CN115460087B - Method and device for deploying business processes in cloud computing environment - Google Patents
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
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- H—ELECTRICITY
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- H04L67/10—Protocols in which an application is distributed across nodes in the network
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Abstract
The disclosure provides a method and a device for deploying business processes in a cloud computing environment, and belongs to the technical field of communication. The method comprises the following steps: initializing and generating N strategies which are deployed on a plurality of cloud resources and meet physical constraint conditions of K tasks; the following A1 to A4 are repeatedly performed W times: a1: dividing N strategies meeting physical constraint conditions into a first group and a second group based on fitness; a2: preprocessing cloud resources in each strategy of the first group; a3: if Q strategies do not meet the physical constraint condition, regenerating the Q strategies until Q strategies meeting the physical constraint condition are obtained; a4: determining the adaptability of each strategy in the strategies meeting the physical constraint conditions; and determining the strategy with the smallest N fitness values calculated at the W time as a deployment strategy of K tasks. Based on the technical scheme provided by the embodiment of the disclosure, proper cloud resources and service strategies can be selected for the service.
Description
Technical Field
The disclosure belongs to the technical field of communication, and particularly relates to a method and a device for deploying business processes in a cloud computing environment.
Background
With the development of virtualization technology and distributed computing technology, cloud computing has become a strategic focus of information industry development, which can support cost-effective computing resource usage.
In general, a business process is composed of a plurality of tasks, which are required to be connected according to a certain logic relationship and a certain precedence relationship, and a certain time sequence constraint is attached to each connection edge. The delivery of user business process requirements has time constraints that need to be performed in logical order on time to avoid as much as possible degradation of user quality of experience due to business violations.
However, how to select a suitable cloud resource and an optimal service policy for each task according to the service flow sequence, and simultaneously meet the timing constraint in the service flow process is a technical problem that needs to be solved at present.
Disclosure of Invention
The embodiment of the disclosure aims to provide a method and a device for deploying a business process in a cloud computing environment, which can solve the problem of selecting proper cloud resources and service strategies for businesses.
In order to solve the above technical problems, the present disclosure is implemented as follows:
in a first aspect, an embodiment of the present disclosure provides a method for service flow deployment in a cloud computing environment, where the method includes: initializing and generating N strategies which are used for deploying K tasks on a plurality of cloud resources and meet physical constraint conditions, wherein the K tasks are tasks in at least one business process, the physical constraint conditions comprise constraint conditions of storage capacity and computing capacity, and K and N are integers larger than 1; the following A1 to A4 are repeatedly performed W times: a1: dividing N strategies meeting physical constraint conditions into a first group and a second group based on fitness, wherein the fitness of the strategies of the first group is larger than that of the strategies of the second group, and the fitness indicates the deployment cost and the degree of default cost of the strategies; a2: preprocessing cloud resources of each strategy of the first group to obtain preprocessed strategies, and determining whether the preprocessed strategies meet physical constraint conditions; a3: if Q strategies do not meet the physical constraint conditions, regenerating the Q strategies, and determining whether the regenerated strategies meet the physical constraint conditions or not until Q strategies meeting the physical constraint conditions are obtained, wherein Q is a positive integer; a4: determining a second group of strategies, the Q strategies meeting the physical constraint conditions, and the adaptability of each strategy in the strategies meeting the physical constraint conditions in the preprocessed strategies; and determining the strategy with the minimum N fitness values calculated at the W time as the deployment strategy of the K tasks, wherein W is an integer larger than 1.
In a second aspect, an embodiment of the present disclosure provides an apparatus for service flow deployment, where the apparatus includes: the device comprises a generation module, an execution module and a determination module; the generating module is used for initializing and generating N strategies meeting physical constraint conditions for deploying K tasks on a plurality of cloud resources, wherein the K tasks are tasks in at least one business process, the physical constraint conditions comprise constraint conditions of storage capacity and computing capacity, and K and N are integers larger than 1; an execution module for repeatedly executing W times A1 to A4 described below: a1: dividing N strategies meeting physical constraint conditions into a first group and a second group based on fitness, wherein the fitness of the strategies of the first group is larger than that of the strategies of the second group, and the fitness indicates the deployment cost and the degree of default cost of the strategies; a2: preprocessing cloud resources of each strategy of the first group to obtain preprocessed strategies, and determining whether the preprocessed strategies meet physical constraint conditions or not; a3: if Q strategies do not meet the physical constraint conditions, regenerating the Q strategies, and determining whether the regenerated strategies meet the physical constraint conditions or not until Q strategies meeting the physical constraint conditions are obtained, wherein Q is a positive integer; a4: determining a second group of strategies, Q strategies meeting physical constraint conditions, and the adaptability of each strategy in the strategies meeting the physical constraint conditions in the preprocessed strategies; the determining module is further used for determining a strategy with the smallest N fitness values calculated by the executing module at the W time as a deployment strategy of the K tasks; w is an integer greater than 1.
In a third aspect, embodiments of the present disclosure provide a server including a processor, a memory, and a program or instruction stored on the memory and executable on the processor, the program or instruction, when executed by the processor, implementing the steps of a method for business process deployment in a cloud computing environment according to the first aspect.
In a fourth aspect, embodiments of the present disclosure provide a readable storage medium having stored thereon a program or instructions which, when executed by a processor, implement the steps of a method for business process deployment in a cloud computing environment according to the first aspect.
In a fifth aspect, an embodiment of the present disclosure provides a chip, where the chip includes a processor and a communication interface, where the communication interface is coupled to the processor, and the processor is configured to execute a program or instructions to implement a method for service flow deployment in a cloud computing environment according to the first aspect.
In a sixth aspect, embodiments of the present disclosure provide a computer program product containing instructions that, when run on a computer, cause the computer to perform the steps of the method of business process deployment in a cloud computing environment as described in the first aspect.
