WO2022041271A1 - 一种轨交应用的考虑时间和费用的云数据资源调度方法 - Google Patents

一种轨交应用的考虑时间和费用的云数据资源调度方法 Download PDF

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WO2022041271A1
WO2022041271A1 PCT/CN2020/112720 CN2020112720W WO2022041271A1 WO 2022041271 A1 WO2022041271 A1 WO 2022041271A1 CN 2020112720 W CN2020112720 W CN 2020112720W WO 2022041271 A1 WO2022041271 A1 WO 2022041271A1
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cloud
cost
task
scheduling
deadline
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PCT/CN2020/112720
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French (fr)
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夏泽宇
夏钢
方芳
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苏州铭冠软件科技有限公司
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    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06FELECTRIC DIGITAL DATA PROCESSING
    • G06F9/00Arrangements for program control, e.g. control units
    • G06F9/06Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
    • G06F9/44Arrangements for executing specific programs
    • G06F9/455Emulation; Interpretation; Software simulation, e.g. virtualisation or emulation of application or operating system execution engines
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06FELECTRIC DIGITAL DATA PROCESSING
    • G06F9/00Arrangements for program control, e.g. control units
    • G06F9/06Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
    • G06F9/46Multiprogramming arrangements
    • G06F9/50Allocation of resources, e.g. of the central processing unit [CPU]

