- n ⁇ k matrix the requirements of n tasks on k-dimensional QoS can be expressed as n ⁇ k matrix, expressed as:
- QoS Quality of Service
- negative metrics higher metrics indicate higher quality of service, such as reliability, security, and stability
- lower metrics indicate higher quality of service, such as service charges
- Positive measures are standardized by the formula
- consumption metrics are standardized by the formula
- the normalized matrix is expressed as:
- Equation 1 For positive metrics, the user's satisfaction with the QoS on the dimension is calculated by Equation 1, and for the negative metric, the satisfaction is calculated by Equation 2;
- the average QoS satisfaction for all user tasks is:
- the QoS distance calculation is:
- w j represents the weight of the j-th QoS in the distance calculation; w j uses the dispersion maximization method to determine the size; the larger the difference in the capability of the resource provided on the j-th QoS, indicating that the parameter is measuring distance The greater the impact, the larger the corresponding w j , otherwise, the smaller the w j ; considering the equal availability of all resources in the j-th QoS, the parameter has a influence of 0 when measuring the distance.
- Dj represents the total deviation of the service capabilities of each resource and other resource service capabilities in the j-th QoS
- the present invention is directed to a user task scheduling problem with different QoS target constraints in a cloud computing environment, and a cloud computing task scheduling method with multiple QoS constraints is proposed.
- the method considers the maximum QoS satisfaction and the minimum QoS distance between the task and the resource, constructs the objective function reflecting the QoS service capability, and solves the objective function by the Lagrangian method to obtain the solution of resource selection and task scheduling.
- the invention selects the resource with the minimum QoS distance for mapping under the premise of satisfying the maximum satisfaction of the user task QoS, not only can ensure the task scheduling efficiency in the multi-dimensional QoS constraint, reduce the average task execution time, and can also ensure the resource utilization rate.
- 1 is a cloud computing task scheduling model of the present invention
- the specific steps of the task scheduling of the present invention are:
- the allocation t1 is performed to the resource; otherwise, the QoS distance of each resource in the t1 to RR set is calculated, and the resource t1 is allocated to the resource with the smallest QoS distance;
- the task of allocating resources is deleted, and then the allocation of the resources is performed according to the method of the previous step; until all tasks are assigned execution resources;
- the unexecuted task is scheduled to the idle resource with the highest QoS satisfaction until the task is completed;
- the scheduling algorithm attempts to search for a QoS-optimized task scheduling scheme.
- the QoS description given by tasks and resources can have several categories. For each type of QoS description, the concept of benefit function is introduced to quantify the benefits obtained by the system when it is satisfied to different degrees.
- the QoS description as a metric includes:
- Temporal description A time-related attribute description.
- the temporal QoS description of the task includes the total completion time, the start time, the latest completion time, and the like.
- the temporal QoS description of the resource includes the computing power of the resource and the like. Without loss of generality, consider the latest completion time of the task and the running time of the task derived from the resource computing ability on the resource.
- Reliability Description Long-running tasks may fail due to resource failure. Re-executing the task will result in repeated resource consumption, resulting in reduced system performance. Scheduling tasks according to their required reliability can reduce this. This type of description of the task includes the minimum successful completion rate, etc.; this type of description of the resource includes unit time failure rate and the like.
- Priority Describe the relative importance of the task. Tasks with higher priority need to be executed earlier, while resources with higher priority will be used first when the benefits are the same.
- the QoS description as a policy is mainly the service level, which acts on the above various descriptions, including:
- Soft level There are some QoS descriptions that do not require such strong constraints. If such a description of the task is satisfied, The maximum benefit is obtained, but if it is not satisfied, the dispatch is not considered to be invalid, but the benefit is affected.
- Best-effort level This level is used to represent some QoS descriptions that are not noticed or insignificant. Such descriptions of tasks and resources are to be met or implemented as much as possible.
- a cloud environment is a distributed environment with multiple entities. From the perspective of cloud user entities and cloud resource entities, their QoS objectives in terms of management mechanisms, security policies, and costs are different. For example, the user not only expects the task to be completed for the shortest time, but also minimizes the cost of task execution.
