WO2024055877A1

WO2024055877A1 - Method and apparatus for deploying business process in cloud computing environment

Info

Publication number: WO2024055877A1
Application number: PCT/CN2023/117095
Authority: WO
Inventors: 孙梦宇; 王旭亮; 黄志兰
Original assignee: 中国电信股份有限公司
Priority date: 2022-09-15
Filing date: 2023-09-05
Publication date: 2024-03-21
Also published as: CN115460087A; CN115460087B

Abstract

The present disclosure provides a method and apparatus for deploying a business process in a cloud computing environment, and belongs to the technical field of communication. The method comprises: initializing and generating N policies satisfying physical constraint conditions for deploying K tasks on a plurality of cloud resources; repeatedly executing W times the following A1 to A4: A1: based on suitability, dividing the N policies satisfying the physical constraint conditions into a first group and a second group; A2: preprocessing the cloud resources in each policy of the first group; A3: if there are Q policies which do not satisfy the physical constraint conditions, regenerating Q policies until Q policies satisfying the physical constraint conditions are obtained; A4: determining the suitability of each policy of the policies satisfying the physical constraint conditions; and determining the policy having the lowest of the N suitability values following W computations to be the deployment policy for the K tasks. The technical solution provided by the embodiments of the present disclosure allows for selecting suitable cloud resources and service policies for business processes.

Description

Method and device for business process deployment in cloud computing environment

Cross-references to related applications

This application claims priority to the Chinese patent application with application number 202211124516.8 and titled "Method and Device for Business Process Deployment in a Cloud Computing Environment" submitted on September 15, 2022. The entire content of this Chinese patent application is fully incorporated by reference. Incorporated herein.

Technical field

The present disclosure belongs to the field of communication technology, and specifically relates to a method and device for business process deployment in a cloud computing environment.

Background technique

With the development of virtualization technology and distributed computing technology, cloud computing has become a strategic focus of the development of the information industry and can support cost-effective use of computing resources.

Usually, a business process consists of multiple tasks, which need to be connected according to certain logical relationships and sequence relationships, and certain timing constraints are attached to each connection edge. The delivery of user business process requirements has time constraints and needs to be executed on time in a logical sequence to avoid business breaches that lead to a decrease in user experience quality.

However, how to select appropriate cloud resources and the best service strategy for each task according to the order of the business process, while meeting the timing constraints in the business process, is an urgent technical problem that needs to be solved.

Contents of the invention

In a first aspect, embodiments of the present disclosure provide a method for business process deployment in a cloud computing environment. The method includes: initializing and generating N policies that satisfy physical constraints for deploying K tasks on multiple cloud resources. The K Each task is a task in at least one business process. Physical constraints include storage capacity and computing capacity constraints. K and N are both integers greater than 1; repeat the following A1 to A4 W times: A1: based on fitness Divide N strategies that satisfy physical constraints into the first group and the second group. The fitness of the strategies in the first group is greater than the fitness of the strategies in the second group. The fitness indicates the deployment cost and default cost of the strategy; A2: Preprocess the cloud resources of each policy in the first group, obtain the preprocessed policy, and determine whether the preprocessed policy satisfies the physical constraints; A3: If there are Q policies that do not meet the physical constraints, re- Generate Q strategies, and determine whether the regenerated strategies satisfy the physical constraints, until Q strategies that satisfy the physical constraints are obtained, Q is a positive integer; A4: Determine the second group of strategies, the Q strategies that satisfy the physical constraints strategy, and the fitness of each strategy in the preprocessed strategy that satisfies the physical constraints; the strategy with the smallest median fitness value of the N calculations for the Wth time is determined as the deployment strategy for the K tasks, W is an integer greater than 1.

In the second aspect, embodiments of the present disclosure provide a device for business process deployment. The device includes: a generation module, an execution module and a determination module; the generation module is used to initialize and generate N for deploying K tasks on multiple cloud resources. A strategy that satisfies physical constraints. The K tasks are tasks in at least one business process. The physical constraints include storage capacity and computing capacity constraints. Both K and N are integers greater than 1; the execution module is used to repeat Execute the following A1 to A4 W times: A1: Divide N strategies that satisfy physical constraints into the first group and the second group based on fitness. The fitness of the strategies of the first group is greater than the adaptation of the strategies of the second group. Degree, fitness indicates the degree of deployment cost and default cost of the strategy; A2: Preprocess the cloud resources of each strategy in the first group, obtain the preprocessed strategy, and determine whether the preprocessed strategy satisfies the physical constraints. ; A3: If there are Q strategies that do not satisfy the physical constraints, regenerate Q strategies and determine whether the regenerated strategies satisfy the physical constraints until Q strategies that satisfy the physical constraints are obtained, Q is a positive integer; A4 : Determine the fitness of each strategy in the second group of strategies, Q strategies that satisfy physical constraints, and the preprocessed strategies that satisfy physical constraints; determine the module, which is also used to calculate the execution module for the Wth time The strategy with the smallest median fitness among the N is determined as the deployment strategy for the K tasks; W is an integer greater than 1.