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  • the invention relates to the application field of big data and cloud computing, in particular to a cloud computing data resource scheduling method considering time and cost in rail transit industry applications.
  • this patent will study the scheduling problem in the cloud from the user's point of view, according to the user's demand for deadlines and cost requirements, so as to improve the utilization rate of the private cloud.
  • the scheduling method proposed in this patent is relatively simple, does not involve differential and integral operations, and has relatively low complexity.
  • the present invention provides a scheduling model and method based on deadline and cost constraints in the cloud, which solves the problem that the existing cloud scheduling mostly focuses on a certain index in the scheduling, cost and performance of a single cloud. And the problem of not considering the cost of the user side.
  • a cloud computing data resource scheduling method considering time and cost comprising the steps:
  • R u is:
  • C u is the computing capability of public cloud resources
  • L u is the transmission capability
  • P u is the computing price
  • S u is the storage price
  • C r is the computing capability of private cloud resources
  • L r is the transmission capability
  • Task Ti is defined as:
  • T ⁇ i is the priority weight value of cost or time
  • TD i is the deadline of the task
  • TC i is the size of the task
  • TL i is the amount of information required for task calculation
  • TM i is the budget cost of the task
  • the completion time t i R u and t i R r of the task T i in the public cloud and the private cloud can be calculated as follows:
  • the deadline constraints are t i R u ⁇ TD i and t i R r ⁇ TD i ;
  • the cost constraint is F i ⁇ TM i ;
  • the cost priority scheduling strategy is artificially set, or when T ⁇ i is 0, the priority strategy is adaptively judged, that is, the task is scheduled to the private cloud first and the cost is reduced when the deadline constraints are met.
  • the cost constraint condition F i ⁇ TM i must also be satisfied . ;
  • the cost problem of n tasks is transformed into the following optimization problem, and the related optimization and constraints are described as follows:
  • the priority strategy is adaptively determined, that is, the task is prioritized to the resource that completes the task faster when the cost constraint is satisfied.
  • the aforementioned cloud data resource scheduling method for rail transit applications considering time and cost is characterized in that: the task size in the formula (3) is the task code amount.
  • the aforementioned cloud data resource scheduling method for rail transit applications considering time and cost is characterized in that: the cost is generated on the lease of the public cloud, and the cost includes computing cost and storage cost.
  • the aforementioned cloud data resource scheduling method considering time and cost for rail transit applications is characterized in that: if the T ⁇ i is 1, in order to consider the cost priority scheduling strategy, if T ⁇ i is 2, in order to consider the deadline to give priority to Scheduling strategy, if T ⁇ i is 0, it is an automatic judgment scheduling strategy.
  • the aforementioned cloud data resource scheduling method considering time and cost for rail transit application is characterized in that: the principle that private cloud is given priority in the step 5) is: if the completion time of the public cloud and the private cloud is the same or similar, the task It will be dispatched to the private cloud first, otherwise it will be dispatched to the public cloud.
  • the aforementioned cloud data resource scheduling method considering time and cost for rail transit applications is characterized in that: the cloud computing data resource scheduling model includes a user interface, a task request manager, and a scheduling manager; The task request is sent to the request manager, and the task request manager is used to transmit the task request information to the scheduling manager. According to the task requirements, the scheduling manager combines the resource information of the public cloud and the private cloud stored in the scheduling manager to use cloud scheduling. The method schedules the task to the public cloud or private cloud, and returns the result to the user after completing the task processing.
  • the above-mentioned cloud data resource scheduling method considering time and cost for rail transit application is characterized in that: the task request information includes task size, required data amount, required completion deadline and budget cost.
  • the aforementioned cloud data resource scheduling method considering time and cost for rail transit applications is characterized in that: the information of the public cloud and private cloud resources includes the computing capability, transmission capability, and computing price of the resource.
  • the present invention proposes two scheduling strategies considering time and cost constraints—deadline priority and cost priority strategy, and establishes a task and resource model in the cloud, which can be based on the task requirements submitted by users and artificial intelligence.
  • the defined priority strategy or adaptive selection of appropriate scheduling resources, tasks with higher deadlines can be prioritized to the public cloud, and tasks with high cost requirements can be prioritized to the private cloud, and both strategies meet deadlines and Certain cost constraints; the present invention is simple to calculate and does not involve complex algorithms.
  • Figure 1 is a schematic diagram of the cloud scheduling model.
  • a cloud data resource scheduling model considering time and cost for rail transit applications includes a user interface, a task request manager and a scheduling manager; the user interface is used to send the task request submitted by the user to the task request The manager, the task request manager is used to transmit the task request information to the scheduling manager, and the scheduling manager uses the big data cloud computing of the present invention in combination with the resource information of the public cloud and the private cloud stored in the scheduling manager according to the task requirements.
  • the data resource scheduling method schedules tasks to the public cloud or private cloud, and returns the result to the user after completing the task processing.
  • Task request information includes task weight, task size, amount of data required, deadline for completion, and budget costs, etc.; information on public cloud and private cloud resources, including computing power, transmission capacity, and computing price of the two resources.
  • the resources in the cloud are divided into public cloud resources and private cloud resources, and the resources described in the present invention mainly represent virtual machines.
  • R u is:
  • C u is the computing power of public cloud resources, which affects the computing time of the task
  • Lu is the transmission capacity, which affects the transmission time of the task
  • P u is the computing price
  • Su is the storage price
  • C r is the computing capability of private cloud resources, which affects the computing time of the task
  • L r is the transmission capability, which affects the task transmission time.
  • task T i be a unit of task request, and each resource processes one task at a time, then task T i is defined as:
  • T ⁇ i is the priority weight value of cost or time. If it is 1, it is considered that the priority scheduling strategy is considered to be cost. If it is 2, it is considered that the priority scheduling strategy is considering the deadline. If it is 0, it is considered that the scheduling strategy is automatically determined. In this way, cost or time priority scheduling can be set manually, TD i is the deadline of the task; TC i is the size of the task; TL i is the amount of information required for task calculation; TM i is the budget cost of the task.
  • the completion time t i R u and t i R r of the task T i in the public cloud and the private cloud can be calculated as follows:
  • the deadline constraints are t i R u ⁇ TD i and t i R r ⁇ TD i .
  • the present invention assumes that the cost is mainly generated on the public cloud, and is mainly composed of two parts: calculation cost and storage cost. Ignoring the transmission cost, the calculation formula of the cost F i of the task T i on the public cloud is as follows:
  • the cost constraint is F i ⁇ TM i .
  • Cost-First a cost-first scheduling strategy
  • Time-First a deadline-first scheduling strategy
  • the cost priority scheduling strategy is manually set, or when T ⁇ i is 0, the priority strategy is adaptively judged, that is, tasks are scheduled to the private cloud preferentially when the deadline constraints are met to reduce costs.
  • the priority strategy is adaptively judged, that is, tasks are scheduled to the private cloud preferentially when the deadline constraints are met to reduce costs.
  • the cost constraint condition F i ⁇ TM must also be satisfied. i ;
  • the cost problem of n tasks is transformed into the following optimization problem, and the related optimization and constraints are described as follows:
  • the user sends the task request to the request manager through the user interface, the request manager transmits the task request information to the scheduling manager, and the scheduling manager combines the public cloud and private data stored in the scheduling manager according to the task requirements.
  • the cloud scheduling method of the present invention is used to schedule tasks to a public cloud or a private cloud for completion, and the result is returned to the user.
  • the scheduling method of the invention is simple and practical, only needs to perform condition judgment on tasks, does not involve integral and differential operations, and has low algorithm complexity.