In the embodiment of the present disclosure, first, N policies that satisfy physical constraints, including constraints of storage capacity and computing capacity, deployed on a plurality of cloud resources may be generated for K tasks in at least one business process, and then, the following steps are repeatedly performed for W times: dividing the N strategies into 2 groups according to the fitness, preprocessing a group of strategies with large fitness values, judging whether the new strategies obtained after preprocessing meet physical constraint conditions, continuously regenerating the strategies which are not met until the strategies in the group meet the physical constraint conditions, and preprocessing again based on the fitness groups. And finally, after the W-th processing, determining the strategy with the smallest fitness value in the N strategies as a deployment strategy of each task in the business process. Based on the above manner, the deployment strategy with low deployment cost meeting the task requirements can be comprehensively determined according to the time sequence requirements of each task, the computing capacity, the storage capacity and the deployment cost of cloud resources and the default cost in the business process.
Drawings
Fig. 1 is a schematic deployment diagram of a business process on a cloud resource in a cloud computing environment according to an embodiment of the present disclosure;
FIG. 2 is one of flow diagrams of a method for business flow deployment in a cloud computing environment provided by an embodiment of the present disclosure;
FIG. 3 is a second flow diagram of a method for business flow deployment in a cloud computing environment according to an embodiment of the present disclosure;
FIG. 4 is a third flow diagram of a method for business flow deployment in a cloud computing environment according to an embodiment of the present disclosure;
FIG. 5 is a schematic diagram of a possible architecture of a device for business process deployment according to an embodiment of the disclosure;
FIG. 6 is a second schematic diagram of a possible architecture of a device for business process deployment according to an embodiment of the disclosure;
fig. 7 is a schematic device hardware diagram of a service flow deployment according to an embodiment of the present disclosure.
Detailed Description
For ease of understanding, related terms referred to in the embodiments of the present disclosure are explained first:
in a cloud computing environment, different cloud service resources (denoted as cloud resources) can be included, users in a network can propose functional requirements, and different cloud service resources are selected for renting so as to meet the demands of network users. In general, in the method for deploying a service flow in a cloud computing environment provided by the embodiment of the present disclosure, a user demand is split into a series of task sets having a logical relationship and a time sequence constraint, each task rents different cloud service resources respectively, and a deployment policy that minimizes service cost is found according to different pricing policies of the cloud service resources under the condition that the time sequence constraint of the service flow is satisfied as much as possible.
1. Business process
A service flow bp is represented by a binary group, wherein bp= { Tsk, edg }, tsk represents a task set contained in the service flow, and Tsk= { Tsk 1 ,tsk 2 …, edg represents the sequential edges between different tasks in the business process,
2. tasks
The task tsk is represented by a six-tuple, where tsk= { nm, dsc, χ, β, dur, pen }, nm represents the name of tsk, dsc represents the textual description of the task, χ represents the amount of computation required by the task, β represents the storage space required by the task, dur represents the duration of executing the task, and pen represents the offending cost of the task due to exceeding the timing constraints.
3. Cloud resources
And (3) representing the cloud resource CR by a quadruple, wherein CR= { pvc, cmp, str, pcs }, wherein cmp represents the computing power of a server corresponding to the cloud resource, str represents the memory space of the cloud resource, and pcs represents the pricing strategy of the cloud resource.
In general, cloud service pricing policies can be divided into two modes:
(1) Prepaid policy m 1
The cloud servers with different configurations can be selected, and cloud resources can be used at will within a certain time in a mode of paying a certain fee in advance.
(2) Per-minute billing policy m 2
And in a buying and using mode, paying for the cloud resources according to unit price and using time.
The following description of the technical solutions in the embodiments of the present disclosure will be made clearly and completely with reference to the accompanying drawings in the embodiments of the present disclosure, and it is apparent that the described embodiments are some embodiments of the present disclosure, but not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art without inventive effort, based on the embodiments in this disclosure are intended to be within the scope of this disclosure.
The terms first, second and the like in the description and in the claims, are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged, where appropriate, such that embodiments of the disclosure may be practiced in sequences other than those illustrated and described herein, and that the objects identified by "first," "second," etc. are generally of the same type and are not limited to the number of objects, e.g., the first object may be one or more. Furthermore, in the description and claims, "and/or" means at least one of the connected objects, and the character "/", generally means that the associated object is an "or" relationship.
It is noted that the techniques described in embodiments of the present disclosure are not limited to LTE (Long Term Evolution )/LTE-a (LTE-Advanced, evolution of LTE) systems, but may also be used in other wireless communication systems, such as CDMA (Code Division Multiple Access ), TDMA (Time Division Multiple Access, time division multiple access), FDMA (Frequency Division Multiple Access ), OFDMA (Orthogonal Frequency Division Multiple Access, orthogonal frequency division multiple access), SC-FDMA (Single-carrier Frequency-Division Multiple Access, single carrier frequency division multiple access), and other systems. The terms "system" and "network" in embodiments of the present application are often used interchangeably, and the techniques described may be used for both the above-mentioned systems and radio technologies, as well as other systems and radio technologies. However, the following description describes an NR system for purposes of example and NR terminology is used in much of the following description, although the techniques may also be applied to applications other than NR system applications, such as 6G (6 th Generation) communication systems.
The method for deploying the business processes in the cloud computing environment provided by the embodiment of the disclosure is described in detail below through specific embodiments and application scenarios thereof with reference to the accompanying drawings.
Fig. 1 is a schematic deployment diagram of a business process on a cloud resource in a cloud computing environment according to an embodiment of the present disclosure. As shown in fig. 1, including i cloud resources, i being an integer greater than 1, each cloud resource includes: cloud provider, computing power, storage space, and pricing policies, as shown in fig. 1, respectively. Taking the deployment of 2 business processes as an example, wherein the business processes bp 1 The method comprises 4 tasks, namely: tsk 1-1 、tsk 1-2 、tsk 1-3 And tsk 1-4 . Business process bp 2 The method comprises 3 tasks, namely: tsk 2-1 、tsk 2-2 And tsk 2-3 。
Wherein tsk is performed 1-1 The duration interval of (2) is [3,4 ]]Executing tsk 1-2 The duration interval of (1, 3)]Executing tsk 1-3 The duration interval of (2, 4)]Executing tsk 1-4 The duration interval of (2) is [3,5 ]]Executing tsk 2-1 The duration interval of (2) is [1,2 ]]Executing tsk 2-2 The duration interval of (2, 4)]Executing tsk 2-3 The duration interval of (2) is [4,6 ]]。
Wherein tsk is 1-1 And tsk 1-2 Deployment at CR 1 Upper execution, tsk 1-3 、tsk 1-4 And tsk 2-3 Deployment at CR 2 Upper execution, tsk 2-1 And tsk 2-2 Deployment at CR i The logical order of execution of the tasks is indicated by the arrows between the tasks in fig. 1.