- the cloud resource entity is more concerned with the benefits of its own task execution, while paying attention to the maximum throughput of the entire system.
- the present invention will focus on the following four types of QoS and quantize it:
- n is the number of security parameters
- ti is the weight of the i-th security parameter
- RunTime indicates the normal running time of the resource
- FailureTime indicates the resource expiration time
- Nsuccess represents the number of tasks successfully executed on the resource, and Ntotal represents the total number of tasks assigned to the resource.
- V represents the unit price of resource utilization
- T represents the time when the task runs on the resource.
- the cloud task types considered are mainly computational meta-tasks, and there is no dependency between the tasks.
- the cross-resource execution in the execution process is not considered, that is, the user submits
- the meta-task is the smallest unit of task scheduling.
- Ti ⁇ tID, tLen, tQoS, tSta ⁇ .
- tID indicates the unique identifier of the task.
- tLen indicates the length of the task, unit: MI (Million Instruction);
- tQoS ⁇ QoS 1 , QoS 2 , ..., QoS k ⁇ represents the multi-dimensional QoS requirement of the task, and k represents the QoS dimension.
- the way cloud provides resources is to virtualize physical hosts into multiple virtual machines for resource provision in the data center.
- Ph ⁇ pID, pType, pSta ⁇ .
- PID indicates the unique identifier of the physical host
- Ptype indicates the type of physical host, such as workstation, mainframe or minicomputer
- Psta indicates the status of the physical host
- Psta ⁇ pFree, pRun ⁇ , pFre indicates that the physical host is idle, that is, no virtual machine is deployed or the virtual machine does not perform tasks, and pRun indicates that the physical host is in working state.
- the resource is represented in the form of a virtual machine.
- the number of (virtual machines), rj represents the jth resource, j ⁇ [1,m].
- Rj ⁇ rID,rCap,rQoS,rSta,rLoc ⁇ .
- rID indicates the unique identifier of the resource.
- rCap indicates the computing power of the resource, in MIPS (Million Instructin Per Second);
- rQoS ⁇ QoS 1 , QoS 2 , ..., QoS k ⁇ represents the multi-dimensional QoS service capability of the resource, and k represents the QoS dimension.
- rLoc indicates the physical host where the resource is located.
- the QoS dimension provided by the resource is k and the number of resources is m
- the QoS provided by the m resources is an m ⁇ k matrix, which is expressed as:
- n ⁇ k matrix the requirements of n tasks on k-dimensional QoS can be expressed as n ⁇ k matrix, expressed as:
- the following two matrices are standardized.
- QoS Quality of Service
- a higher positive metric indicates a higher quality of service, such as reliability, security, and stability.
- the lower the consumption metric the higher the quality of service, such as service charges.
- Positive metrics are normalized by Equation 1
- consumption metrics are normalized by Equation 2,
- the normalized matrix is expressed as:
- Task scheduling should maximize the satisfaction of the user's QoS requirements.
- the user's satisfaction with the QoS on the dimension is calculated in Equation 1
- the satisfaction is calculated in Equation 2.
- the average QoS satisfaction for all user tasks is:
- resources with the highest satisfaction rate will be selected for distribution.
- the tasks In order not to make the tasks with low QoS requirements occupy high QoS resources and affect the execution of tasks of other users, resulting in an increase in the total execution time, the tasks should be allocated as much as possible to resources that are similar to their own QoS requirements. Use weighted distance to measure the QoS distance between tasks and resources.
- w j represents the weight of the j-th QoS in the distance calculation. Since the capacity gaps of resources provided in each dimension of QoS are different, in order to better perform distance measurement, the dispersion maximization method is used to determine the size of w j . The larger the difference in the size of the resources provided by the resource in the j-th QoS, the greater the influence of the parameter when measuring the distance, and the corresponding w j is larger, otherwise, w j is smaller. Considering that all resources are equally sized on the j-th QoS, the parameter affects 0 when measuring distance, and w j should be set to zero. For the j-th QoS, D i,j (w) is used to indicate the dispersion of the resource r i from other resources in this QoS service capability.