Abstract

一种轨交应用的考虑时间和费用的云数据资源调度方法,提出了在截止时间优先和费用优先的轨道交通行业应用的数据资源调度策略,建立了云数据中的任务和资源调用与计算模型,能够根据用户提交的任务需求人为设定或自适应选择合适的调度资源,对截止时间要求比较高的任务可以优先调度至公有云,对费用要求高的任务可以优先调度至私有云,而且两种策略均满足截止时间和一定的费用约束。能够根据轨道交通行业的数据使用任务目标自适应选择调度资源或根据人为设定的调度策略进行调度,方便简单。

Description

一种轨交应用的考虑时间和费用的云数据资源调度方法 技术领域
本发明涉及大数据和云计算的应用领域,具体涉及在轨道交通行业应用中的一种考虑时间和费用的云计算数据资源调度方法。
背景技术
轨道交通行业的信息化和数据化运营管理越来越依靠大数据和云计算技术开展复杂而高可靠的管理要求,同时公益性服务必须降低各种成本费用。在大数据云计算环境中常常采用虚拟化数字技术,将服务器整体虚拟化为一个数据资源池,由于数据资源种类多、规模大,因此云数据资源调度成为大数据云计算研究的热点之一。数据云包括本地私有云和计算能力更高的公有云,如一些需要更强计算能力和存储能力的应用,或在高峰时段私有云资源过载,不能同时处理更多的应用任务。但如何平衡调用云资源是个重要问题,现有研究方法中很多是侧重于云中单一云的调度问题。本专利将针对这些问题,从用户的角度出发,针对用户对截止时间的需求差异以及费用的要求,研究云中的调度问题,以提高私有云的利用率。本专利提出的调度方法较简单,不涉及微分、积分运算,复杂度相对较低。
发明内容
为解决现有技术中的不足,本发明提供一种云中基于截止时间和费用约束的调度模型及方法,解决了现有云调度中多侧重于单一云的调度、费用和性能中某一个指标及未考虑用户方的成本费用的问题。
为了实现上述目标,本发明采用如下技术方案:一种考虑时间和费用的云计算数据资源调度方法,包括步骤:
1)资源描述:
定义公有云资源:R u为:
R u=<C u,L u,P u,S u>         (1)
其中,C u为公有云资源计算能力;L u为传输能力;P u为计算价格,S u为存储价格;
定义私有云资源R r
R r=<C r,L r>       (2)
其中,C r为私有云资源计算能力;L r为传输能力;
2)任务描述:
任务T i定义为:
Ti=<Tλ i,TD i,TC i,TL i,TM i>      (3)
其中,Tλ i为费用或时间的优先级权重值,TD i为任务的截止时间;TC i为任务大小;TL i为任务计算所需信息量;TM i为任务的预算花费;
3)调度约束描述:
由任务和云资源的定义可计算任务T i分别在公有云和私有云中的完成时间t iR u、t iR r如下:
Figure PCTCN2020112720-appb-000001
Figure PCTCN2020112720-appb-000002
截止时间约束条件为t iR u≤TD i和t iR r≤TD i
任务T i在公有云上的花费F i计算公式如下:
F i=TC i×P u+TL i×Q u        (6)
费用约束条件为F i≤TM i
4)费用优先调度策略:
当Tλ i为1时,即人为设定费用优先调度策略,或者当Tλ i为0时,自适应判断优先策略,即在满足截止时间约束条件下优先将任务调度至私有云,减少费用,首先判断任务T i在私有云的完成时间约束,若t iR r≤TD i,则调度至私有云,否则调度至公有云,调度至公有云时,也要满足费用约束条件F i≤TM i;n个任务的花费问题转化为以下优化问题,相关优化及约束描述如下:
最小费用公式为:
Figure PCTCN2020112720-appb-000003
公有云截止时间约束条件:
Figure PCTCN2020112720-appb-000004
私有云截止时间约束条件:
Figure PCTCN2020112720-appb-000005
费用时间约束条件:
F i=TC i×P u+TL i×Q u≤TM i        (10)