Fig. 2 is a flow chart of a method for service flow deployment in a cloud computing environment according to an embodiment of the present disclosure, as shown in fig. 2, the method includes the following steps S201 to S203:
S201, initializing and generating N strategies which are deployed on a plurality of cloud resources and meet physical constraint conditions of K tasks.
The K tasks are tasks in at least one business process, the physical constraint conditions comprise constraint conditions of energy storage capacity and computing capacity, and K and N are positive integers greater than 1.
Alternatively, as shown in fig. 3 in conjunction with fig. 2, S201 described above may be specifically performed by S21 and S22 described below:
s21, generating N strategies for deploying K tasks on a plurality of cloud resources, and determining whether each strategy meets physical constraint conditions.
Illustratively, at initialization, N policies may be randomly generated.
It should be noted that if any one of the storage capability and the computing capability in one policy does not satisfy the corresponding constraint condition, the policy does not satisfy the physical constraint condition, and if the storage capability and the computing capability in one policy both satisfy the corresponding constraint condition, the policy satisfies the physical constraint condition.
For example, to facilitate understanding the technical solution provided by the embodiments of the present disclosure, table 1 is an exemplary table of policies for task deployment of a service flow provided by the embodiments of the present disclosure, and assuming that 3 cloud resources may serve 7 tasks of service flow 1 and service flow 2, where 20 deployed policies are randomly generated, it may be determined whether each of the 20 policies satisfies a physical constraint condition.
For the sake of explanation, policy 1 is "1323113", and policy 1 indicates that task 1 is disposed on cloud resource 1, task 2 is disposed on cloud resource 3, task 3 is disposed on cloud resource 2, task 4 is disposed on cloud resource 3, task 5 is disposed on cloud resource 1, task 6 is disposed on cloud resource 1, and task 7 is disposed on task 3.
TABLE 1
| Task 1 | Task 2 | Task 3 | Task 4 | Task 5 | Task 6 | Task 7 | |
| Strategy 1 | 1 | 3 | 2 | 3 | 1 | 1 | 3 |
| Policy 2 | 2 | 3 | 1 | 2 | 3 | 2 | 1 |
| Strategy 3 | 1 | 2 | 3 | 1 | 2 | 3 | 2 |
| ... | ... | ... | ... | ... | ... | ... | ... |
| Strategy 20 | 3 | 2 | 1 | 2 | 3 | 2 | 3 |
S22, if the P strategies do not meet the physical constraint conditions, regenerating the P strategies, and determining whether the regenerated strategies meet the physical constraint conditions or not until N strategies meeting the physical constraint conditions are obtained.
Wherein P is a positive integer less than or equal to N.
Specifically, if there are P policies that do not satisfy the physical constraint condition in the N policies, deleting the P policies, regenerating the P policies, determining whether the regenerated P policies satisfy the physical constraint condition, if yes, executing the following S202, and if there are policies that do not satisfy the physical constraint condition in the regenerated P policies, continuing deleting and generating new policies until N policies that satisfy the physical constraint condition are obtained, and executing the following S202.
For example, assuming that 5 policies out of the 20 policies do not satisfy the physical constraint condition, the 5 policies may be deleted, and 5 policies may be regenerated, and then it is determined whether the 5 policies satisfy the physical constraint condition, if all the 5 policies satisfy the physical constraint condition, the following S202 is continuously executed, and if at least one policy out of the regenerated 5 policies does not satisfy the physical constraint condition, the unsatisfied policies are continuously deleted, and the physical constraint condition is regenerated and continuously determined whether the physical constraint condition is satisfied.
S202, repeatedly performing W times A1 to A4 described below:
a1: n policies satisfying physical constraints are divided into a first group and a second group based on fitness.
Wherein the fitness of the policies of the first group is greater than the fitness of the policies of the second group.
In the embodiment of the disclosure, the fitness indicates the deployment cost and the degree of the default cost of the policy. The smaller the adaptability is, the lower the cost for indicating policy deployment is, and the better the policy is; the greater the fitness, the higher the cost of indicating policy deployment, the worse the policy.
Illustratively, the first set of policies may be N/2 policies of N policies with fitness from small to large N/2 after ranking, and the second set of policies may be N/2 policies of N policies with fitness from small to large N/2 before ranking. Alternatively, the first set of policies may be policies having fitness greater than or equal to a preset fitness among the N policies, and the second set of policies may be policies having fitness less than the preset fitness among the N policies.
Continuing with the example above, after the process of S202, 20 policies satisfying the physical constraint may be obtained, and then the fitness of each policy may be calculated, thereby obtaining fitness of 20 policies. Taking the first grouping method as an example for explanation, 10 first policies with relatively large adaptability and 10 second policies with relatively small adaptability can be obtained.
A2: and preprocessing cloud resources of each strategy of the first group to obtain preprocessed strategies, and determining whether the preprocessed strategies meet physical constraint conditions.
Continuing to combine the above examples, preprocessing the cloud resources corresponding to the tasks in the first group of 10 policies with relatively large adaptability, so as to obtain new 10 policies, and then calculating whether the 10 new policies meet the physical constraint condition.
If all the policies after the pretreatment meet the physical constraint condition, the fitness of the policies after the pretreatment is directly determined, and N policies are grouped again based on the fitness of the policies in the second group and the fitness of the policies after the pretreatment, and the next treatment is executed.
A3: if Q strategies do not meet the physical constraint condition, regenerating the Q strategies, and determining whether the regenerated strategies meet the physical constraint condition or not until Q strategies meeting the physical constraint condition are obtained.
Wherein Q is a positive integer.