In a third aspect, embodiments of the present disclosure provide a server, which includes a processor, a memory and a server stored in There are programs or instructions on the memory that can be run on the processor. When the programs or instructions are executed by the processor, the steps of the method for business process deployment in a cloud computing environment as described in the first aspect are implemented.

In a fourth aspect, embodiments of the present disclosure provide a readable storage medium that stores programs or instructions. When the programs or instructions are executed by a processor, the cloud computing environment as described in the first aspect is implemented. Steps in the business process deployment method.

In a fifth aspect, embodiments of the present disclosure provide a chip. The chip includes a processor and a communication interface. The communication interface is coupled to the processor. The processor is used to run programs or instructions to implement the first aspect. The method of business process deployment in a cloud computing environment.

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 for business process deployment in a cloud computing environment as described in the first aspect.

Description of drawings

Figure 1 is a schematic diagram of the deployment of business processes on cloud resources in a cloud computing environment provided by an embodiment of the present disclosure;

Figure 2 is one of the flow diagrams of a method of business process deployment in a cloud computing environment provided by an embodiment of the present disclosure;

Figure 3 is a second schematic flowchart of a method for business process deployment in a cloud computing environment provided by an embodiment of the present disclosure;

Figure 4 is a third schematic flowchart of a method for business process deployment in a cloud computing environment provided by an embodiment of the present disclosure;

Figure 5 is one possible structural diagram of a device for business process deployment provided by an embodiment of the present disclosure;

Figure 6 is a second possible structural diagram of a device for business process deployment provided by an embodiment of the present disclosure;

Figure 7 is a schematic diagram of a device hardware for business process deployment provided by an embodiment of the present disclosure.

Detailed ways

In order to facilitate understanding, the relevant terms involved in the embodiments of the present disclosure are first explained:

In a cloud computing environment, different cloud service resources (recorded as cloud resources) can be included. Users in the network can put forward functional requirements and select different cloud service resources for rent to meet the needs of network users. Generally, user requirements are not a single atomic task. In the method of business process deployment in a cloud computing environment provided by embodiments of the present disclosure, user requirements are split into a series of task sets with logical relationships and timing constraints, and each task is rented separately Different cloud service resources, while satisfying business process timing constraints as much as possible, can find deployment strategies that minimize service costs according to different pricing strategies of cloud service resources.

1. Business process

The business process bp is expressed as a tuple, where bp={Tsk,Edg}, Tsk represents the set of tasks included in the business process, Tsk={tsk ₁ ,tsk ₂ ,…}, and Edg represents the relationship between different tasks in the business process. The sequential edges of

2. Task

Represent the task tsk as a six-tuple, where tsk={nm,dsc,χ,β,dur,pen}, nm represents the name of tsk, dsc represents the text description of the task, χ represents the amount of calculation required by the task, β Represents the storage space required by the task, dur represents the duration interval of executing the task, and pen represents the violation cost caused by the task exceeding the timing constraints.

3. Cloud resources

A four-tuple represents the cloud resource CR, where CR = {pvd, cmp, str, pcs}, pvd represents the cloud service provider, cmp represents the computing power of the server corresponding to the cloud resource, and str represents the memory space of the cloud resource. pcs represents the pricing strategy of cloud resources.

Generally, cloud service pricing strategies can be divided into the following two models:

(1) Prepaid strategy m ₁

You can choose cloud servers with different configurations, and you can use cloud resources at will within a certain period of time by paying a certain fee in advance.

(2) Minute-by-minute billing strategy m ₂

Pay for cloud resources based on unit price and usage time in a pay-as-you-go manner.