5)截止时间优先调度策略:
当Tλ i为2时,即截止时间优先时,或当Tλ i为0时,自适应判断优先策略时,即在满足费用约束条件下优先将任务调度至完成该任务较快的资源上,首先判断任务在公有和私有云资源的完成时间,将任务调度至能尽早完成的资源上,在同等条件下,兼顾私有云优先的原则,调度至公有云时,首先需要判断费用约束条件,相关优化及约束描述如下公式:
最小时间公式:
Figure PCTCN2020112720-appb-000006
公有云截止时间约束条件:
Figure PCTCN2020112720-appb-000007
私有云截止时间约束条件:
Figure PCTCN2020112720-appb-000008
费用时间约束条件:
F i=TC i×P u+TL i×Q u≤TM i           (14)。
前述的一种轨交应用的考虑时间和费用的云数据资源调度方法,其特征是:所述公式(3)中任务大小为任务代码量。
前述的一种轨交应用的考虑时间和费用的云数据资源调度方法,其特征是:所述费用的产生在公有云的租用上,费用包括计算费用、存储费用。
前述的一种轨交应用的考虑时间和费用的云数据资源调度方法,其特征是:所述Tλ i若为1,为考虑费用优先调度策略,若为Tλ i为2,为考虑截止时间优先调度策略,若Tλ i为0,为自动判断调度策略。
前述的一种轨交应用的考虑时间和费用的云数据资源调度方法,其特征是:所述步骤5)中私有云优先的原则为:若公有云和私有云的完成时间相同或相似,任务将优先调度至私有云,否则调度至公有云。
前述的一种轨交应用的考虑时间和费用的云数据资源调度方法,其特征是:云计算数据资源调度模型包括用户接口、任务请求管理器和调度管理器;用户接口用于将用户提交的任务请求送至请求管理器,任务请求管理器用于将任务请求信息传输至调度管理器,调度管理器根据任务需求,结合保存在调度管理器中的公有云和私有云的资源信息,运用云调度方法将任务调度至公有云或私有云上,完成任务处理后将结果返回给用户。
前述的一种轨交应用的考虑时间和费用的云数据资源调度方法,其特征是:所述任务请求信息包括任务大小、所需数据量、要求完成的截止时间及预算费 用。
前述的一种轨交应用的考虑时间和费用的云数据资源调度方法,其特征是:所述公有云和私有云资源的信息包括资源的计算能力、传输能力、计算价格。
本发明所达到的有益效果:本发明考虑时间和费用约束提出了两个调度策略—截止时间优先和费用优先策略,建立了云中的任务和资源模型,能够根据用户提交的任务需求和人为自定义的优先策略或自适应选择合适的调度资源,对截止时间要求比较高的任务可以优先调度至公有云,对费用要求高的任务可以优先调度至私有云,而且两种策略均满足截止时间和一定的费用约束;本发明计算简单,不涉及复杂算法。
附图说明
图1是云调度模型示意图。
具体实施方式
下面结合附图对本发明作进一步描述。以下实施例仅用于更加清楚地说明本发明的技术方案,而不能以此来限制本发明的保护范围。
如图1所示,一种轨交应用的考虑时间和费用的云数据资源调度模型,包括用户接口、任务请求管理器和调度管理器;用户接口用于将用户提交的任务请求送至任务请求管理器,任务请求管理器用于将任务请求信息传输至调度管理器,调度管理器根据任务需求,结合保存在调度管理器中的公有云和私有云的资源信息,运用本发明的大数据云计算数据资源调度方法将任务调度至公有云或私有云上,完成任务处理后将结果返回给用户。
任务请求信息包括任务权重、任务大小、所需数据量、要求完成的截止时间及预算费用等;公有云和私有云资源的信息,包括两种资源的计算能力、传输能力、计算价格等。
一种基于上述大数据云计算数据资源调度模型的考虑时间和费用的调度方法,
1)资源描述:
云中的资源分为公有云资源和私有云资源,本发明所述的资源主要代表虚拟机。
定义公有云资源:R u为:
R u=<C u,L u,P u,S u>         (1)
其中,C u为公有云资源计算能力,影响任务的计算时间;L u为传输能力,影响任务的传输时间;P u为计算价格,S u为存储价格;