A4: determining the second group of strategies, the Q strategies meeting the physical constraint conditions, and the fitness of each strategy in the strategies meeting the physical constraint conditions in the preprocessed strategies.
Note that, in A4, the fitness of N policies is obtained in total.
The fitness of the policy of the second group determined in A4 may be a fitness calculated when grouping is performed in A1 each time A1 to A4 are performed. I.e. the fitness to be calculated in A4 is the fitness of the policies after A2 and A3 processing for the policies of the first group.
S203, determining a strategy with the minimum N fitness values determined at the W time as a deployment strategy of K tasks.
Wherein W is a positive integer.
It will be appreciated that a minimum fitness value may indicate that the overall cost of deployment of the policy is minimal.
The embodiment of the disclosure provides a method for deploying service flows in a cloud computing environment, firstly, N policies meeting physical constraint conditions, including constraint conditions of storage capacity and computing capacity, deployed on a plurality of cloud resources can be generated for K tasks in at least one service flow in an initialized mode; then, the following steps are repeatedly performed W times: dividing the N strategies into 2 groups according to the fitness, preprocessing a group of strategies with large fitness values, judging whether the new strategies obtained after preprocessing meet physical constraint conditions, continuously regenerating the strategies which are not met until the strategies in the group meet the physical constraint conditions, and preprocessing again based on the fitness groups. And finally, after the W-th processing, determining the strategy with the smallest fitness value in the N strategies as a deployment strategy of each task in the business process. Based on the above manner, the deployment strategy with low deployment cost meeting the task requirements can be comprehensively determined according to the time sequence requirements of each task, the computing capacity, the storage capacity and the deployment cost of cloud resources and the default cost in the business process.
Optionally, in the method for service flow deployment in a cloud computing environment provided by the embodiment of the present disclosure, the determining whether each policy meets the physical constraint condition in S21 may be performed by the following S11:
s11, determining whether the storage capacity and the computing capacity of the cloud resources in each policy meet corresponding constraint conditions.
In combination with policy 1 in table 1, it is necessary to calculate whether the storage capacity and the calculation capacity of each cloud resource when executing the corresponding task satisfy the corresponding constraint condition. For example, whether the storage capacity and the computing capacity of the computing cloud resource 1 when executing the task 1, the task 5, and the task 6 satisfy the corresponding constraint conditions, whether the storage capacity and the computing capacity of the cloud resource 2 when executing the task 3 satisfy the corresponding constraint conditions, and whether the storage capacity and the computing capacity of the cloud resource 3 when executing the task 2, the task 4, and the task 7 satisfy the corresponding constraint conditions are required.
Based on the scheme, after the policies of each task deployed on the cloud resource are generated, whether the storage capacity of the cloud resource related in each policy meets the requirements of the task deployed on the resource and whether the computing capacity meets the requirements of the task deployed on the resource can be judged, so that the policies which do not meet the physical constraint condition in the randomly generated policies can be eliminated.
Optionally, in the method for deploying a business process in a cloud computing environment provided by the embodiment of the present disclosure, when determining whether the storage capability of a cloud resource in each policy meets a corresponding constraint condition, S11 may include the following S11a:
s11a, determining whether the storage capacity of the cloud resources in each strategy meets the corresponding constraint condition or not based on the storage space required by the task, the storage space of the cloud resources and the decision situation of the task deployment on the cloud resources.
By way of example, it may be determined whether storage capabilities of the cloud resources under policy satisfy storage constraints based on the following equation (1).
Wherein X is ij Representing task tsk i CR (computed tomography) deployed on cloud resources j Is the decision case of X ij =1 represents task tsk i CR (computed tomography) deployed on cloud resources j On X ij =0 denotes task tsk i CR not deployed in cloud resources j On CR (CR) j Str represents cloud resource CR j Is provided.
It can be understood that if the storage capability of the cloud resource in one policy satisfies the above formula (1), the storage capability of the cloud resource in the policy satisfies the corresponding constraint condition, and if the storage capability of the cloud resource in one policy does not satisfy the above formula (1), the policy does not satisfy the physical constraint condition.
Based on the scheme, whether the storage capacity of each cloud resource involved in the strategy meets the requirement of the task can be determined based on the storage space required by each task in the business process, the deployment condition of each task on the cloud resource in one strategy and the storage space of each cloud resource, so that whether the strategy is a strategy meeting the storage requirement can be determined, and the follow-up screening can be conveniently carried out.
Optionally, in the method for deploying a business process in a cloud computing environment provided by the embodiment of the present disclosure, when determining whether the storage capability of a cloud resource in each policy meets a corresponding constraint condition, S11 described above may include S11b as follows:
s11b, determining whether the computing capacity of the cloud resource in each strategy meets the corresponding constraint condition based on the computing capacity required by the task, the computing capacity of the cloud resource and the decision situation of the task deployment on the cloud resource.
For example, it may be determined whether the storage capacity of the cloud resource under each policy satisfies the constraint condition of storage based on the following formula (1).
Wherein CR is j Cmp represents cloud resource CR j Is added to the computing power of (a).
It can be understood that if the computing power of the cloud resource in one policy satisfies the above formula (2), the computing power of the cloud resource in the policy satisfies the corresponding constraint condition, and if the computing power of the cloud resource in one policy does not satisfy the above formula (2), the policy does not satisfy the physical constraint condition.
Based on the scheme, whether the computing capacity of each cloud resource involved in the strategy meets the requirement of the task can be determined based on the computing capacity required by each task in the business process, the deployment condition of each task on the cloud resource in one strategy and the computing capacity of each cloud resource, so that whether the strategy is a strategy meeting the computing requirement can be determined, and the follow-up screening can be conveniently carried out.
Optionally, in the method for service flow deployment in a cloud computing environment provided by the embodiment of the present disclosure, the preprocessing for the cloud resource in the above A2 may be at least one of the following two manners:
pretreatment method 1: and exchanging cloud resources of at least one task in every two strategies of the first group to obtain exchanged strategies.