The technical solutions in the embodiments of the present disclosure will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present disclosure. Obviously, the described embodiments are part of the embodiments of the present disclosure, rather than all of the embodiments. Based on the embodiments in this disclosure, all other embodiments obtained by those of ordinary skill in the art without making creative efforts fall within the scope of protection of this disclosure.

The terms "first", "second", etc. in the description and claims of the present disclosure are used to distinguish similar objects and are not used to describe a specific order or sequence. It is to be understood that the figures so used are interchangeable under appropriate circumstances so that embodiments of the present disclosure can be practiced in sequences other than those illustrated or described herein, and that "first," "second," etc. are distinguished Objects are usually of one type, and the number of objects is not limited. For example, the first object can be one or multiple. In addition, "and/or" in the description and claims indicates at least one of the connected objects, and the character "/" generally indicates that the related objects are in an "or" relationship.

It is worth pointing out that the technology described in the embodiments of the present disclosure is not limited to LTE (Long Term Evolution, Long Term Evolution)/LTE-A (LTE-Advanced, LTE Evolution) systems, and can also be used in other wireless communication systems, such as CDMA (Code Division Multiple Access, Code Division Multiple Access), TDMA (Time Division Multiple Access, Time Division Multiple Access), FDMA (Frequency Division Multiple Access, Frequency Division Multiple Access), OFDMA (Orthogonal Frequency Division Multiple Access, Orthogonal Frequency Division Multiple Access) address), SC-FDMA (Single-carrier Frequency-Division Multiple Access, single-carrier frequency division multiple access) and other systems. The terms "system" and "network" in the embodiments of this application are often used interchangeably, and the described technology can be used not only for the above-mentioned systems and radio technologies, but also for other systems and radio technologies. However, the following description describes an NR system for example purposes, and NR terminology is used in most of the following description, although these techniques can also be applied to applications other than NR system applications, such as 6G (6th Generation, 6th Generation) communication systems. .

The method of business process deployment in a cloud computing environment provided by the embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings through specific embodiments and application scenarios.

Figure 1 is a schematic diagram of the deployment of business processes on cloud resources in a cloud computing environment provided by an embodiment of the present disclosure. As shown in Figure 1, it includes i cloud resources, where i is an integer greater than 1. Each cloud resource includes four attributes: cloud provider, computing power, storage space and pricing strategy, as shown in Figure 1 respectively. Take the deployment of two business processes as an example. Business process bp1 includes four tasks, namely: tsk _1-1 , tsk _1-2 , tsk _1-3 and tsk _1-4 . Business process bp ₂ includes 3 tasks, namely: tsk _2-1 , tsk _2-2 and tsk _2-3 .

Among them, the duration interval for executing tsk _1-1 is [3,4], the duration interval for executing tsk _1-2 is [1,3], and the duration interval for executing tsk _1-3 is [2,4], The duration interval for executing tsk _1-4 is [3,5], the duration interval for executing tsk _2-1 is [1,2], the duration interval for executing tsk _2-2 is [2,4], and the duration interval for executing tsk The duration interval of _2-3 is [4,6].

Among them, tsk _1-1 and tsk _1-2 are deployed on CR ₁ , tsk _1-3 , tsk _1-4 and tsk _2-3 are deployed on CR ₂ , and tsk _2-1 and tsk _2-2 are deployed on CR 1. Executed on CR _i , the logical sequence of execution of each task is the logical sequence indicated by the arrows between each task in Figure 1.

Figure 2 is a schematic flowchart of a method of business process deployment in a cloud computing environment provided by an embodiment of the present disclosure. As shown in Figure 2, the method includes the following S201 to S203:

S201. Initialize and generate N strategies that satisfy physical constraints for deploying K tasks on multiple cloud resources.

Among them, K tasks are tasks in at least one business process. Physical constraints include storage capacity and computing capacity constraints. K and N are both positive integers greater than 1.

Optionally, in conjunction with Figure 2, as shown in Figure 3, the above-mentioned S201 can be specifically executed through the following S21 and S22:

S21. Generate N strategies for deploying K tasks on multiple cloud resources, and determine whether each strategy satisfies physical constraints.

For example, during initialization, N strategies may be randomly generated.

It should be noted that if any of the storage capacity and computing power in a policy does not meet the corresponding constraints, the policy does not meet the physical constraints. If the storage capacity and computing power in a policy both meet the corresponding constraints , then the strategy satisfies the physical constraints.