定义私有云资源R r
R r=<C r,L r>         (2)
其中,C r为私有云资源计算能力,影响任务的计算时间;L r为传输能力,影响任务传输时间。
2)任务描述:
设任务T i为任务请求的一个单位,每个资源一次处理一个任务,则任务T i定义为:
Ti=<Tλ i,TD i,TC i,TL i,TM i>        (3)
其中,Tλ i为费用或时间的优先级权重值,若为1,则认为考虑费用优先调度策略,若为2为认为考虑截止时间优先调度策略,若为0,认为自动判断调度策略。这样设定可以人为设定费用或时间优先调度,TD i为任务的截止时间;TC i为任务大小;TL i为任务计算所需信息量;TM i为任务的预算花费。
3)调度约束描述:
由任务和云资源的定义可计算任务T i分别在公有云和私有云中的完成时间t iR u、t iR r如下:
Figure PCTCN2020112720-appb-000009
Figure PCTCN2020112720-appb-000010
截止时间约束条件为t iR u≤TD i和t iR r≤TD i
本发明假设费用产生主要在公有云的上,主要由计算费用、存储费用两部分组成,忽略传输费用,任务T i在公有云上的花费F i计算公式如下:
F i=TC i×P u+TL i×Q u      (6)
费用约束条件为F i≤TM i
根据不同应用对性能和费用的需求差异,提出了两种调度策略:一种是以费用优先的调度策略(简称为Cost-First),一种是以截止时间优先的调度策略(简称为Time-First)。
4)费用优先调度策略:
当Tλ i为1时,即人为设定费用优先调度策略,或者当Tλ i为0时,自适应判断优先策略,即在满足截止时间约束条件下优先将任务调度至私有云,减少费用。首先判断任务T i在私有云的完成时间约束,若t iR r≤TD i,则调度至私有云,否则调度至公有云,调度至公有云时,也要满足费用约束条件F i≤TM i;n个任务的花费问题转化为以下优化问题,相关优化及约束描述如下:
最小费用公式为:
Figure PCTCN2020112720-appb-000011
公有云截止时间约束条件:
Figure PCTCN2020112720-appb-000012
私有云截止时间约束条件:
Figure PCTCN2020112720-appb-000013
费用时间约束条件:
F i=TC i×P u+TL i×Q u≤TM i       (10)
5)截止时间优先调度策略:
当Tλ i为2时,即截止时间优先时,或当Tλ i为0时,自适应判断优先策略时,即在满足费用约束条件下优先将任务调度至完成该任务较快的资源上。首先判断任务在公有和私有云资源的完成时间,将任务调度至能尽早完成的资源上,在同等条件下,兼顾私有云优先的原则。也就是说如果在公有云和私有云的完成时间相同或相似,任务将优先调度至私有云,否则调度至公有云。调度至公有云时,首先需要判断费用约束条件,相关优化及约束描述如下公式:
最小时间公式:
Figure PCTCN2020112720-appb-000014
公有云截止时间约束条件:
Figure PCTCN2020112720-appb-000015
私有云截止时间约束条件:
Figure PCTCN2020112720-appb-000016
费用时间约束条件:
F i=TC i×P u+TL i×Q u≤TM i       (14)
利用云调度模型,用户通过用户接口将任务请求送至请求管理器,请求管理器将任务请求信息传输至调度管理器,调度管理器根据任务需求,结合保存在调度管理器中的公有云和私有云的资源信息,运用本发明的云调度方法将任务 调度至公有云或私有云完成,并将结果返回给用户。
本发明调度方法简单实用,只需要对任务进行条件判断,不涉及积分微分操作,算法复杂度低。
以上所述仅是本发明的优选实施方式,应当指出,对于本技术领域的普通技术人员来说,在不脱离本发明技术原理的前提下,还可以做出若干改进和变形,这些改进和变形也应视为本发明的保护范围。