Illustratively, policy "1231232 "and policy" 321232Exchanging cloud resources of tasks 4 to 6 in 3 "to obtain 2 new policies 1232322 "and" 3211233”。
Pretreatment method 2: and randomly replacing cloud resources of at least one task in each strategy of the first group to obtain replaced strategies.
Illustratively, policy "123Task 3 cloud resources in 2322 "change randomly, e.g., change to a new policy" 12 12322”。
For example, if the preprocessing of the cloud resource includes the above 2 modes, the above preprocessing mode 1 may be executed first, then the above preprocessing mode 2 may be executed, or the above preprocessing mode 2 may be executed first, then the above preprocessing mode 1 may be executed, which is not particularly limited in the embodiment of the present disclosure.
It will be appreciated that in the embodiments of the present disclosure, the policy obtained after the preprocessing is actually a completely new deployment policy, and thus, it is necessary to continue to determine whether the physical constraint condition is satisfied for the new policy.
Based on the scheme, after N strategies are grouped based on the fitness, strategies with high deployment cost and high default cost indicated by the fitness can be preprocessed, so that a group of strategies is newly generated based on the group of strategies, the subsequent strategy screening is facilitated, and the strategy with optimal fitness is selected for task deployment.
Optionally, in the method for service flow deployment in a cloud computing environment provided by the embodiment of the present disclosure, before A1 described above, the method further includes the following steps A5 to A7:
a5: the deployment cost for each policy is calculated.
A6: the penalty cost for each policy is calculated.
A7: and determining the fitness of each strategy according to the deployment cost and the default cost.
For example, fitness of a policy = deployment cost + breach cost.
Based on the scheme, before executing the A1, the fitness of the N strategies meeting the physical constraint conditions generated by the S201 can be judged first to evaluate the deployment cost and the degree of the default cost of each strategy, so that the follow-up pretreatment of the strategy with higher deployment cost and default cost is facilitated, and the follow-up treatment is performed based on the strategy obtained after the pretreatment so as to obtain the strategy with optimal fitness.
Optionally, in the method for service flow deployment in a cloud computing environment provided by the embodiment of the present disclosure, the A5 may specifically be executed by the following a51:
a51: and calculating the deployment cost of each strategy based on the maximum execution time of the task, the price per unit time of the cloud resource pricing and the decision condition of the target pricing strategy in the cloud resource selected by the task.
Illustratively, the deployment cost for each policy may be determined based on equation (3) below.
Wherein Max (tsk) i Dur) represents a task tsk i Maximum execution time of c) ij Representing task tsk i Selected cloud resource CR j Price per unit time, X ijm Representing task tsk i Selecting cloud resource CR j The decision case at pricing strategy m may be a binary variable. For example, X ijm =1 represents task tsk i Selecting cloud resource CR j Pricing policies m, X of (2) ijm Task tsk shown by =0 i Unselected cloud resource CR j Is set, the pricing strategy m of (2).
Based on the scheme, the deployment cost of each strategy can be calculated based on the maximum execution time of each task demand, the pricing strategy of each cloud resource, the price per unit time of the cloud resource pricing and the decision condition of the target pricing strategy of task selection, so that the optimal deployment strategy can be comprehensively screened based on the deployment cost.
Optionally, in the method for service flow deployment in a cloud computing environment provided by the embodiment of the present disclosure, the above A6 may specifically be executed by the following a61:
a61: based on the violation cost caused by the violation of the time sequence constraint by the task in operation and the decision condition of whether the task is violated, calculating the violation cost of each strategy.
Illustratively, the violation costs for each policy may be determined based on equation (4) below.
Wherein pen i Representing due to task tsk i Violating cost caused by violating time sequence constraint in deployment operation process, Y i Representing task tsk i Decision-making of whether or not to violate Y i =1 indicates that the task tsk is not fully satisfied i Timing constraints of Y i =0 means that the task tsk is fully satisfied i No violations are caused by timing constraints of (2).
Further, the fitness of each of the above policies may be determined based on the following formula (5).
Fit=cost+pen formula (5)
Illustratively, in S204 described above, the smallest fitness among the fitness of the N policies may be determined based on the following equation (5).
Objt=Min (COST+PEN) equation (6)
Based on the scheme, the violation cost of each strategy can be determined according to the cost caused by the violation of time sequence constraint in the operation of the task to be deployed and whether each task deployment in the strategy generates the violation of time sequence on each cloud resource, so that whether each strategy to be deployed meets the time sequence requirement of the task can be accurately judged, and the follow-up accurate determination of the optimal deployment strategy meeting the requirement of each task based on the time sequence is facilitated.
Optionally, in the method for service flow deployment in a cloud computing environment provided by the embodiment of the present disclosure, in a61 described above, it may be determined whether the task violates the timing constraint based on the end time of the task, the start time of the task, and the target timing constraint corresponding to the task.
By way of example, it may be determined whether a task in the policy violates a timing constraint based on equation (6) below.
0≤F(tsk i )-S(tsk i )≤bp.stk i TC formula (7)
Wherein F (tsk) i ) Representing task tsk in policy i End time of S (tsk) i ) Representing task tsk in policy i Start time of (1), bp.stk i TC represents a task tsk in the business process bp i The corresponding timing constraints.
Based on the scheme, whether the actual execution of the task in the strategy violates the time sequence constraint of the task requirement or not can be determined according to the processing time of each task in each strategy and the time sequence constraint of the task requirement in the service flow, so that the deployment strategy meeting the task requirement can be determined based on the time sequence requirement and the estimated time sequence condition of deployment.
Examples:
fig. 4 is a schematic diagram of a business process optimization deployment flow in a cloud computing environment based on a genetic algorithm, and as shown in fig. 4, the above formula (6) may be used as an objective function of the genetic algorithm, that is, a business deployment policy with minimum fitness is found, cloud resource positions of each task deployment are recorded as a gene, all task deployment positions in the whole business process are used as a chromosome, the population size is set to 20, the objective function of the formula (6) is used as an adaptive function, and the business process in the cloud computing environment is optimally deployed.