- Dj represents the total deviation of the service capabilities of each resource from the other resource service capabilities in the j-th QoS.
- the value of the resource QoS integrated service capability and the user QoS comprehensive demand after the QoS parameters are standardized is calculated by the following formula.
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Abstract
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- 一种多服务质量约束的云计算任务调度方法,其特征在于:所有用户对提交的任务给出其需求的QoS描述,所有资源在发布时也给出其提供QoS服务能力描述;调度算法根据这些QoS描述,尝试搜索得到QoS最优化的任务调度方案。
- 根据权利要求1所述的多服务质量约束的云计算任务调度方法,其特征在于:所述的方法具体步骤是:获取任务集T和云资源集R,并提取任务集和资源的QoS;对任务集和资源QoS进行标准化,得到标准化任务QoS矩阵和资源QoS矩阵;计算各为资源的QoS权值;计算任务集中各任务的综合QoS需求,并按照QoS需求将任务降序排列,得到新的任务集TT;对新的任务集TT中的第一个任务t1,计算其到各资源的QoS满意度;得到QoS满意度最大的资源集RR;若最大的资源集RR中只有一个资源,分配t1到该资源执行;否则,计算t1到RR集中各资源的QoS距离,分配t1到QoS距离最小的资源上执行;若有多个任务,则将分配资源的任务删除,然后,按照前一步的方法进行执行资源的分配;直至全部任务分配了执行资源;在任务执行资源分配完成后,轮询任务是否执行完毕;如任务未执行完毕,则检查是否有空闲资源;在有空闲资源情况时,进一 步判断该资源中的最高QoS满意度任务序列中有无未执行完毕的任务;未有空闲资源时,则返回到任务执行完毕与否的轮询;如空闲资源中最高QoS满意度任务执行完毕,则将未执行完毕的任务调度到该最高QoS满意度的空闲资源直至任务执行完毕;如任务执行完毕,则结束。
- 根据权利要求2所述的多服务质量约束的云计算任务调度方法,其特征在于:所述的任务集合表示为T,T={t1,t2,...,tn},n=|T|,为用户任务数量,ti表示用户的第i个任务,i∈[1,n];ti={tID,tLen,tQoS,tSta};tID:表示任务的唯一标识;tLen:表示任务的长度,单位:MI(Million Instruction);tQoS:tQoS={QoS1,QoS2,...,QoSk}表示任务的多维QoS需求,k表示QoS维度;tSta:tSta={tAlloc,tExecu,tSucc},表示用户任务的状态,分三种:tAlloc表示任务待调度状态,tExecu表示任务执行状态,tSucc表示任务执行完成状态;将资源的物理主机集合表法为P,P={p1,p2...,pl},l=|P|,为物理主机数量;Ph={pID,pType,pSta}。PID:表示物理主机的唯一标识;Ptype:表示物理主机的类型,如工作站、大型机或微型机等;Psta:表示物理主机的状态,Psta={pFree,pRun},pFre表示物理主机处于空闲状态,即没有部署虚拟机或虚拟机没有执行任务,pRun表示物理主机处于工作状态;以虚拟机形式表示资源,资源集合表示为R,R={r1,r2,...,rm},m=|R|,为云 数据中心提供的资源(虚拟机)数量,rj表示第j个资源,j∈[1,m];rj={rID,rCap,rQoS,rSta,rLoc};rID:表示资源的唯一标识;rCap:表示资源的计算能力,单位:MIPS(Million Instructin Per Second);rQoS:rQoS={QoS1,QoS2,...,QoSk}表示资源的多维QoS服务能力,k表示QoS维度。rSta:rSta={rRun,rFre}表示资源的状态,分两种:rRun表示资源处于执行任务状态,rFre表示资源处于空闲状态。rLoc:表示资源所在的物理主机。