Claims (8)

  1. 一种轨交应用的考虑时间和费用的云数据资源调度方法,包括步骤:
    1)资源描述:
    定义公有云资源:R u为:
    R u=<C u,L u,P u,S u>    (1)
    其中,C u为公有云资源计算能力;L u为传输能力;P u为计算价格,S u为存储价格;
    定义私有云资源R r
    R r=<C r,L r>    (2)
    其中,C r为私有云资源计算能力;L r为传输能力;
    2)任务描述:
    任务T i定义为:
    Ti=<Tλ i,TD i,TC i,TL i,TM i>    (3)
    其中,Tλ i为费用或时间的优先级权重值,TD i为任务的截止时间;TC i为任务大小;TL i为任务计算所需信息量;TM i为任务的预算花费;
    3)调度约束描述:
    由任务和云资源的定义可计算任务T i分别在公有云和私有云中的完成时间t iR u、t iR r如下:
    Figure PCTCN2020112720-appb-100001
    Figure PCTCN2020112720-appb-100002
    截止时间约束条件为t iR u≤TD i和t iR r≤TD i
    任务T i在公有云上的花费F i计算公式如下:
    F i=TC i×P u+TL i×Q u    (6)
    费用约束条件为F i≤TM i
    4)费用优先调度策略:
    当Tλ i为1时,即人为设定费用优先调度策略,或者当Tλ i为0时,自适应判断优先策略,即在满足截止时间约束条件下优先将任务调度至私有云,减少费用,首先判断任务T i在私有云的完成时间约束,若t iR r≤TD i,则调度至私有云,否则调度至公有云,调度至公有云时,也要满足费用约束条件F i≤TM i;n个任务的花费问题转化为以下优化问题,相关优化及约束描述如下:
    最小费用公式为:
    Figure PCTCN2020112720-appb-100003
    公有云截止时间约束条件:
    Figure PCTCN2020112720-appb-100004
    私有云截止时间约束条件:
    Figure PCTCN2020112720-appb-100005
    费用时间约束条件:
    F i=TC i×P u+TL i×Q u≤TM i    (10)
    5)截止时间优先调度策略:
    当Tλ i为2时,即截止时间优先时,或当Tλ i为0时,自适应判断优先策略时,即在满足费用约束条件下优先将任务调度至完成该任务较快的资源上,首先判 断任务在公有和私有云资源的完成时间,将任务调度至能尽早完成的资源上,在同等条件下,兼顾私有云优先的原则,调度至公有云时,首先需要判断费用约束条件,相关优化及约束描述如下公式:
    最小时间公式:
    Figure PCTCN2020112720-appb-100006
    公有云截止时间约束条件:
    Figure PCTCN2020112720-appb-100007
    私有云截止时间约束条件:
    Figure PCTCN2020112720-appb-100008
    费用时间约束条件:
    F i=TC i×P u+TL i×Q u≤TM i    (14)。
  2. 根据权利要求1所述的一种轨交应用的考虑时间和费用的云数据资源调度方法,其特征是:所述公式(3)中任务大小为任务代码量。
  3. 根据权利要求1一种轨交应用的考虑时间和费用的云数据资源调度方法,其特征是:所述费用的产生在公有云的租用上,费用包括计算费用、存储费用。
  4. 根据权利要求1所述的一种轨交应用的考虑时间和费用的云数据资源调度方法,其特征是:所述Tλ i若为1,为考虑费用优先调度策略,若为Tλ i为2,为考虑截止时间优先调度策略,若Tλ i为0,为自动判断调度策略。
  5. 根据权利要求1所述的一种轨交应用的考虑时间和费用的云数据资源调度方法,其特征是:所述步骤5)中私有云优先的原则为:若公有云和私有云的完成时间相同或相似,任务将优先调度至私有云,否则调度至公有云。
  6. 根据权利要求1所述的一种轨交应用的考虑时间和费用的云数据资源调度方法,其特征是:云计算数据资源调度模型包括用户接口、任务请求管理器和调度管理器;用户接口用于将用户提交的任务请求送至请求管理器,任务请求管理器用于将任务请求信息传输至调度管理器,调度管理器根据任务需求,结合保存在调度管理器中的公有云和私有云的资源信息,运用云调度方法将任务调度至公有云或私有云上,完成任务处理后将结果返回给用户。
  7. 根据权利要求1所述的一种轨交应用的考虑时间和费用的云数据资源调度方法,其特征是:所述任务请求信息包括任务大小、所需数据量、要求完成的截止时间及预算费用。
  8. 根据权利要求1所述的一种轨交应用的考虑时间和费用的云数据资源调度方法,其特征是:所述公有云和私有云资源的信息包括资源的计算能力、传输能力、计算价格。
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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113961323A (zh) * 2021-10-20 2022-01-21 郑州轻工业大学 一种面向混合云的安全感知任务调度方法和系统