S401, initializing a population, and assuming that cloud resources are randomly deployed for 7 tasks in 2 business processes respectively, the number examples of the chromosome corresponding to the deployed cloud resources are as follows: 1323113, 2312321, 1231232,.. 3212323 (20 chromosomes in total). And (3) respectively judging whether cloud resources selected in the strategies meet service physical constraint conditions according to formulas (1) and (2) aiming at each deployment strategy, wherein the cloud resources comprise storage constraint and calculation constraint, eliminating the unsatisfiable chromosomes, re-initializing to generate new chromosomes, and repeatedly judging whether the physical constraint conditions are met.
S402, for all individuals in the initial population, calculating deployment costs related to strategies in each chromosome according to a formula (3), judging whether deployment strategies corresponding to the chromosomes violate timing constraints of services according to a formula (4), and calculating the irreducible costs of the chromosomes. Generating an objective function of a formula (5) based on the formula (3) and the formula (4), taking the objective function as a population fitness function, calculating fitness function values of all individuals in an initial population, and substituting strategies expressed by different chromosomes into the fitness function to calculate so as to obtain the fitness function values.
Under the setting condition, the smaller the fitness function value is, the better the deployment strategy is.
S403, judging whether iteration is finished.
S404, performing selection operation, namely directly inheriting optimized individuals in the population into the next generation population, for example, the fitness function value corresponding to the chromosome 2312321 is smaller, which indicates that the deployment strategy is better, and the optimized individuals can be directly inherited into the next generation population; performing row crossing operation, namely sequencing other individuals except the optimized individuals in the population according to fitness, exchanging partial genes of adjacent individuals, and evolving the individuals in the population; and carrying out variation operation on the deployment strategy obtained by evolution, carrying out random change on partial gene values of individuals in the population, and carrying out evolution on the individuals in the population.
For example, the 4 th to 6 th genes in chromosomes 1231232 and 3212323 are swapped to give 1232322 and 3211233. And for each deployment strategy after exchange, judging whether the cloud resources after exchange meet service physical constraint conditions according to formulas (1) and (2), wherein the service physical constraint conditions comprise storage constraint and calculation constraint, eliminating the chromosomes which cannot be met, and re-initializing and generating.
For example, the 3 rd gene in chromosome 1232322 is mutated to 1212322. For each deployment strategy after mutation, judging whether the cloud resources after mutation operation meet the service physical constraint conditions according to formulas (1) and (2), wherein the service physical constraint conditions comprise storage constraint and calculation constraint, eliminating chromosomes which cannot be met, and re-initializing to generate.
S406, obtaining a next generation population after the first evolution, evaluating the population by using an fitness function, and continuing the operations from S402 to S405 until the maximum iteration times are met.
S407, obtaining an optimal deployment strategy of the business process in the cloud computing environment according to the individual deployment mode with the minimum fitness function value in the last generation population.
For example, if 1122323 is the chromosome with the smallest fitness function value, then it is described that: bp 1 Tsk of (2) 1 Deployment at CR 1 ，bp 1 Tsk of (2) 2 Deployment at CR 1 ，bp 1 Tsk of (2) 3 Deployment at CR 2 ，bp 1 Tsk of (2) 4 Deployment at CR 2 ，bp 2 Tsk of (2) 1 Deployment at CR 3 ，bp 2 Tsk of (2) 2 Deployment at CR 2 ，bp 2 Tsk of (2) 3 Deployment at CR 3 。
It should be noted that, in the method for service flow deployment in a cloud computing environment provided by the embodiment of the present disclosure, the execution body may also be a device for service flow deployment, or a control module in the device for service flow deployment for executing the method for service flow deployment in the cloud computing environment. In the embodiment of the present disclosure, a method for executing service flow deployment in a cloud computing environment by using a service flow deployment device is taken as an example, and the service flow deployment device provided in the embodiment of the present disclosure is described.
Fig. 5 is a schematic structural diagram of an apparatus for service flow deployment according to an embodiment of the present disclosure, where, as shown in fig. 5, an apparatus 500 for service flow deployment includes: a generation module 501, an execution module 502 and a determination module 503; a generating module 501, configured to generate N policies for deploying K tasks on a plurality of cloud resources, where the K tasks are tasks in at least one service flow, and the physical constraints include constraints of storage capability and computing capability, and K and N are integers greater than 1; the execution module 502 is configured to repeatedly execute W times A1 to A4 described below: a1: dividing N strategies meeting physical constraint conditions into a first group and a second group based on fitness, wherein the fitness of the strategies of the first group is larger than that of the strategies of the second group, and the fitness indicates the deployment cost and the degree of default cost of the strategies; a2: preprocessing cloud resources of each strategy of the first group to obtain preprocessed strategies, and determining whether the preprocessed strategies meet physical constraint conditions; a3: if Q strategies do not meet the physical constraint conditions, regenerating the Q strategies, and determining whether the regenerated strategies meet the physical constraint conditions or not until Q strategies meeting the physical constraint conditions are obtained, wherein Q is a positive integer; a4: determining a second group of strategies, Q strategies meeting physical constraint conditions, and the adaptability of each strategy in the strategies meeting the physical constraint conditions in the preprocessed strategies; a determining module 503, configured to determine, as the deployment policy of the K tasks, a policy with the smallest median of N fitness values calculated at the W-th time, where W is an integer greater than 1.
Optionally, the generating module is specifically configured to: generating N strategies for deploying K tasks on a plurality of cloud resources, and determining whether each strategy meets physical constraint conditions; if P strategies do not meet the physical constraint condition, regenerating the P strategies, and determining whether the regenerated strategies meet the physical constraint condition or not until N strategies meeting the physical constraint condition are obtained, wherein P is a positive integer less than or equal to N.
Optionally, the physical constraints include constraints of energy storage capacity and computing capacity; the determining module is specifically configured to: determining whether the storage capacity and the computing capacity of the cloud resources in each policy meet corresponding constraint conditions.
Optionally, the execution module is specifically configured to: based on the storage space required by the task, the storage space of the cloud resource and the decision situation of the task deployment on the cloud resource, whether the storage capacity of the cloud resource in each strategy meets the corresponding constraint condition is determined.