- 根据权利要求2所述的多服务质量约束的云计算任务调度方法,其特征在于:所述的QoS标准化是:令资源提供的QoS维度为k,资源数量为m,则m个资源所提供的QoS为一个m×k矩阵,表示为:令任务数为n,则n个任务在k维QoS上的需求可表示为n×k矩阵,表示为:然后按一下方式进行标准化处理;首先联立两个矩阵,得到联立矩阵,标准化处理时,将QoS分为积极度量和消极度量;积极度量值越高表明服务质量越高,如可靠性、安全性、稳定性;消耗度量值越低表明服务质量越高,如服务费用;积极度量以式1进行标准化,消耗度量以式2进行标准化;其中,qosi,j表示矩阵QoSm+n,k中第i行第j列QoS值,1<=i<=m+n,1<=j<=k,分别表示在矩阵QoSm+n,k中第j列QoS参数值的最小值和最大值,表示标准化后的第i行第j列的QoS值,标准化矩阵表示为:
- 根据权利要求3所述的多服务质量约束的云计算任务调度方法,其特征在于:所述的QoS标准化是:令资源提供的QoS维度为k,资源数量为m,则m个资源所提供的QoS为一个m×k矩阵,表示为:令任务数为n,则n个任务在k维QoS上的需求可表示为n×k矩阵,表示为:然后按一下方式进行标准化处理;首先联立两个矩阵,得到联立矩阵,标准化处理时,将QoS分为积极度量和消极度量;积极度量值越高表明服务质量越高,如可靠性、安全性、稳定性;消耗度量值越低表明服务质量越高,如服务费用;积极度量以式1进行标准化,消耗度量以式2进行标准化;其中,qosi,j表示矩阵QoSm+n,k中第i行第j列QoS值,1<=i<=m+n,1<=j<=k,分别表示在矩阵QoSm+n,k中第j列QoS参数值的最小值和最大值,表示标准化后的第i行第j列的QoS值,标准化矩阵表示为:
- 根据权利要求2至5任一项所述的多服务质量约束的云计算任务调度方法,其特征在于:所述的QoS距离计算是:利用加权距离来度量任务与资源之间的QoS距离,其中,wj表示第j维QoS在距离计算时所占权重;wj使用离差最大化方法决定大小;资源在第j维QoS上提供的能力大小差距越大,说明该参数在测量距离时的影响就越大,相应的wj越大,否则,wj越小;考虑所有资源在第j维QoS上的提供的能力大小相等,则该参数在测量距离时影响为0,此时应把wj定为0;对于第j维QoS,用Di,j(w)表示资源ri与其他资源在此QoS服务能力的离差,则令Dj表示在第j维QoS上各个资源的服务能力与其他资源服务能力的总离差;用下式计算出在QoS参数标准化后资源QoS综合服务能力和用户QoS综合需求的值;对于各个资源,Uqos(w)越大,其综合QoS服务能力越好;由此构造目标函数,等同于通过拉格朗日法可求得目标函数最大时,
- 根据权利要求6所述的多服务质量约束的云计算任务调度方法,其特征 在于:所述的QoS距离计算是:利用加权距离来度量任务与资源之间的QoS距离,其中,wj表示第j维QoS在距离计算时所占权重;wj使用离差最大化方法决定大小;资源在第j维QoS上提供的能力大小差距越大,说明该参数在测量距离时的影响就越大,相应的wj越大,否则,wj越小;考虑所有资源在第j维QoS上的提供的能力大小相等,则该参数在测量距离时影响为0,此时应把wj定为0;对于第j维QoS,用Di,j(w)表示资源ri与其他资源在此QoS服务能力的离差,则令Dj表示在第j维QoS上各个资源的服务能力与其他资源服务能力的总离差;用下式计算出在QoS参数标准化后资源QoS综合服务能力和用户QoS综合需求的值;对于各个资源,Uqos(w)越大,其综合QoS服务能力越好;由此构造目标函数,等同于通过拉格朗日法可求得目标函数最大时,
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