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20040230675A1 (en) * 2003-05-15 2004-11-18 International Business Machines Corporation System and method for adaptive admission control and resource management for service time guarantees
CN104657220A (zh) * 2015-03-12 2015-05-27 广东石油化工学院 混合云中基于截止时间和费用约束的调度模型及方法
CN107797870A (zh) * 2017-11-10 2018-03-13 苏州铭冠软件科技有限公司 一种云计算数据资源调度方法
CN107908458A (zh) * 2017-11-10 2018-04-13 苏州铭冠软件科技有限公司 一种考虑时间和费用的云计算数据资源调度方法
CN109710392A (zh) * 2018-12-21 2019-05-03 万达信息股份有限公司 一种基于混合云的异构资源调度方法

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20040230675A1 (en) * 2003-05-15 2004-11-18 International Business Machines Corporation System and method for adaptive admission control and resource management for service time guarantees
CN104657220A (zh) * 2015-03-12 2015-05-27 广东石油化工学院 混合云中基于截止时间和费用约束的调度模型及方法
CN107797870A (zh) * 2017-11-10 2018-03-13 苏州铭冠软件科技有限公司 一种云计算数据资源调度方法
CN107908458A (zh) * 2017-11-10 2018-04-13 苏州铭冠软件科技有限公司 一种考虑时间和费用的云计算数据资源调度方法
CN109710392A (zh) * 2018-12-21 2019-05-03 万达信息股份有限公司 一种基于混合云的异构资源调度方法

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113961323A (zh) * 2021-10-20 2022-01-21 郑州轻工业大学 一种面向混合云的安全感知任务调度方法和系统
CN113961323B (zh) * 2021-10-20 2022-06-14 郑州轻工业大学 一种面向混合云的安全感知任务调度方法和系统

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