Optionally, the execution module is specifically configured to: based on the calculation amount required by the task, the calculation capability of the cloud resource and the decision situation of the task deployment on the cloud resource, whether the calculation capability of the cloud resource in each strategy meets the corresponding constraint condition is determined.
Optionally, the execution module is specifically configured to: performing at least one of: exchanging cloud resources of at least one task in every two strategies of the first group to obtain exchanged strategies; and randomly replacing cloud resources of at least one task in each strategy of the first group to obtain replaced strategies.
Optionally, the execution module is further configured to calculate a deployment cost of each policy before the fitness divides the N policies that satisfy the physical constraint condition into the first group and the second group; calculating the default cost of each strategy; and determining the fitness of each strategy according to the deployment cost and the default cost.
Optionally, the execution module is specifically configured to: and calculating the deployment cost of each strategy based on the maximum execution time of the task, the price per unit time of the cloud resource pricing and the decision condition of the target pricing strategy in the cloud resource selected by the task.
Optionally, the execution module is specifically configured to: based on the violation cost caused by the violation of the time sequence constraint by the task in operation and the decision condition of whether the task is violated, calculating the violation cost of each strategy.
Optionally, the execution module is further configured to determine whether the task violates the timing constraint based on an end time of the task, a start time of the task, and a target timing constraint corresponding to the task.
The embodiment of the disclosure provides a device for service flow deployment, firstly, N strategies which are deployed on a plurality of cloud resources and meet physical constraint conditions can be generated for K tasks in at least one service flow, wherein the physical constraint conditions comprise constraint conditions of energy storage capacity and computing capacity; then, the following steps are repeatedly performed W times: dividing the N strategies into 2 groups according to the fitness, preprocessing a group of strategies with large fitness values, judging whether the new strategies obtained after preprocessing meet physical constraint conditions, continuously regenerating the strategies which are not met until the strategies in the group meet the physical constraint conditions, and preprocessing again based on the fitness groups. And finally, after the W-th processing, determining the strategy with the smallest fitness value in the N strategies as a deployment strategy of each task in the business process. Based on the above manner, the deployment strategy with low deployment cost meeting the task requirements can be comprehensively determined according to the time sequence requirements of each task, the computing capacity, the storage capacity and the deployment cost of cloud resources and the default cost in the business process.
The device 500 for service flow deployment provided in the embodiments of the present disclosure can implement each process implemented by the embodiments of the methods of fig. 1 to fig. 4, and in order to avoid repetition, a description is omitted here.
Optionally, as shown in fig. 6, the embodiment of the present disclosure further provides a device 600 for service flow deployment, including a processor 601, a memory 602, and a program or an instruction stored in the memory 602 and capable of being executed on the processor 601, where the program or the instruction is executed by the processor 601 to implement each process of the method embodiment for service flow deployment in the cloud computing environment, and the process may achieve the same technical effect, and for avoiding repetition, a description is omitted herein.
It should be noted that the network entity or the electronic device 700 shown in fig. 7 is only an example, and should not impose any limitation on the functions and the scope of use of the embodiments of the present disclosure.
As shown in fig. 7, the electronic apparatus 700 includes a central processing unit (Central Processing Unit, CPU) 701 that can perform various appropriate actions and processes according to a program stored in a ROM (Read Only Memory) 702 or a program loaded from a storage section 708 into a RAM (Random Access Memory ) 703. In the RAM 703, various programs and data required for the system operation are also stored. The CPU 701, ROM 702, and RAM 703 are connected to each other through a bus 704. An I/O (Input/Output) interface 705 is also connected to bus 704.
The following components are connected to the I/O interface 705: an input section 706 including a keyboard, a mouse, and the like; an output section 707 including a CRT (Cathode Ray Tube), an LCD (Liquid Crystal Display ), and the like, a speaker, and the like; a storage section 708 including a hard disk or the like; and a communication section 709 including a network interface card such as a LAN (Local Area Network, wireless network) card, a modem, or the like. The communication section 709 performs communication processing via a network such as the internet. The drive 710 is also connected to the I/O interface 705 as needed. A removable medium 711 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 710 as necessary, so that a computer program read therefrom is mounted into the storage section 708 as necessary.
In particular, according to embodiments of the present disclosure, the processes described below with reference to flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method shown in the flowcharts. In such an embodiment, the computer program may be downloaded and installed from a network via the communication portion 709, and/or installed from the removable medium 711. When executed by a central processing unit (CPU 701), performs the various functions defined in the system of the present application.
The embodiment of the present disclosure further provides a readable storage medium, where a program or an instruction is stored on the readable storage medium, and when the program or the instruction is executed by a processor, the processes of the method embodiment for service flow deployment in the cloud computing environment are implemented, and the same technical effects can be achieved, so that repetition is avoided, and no further description is given here.
Wherein the processor is a processor in the electronic device described in the above embodiment. The readable storage medium includes computer readable storage medium such as ROM, RAM, magnetic disk or optical disk.
The embodiment of the disclosure further provides a chip, where the chip includes a processor and a communication interface, where the communication interface is coupled to the processor, and the processor is configured to run a program or an instruction, implement each process of the method embodiment for service flow deployment in the cloud computing environment, and achieve the same technical effect, so that repetition is avoided, and no further description is given here.
It should be understood that the chips referred to in the embodiments of the present disclosure may also be referred to as system-on-chip chips, chip systems, or system-on-chip chips, etc.
The embodiments of the present disclosure provide a computer program product including instructions, which when executed on a computer, cause the computer to perform the steps of the method for service flow deployment in a cloud computing environment as described above, and achieve the same technical effects, and are not repeated here.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element. Furthermore, it should be noted that the scope of the methods and apparatus in the embodiments of the present disclosure is not limited to performing the functions in the order shown or discussed, but may also include performing the functions in a substantially simultaneous manner or in an opposite order depending on the functions involved, e.g., the described methods may be performed in an order different from that described, and various steps may also be added, omitted, or combined. Additionally, features described with reference to certain examples may be combined in other examples.
From the above description of the embodiments, it will be clear to those skilled in the art that the above-described embodiment method may be implemented by means of software plus a necessary general hardware platform, but of course may also be implemented by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present disclosure may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium (e.g., ROM/RAM, magnetic disk, optical disk), including several instructions for causing a terminal (which may be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to perform the method described in the embodiments of the present disclosure.
The embodiments of the present disclosure have been described above with reference to the accompanying drawings, but the present disclosure is not limited to the above-described embodiments, which are merely illustrative and not restrictive, and many forms may be made by those of ordinary skill in the art without departing from the spirit of the disclosure and the scope of the claims, which are all within the protection of the present disclosure.
Claims (10)
1. A method of business process deployment in a cloud computing environment, the method comprising:
initializing and generating N strategies for deploying K tasks on a plurality of cloud resources, wherein the N strategies meet physical constraint conditions, the K tasks are tasks in at least one business process, the physical constraint conditions comprise constraint conditions of storage capacity and computing capacity, and K and N are integers larger than 1;
the following A1 to A4 are repeatedly performed W times:
a1: dividing N strategies meeting the physical constraint condition into a first group and a second group based on fitness, wherein the fitness of the strategies of the first group is larger than that of the strategies of the second group, and the fitness indicates the deployment cost and the degree of default cost of the strategies;
a2: preprocessing cloud resources of each strategy of the first group to obtain preprocessed strategies, and determining whether the preprocessed strategies meet the physical constraint conditions;
A3: if Q strategies do not meet the physical constraint condition, regenerating the Q strategies, and determining whether the regenerated strategies meet the physical constraint condition or not until Q strategies meeting the physical constraint condition are obtained, wherein Q is a positive integer;
a4: determining a second group of strategies, the Q strategies meeting the physical constraint conditions and the adaptability of each strategy in the strategies meeting the physical constraint conditions in the preprocessed strategies;
and determining a strategy with the minimum N fitness values calculated at the W time as a deployment strategy of the K tasks, wherein W is an integer larger than 1.
2. The method of claim 1, wherein the initializing generates N policies meeting physical constraints for deploying K tasks on the plurality of cloud resources, comprising:
generating N strategies for deploying K tasks on a plurality of cloud resources, and determining whether each strategy meets physical constraint conditions;
if P strategies do not meet the physical constraint condition, regenerating the P strategies, and determining whether the regenerated strategies meet the physical constraint condition or not until N strategies meeting the physical constraint condition are obtained, wherein P is a positive integer less than or equal to N.
3. The method of claim 1, wherein the physical constraints include constraints on energy storage capacity and computing capacity;
the determining whether each policy satisfies a physical constraint includes:
determining whether the storage capacity and the computing capacity of the cloud resources in each policy meet corresponding constraint conditions.
4. A method according to claim 3, wherein said determining whether the storage capacity of the cloud resources in each policy satisfies the corresponding constraint comprises:
based on the storage space required by the task, the storage space of the cloud resource and the decision situation of the task deployment on the cloud resource, whether the storage capacity of the cloud resource in each strategy meets the corresponding constraint condition is determined.
5. A method according to claim 3, wherein said determining whether the computing power of the cloud resources in each policy satisfies the corresponding constraint comprises:
based on the calculation amount required by the task, the calculation capability of the cloud resource and the decision situation of the task deployment on the cloud resource, whether the calculation capability of the cloud resource in each strategy meets the corresponding constraint condition is determined.
6. The method of claim 1, wherein the pre-processing of cloud resources for each policy of the first group results in a pre-processed policy comprising at least one of:
Exchanging cloud resources of at least one task in every two strategies of the first group to obtain exchanged strategies;
and randomly replacing cloud resources of at least one task in each strategy of the first group to obtain replaced strategies.
7. The method of claim 1, wherein prior to the classifying the N policies satisfying the physical constraint into the first and second groups based on fitness, the method further comprises:
calculating the deployment cost of each strategy;
calculating the default cost of each strategy;
and determining the fitness of each strategy according to the deployment cost and the default cost.
8. The method of claim 7, wherein the calculating the deployment cost for each policy comprises:
and calculating the deployment cost of each strategy based on the maximum execution time of the task, the price per unit time of the cloud resource pricing and the decision condition of the target pricing strategy in the cloud resource selected by the task.
9. The method of claim 7, wherein said calculating the violation costs for each policy comprises:
based on the violation cost caused by the violation of the time sequence constraint by the task in operation and the decision condition of whether the task is violated, calculating the violation cost of each strategy.
10. An apparatus for business process deployment, wherein the apparatus for business process deployment comprises: the device comprises a generation module, an execution module and a determination module;
the generating module is used for generating N strategies meeting physical constraint conditions of K tasks deployed on a plurality of cloud resources, the K tasks are tasks in at least one business process, the physical constraint conditions comprise constraint conditions of energy storage capacity and computing capacity, and K and N are integers larger than 1;
the execution module is used for repeatedly executing the following A1 to A4 for W times: a1: dividing N strategies meeting the physical constraint condition into a first group and a second group based on fitness, wherein the fitness of the strategies of the first group is larger than that of the strategies of the second group, and the fitness indicates the deployment cost and the degree of default cost of the strategies; a2: preprocessing the cloud resources of each strategy of the first group to obtain preprocessed strategies, and determining whether the preprocessed strategies meet the physical constraint conditions; a3: if Q strategies do not meet the physical constraint condition, regenerating the Q strategies, and determining whether the regenerated strategies meet the physical constraint condition or not until Q strategies meeting the physical constraint condition are obtained, wherein Q is a positive integer; a4: determining a second group of strategies, Q strategies meeting the physical constraint conditions, and the adaptability of each strategy in the strategies meeting the physical constraint conditions in the preprocessed strategies;
The determining module is used for determining a strategy with the smallest N fitness values calculated by the executing module at the W time as a deployment strategy of the K tasks; w is an integer greater than 1.
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| CN108694077A (en) * | 2017-04-10 | 2018-10-23 | 中国科学院声学研究所 | Based on the distributed system method for scheduling task for improving binary system bat algorithm |
| CN107133091A (en) * | 2017-05-08 | 2017-09-05 | 武汉轻工大学 | The cloud workflow task dispatching method being classified based on top-down task |
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