For example, in order to facilitate understanding of the technical solution provided by the embodiment of the present disclosure, Table 1 is an exemplary table of a task deployment strategy for a business process provided by the embodiment of the present disclosure. It is assumed that the three cloud resources can be business process 1 and A total of 7 tasks in business process 2 provide services. Among them, 20 deployed strategies are randomly generated. Then it can be determined whether each of the 20 strategies satisfies the physical constraints.

It should be noted that, for the sake of explanation, taking policy 1 as "1323113", policy 1 means that task 1 is deployed on cloud resource 1, task 2 is deployed on cloud resource 3, task 3 is deployed on cloud resource 2, and task 4 is deployed on On cloud resource 3, task 5 is deployed on cloud resource 1, task 6 is deployed on cloud resource 1, and task 7 is deployed on task 3.

Table 1

S22. If there are P strategies that do not satisfy the physical constraints, regenerate P strategies and determine whether the regenerated strategies satisfy the physical constraints until N strategies that satisfy the physical constraints are obtained.

Among them, P is a positive integer less than or equal to N.

Specifically, if there are P strategies among the N strategies that do not satisfy the physical constraints, delete the P strategies, regenerate the P strategies, and determine whether the regenerated P strategies satisfy the physical constraints. If they all meet, Then execute the following S202. If there are policies that do not satisfy the physical constraints among the regenerated P policies, continue to delete and generate new policies until N policies that satisfy the physical constraints are obtained, and then execute the following S202. .

For example, assuming that 5 of the above 20 strategies do not meet the physical constraints, you can delete the 5 strategies and regenerate 5 strategies, and then determine whether the 5 strategies meet the physical constraints. If the 5 strategies If all the policies satisfy the physical constraints, continue to perform the following S202. If at least one of the five regenerated policies does not satisfy the physical constraints, continue to delete the unsatisfied policies, regenerate them, and continue to determine whether they are satisfied. Physical constraints.

S202. Repeat the following A1 to A4 W times:

A1: Divide N strategies that satisfy physical constraints into the first group and the second group based on fitness.

Among them, the fitness of the first group of strategies is greater than the fitness of the second group of strategies.

Among them, in the embodiment of the present disclosure, the fitness indicates the degree of deployment cost and default cost of the strategy. Among them, the smaller the fitness is, the lower the cost of indicating strategy deployment is, and the better the strategy is; the larger the fitness is, the higher the cost of indicating strategy deployment is, and the worse the strategy is.

For example, the first group of strategies can rank the N/2 strategies of the bottom N/2 among the N strategies from small to large in terms of fitness, and the second group of strategies can rank the top of the N strategies in terms of fitness from small to large. N/2 strategies for N/2. Or, the first group The strategy may be a strategy among the N strategies whose fitness is greater than or equal to the preset fitness, and the second group of strategies may be a strategy among the N strategies whose fitness is less than the preset fitness.

Continuing to combine the above example, after the processing of S202, 20 strategies that satisfy the physical constraints can be obtained, and then the fitness of each strategy can be calculated, thereby obtaining the fitness of the 20 strategies. Taking the above-mentioned first grouping method as an example, we can obtain a first group of 10 strategies with relatively large fitness and a second group of 10 strategies with relatively small fitness.

A2: Preprocess the cloud resources of each policy in the first group, obtain the preprocessed policy, and determine whether the preprocessed policy satisfies the physical constraints.

Continuing to combine the above examples, for the first group of 10 strategies with relatively large fitness, preprocess the cloud resources corresponding to the tasks in each strategy to obtain 10 new strategies, and then calculate whether the 10 new strategies Meet physical constraints.

It should be noted that if the preprocessed strategies this time all meet the physical constraints, the fitness of the preprocessed strategies will be determined directly, and based on the fitness of the strategies in the second group this time and the preprocessed strategies The fitness regroups the N strategies and performs the next processing.

A3: If there are Q strategies that do not satisfy the physical constraints, regenerate Q strategies and determine whether the regenerated strategies satisfy the physical constraints until Q strategies that satisfy the physical constraints are obtained.

Among them, Q is a positive integer.

A4: Determine the fitness of each strategy in the second group of strategies, the above-mentioned Q strategies that satisfy physical constraints, and the preprocessed strategies that satisfy physical constraints.

It should be noted that in A4, the fitness of N strategies was obtained in total.

Among them, the fitness of the second group of strategies determined in A4 may be the fitness calculated when performing grouping in A1 each time A1 to A4 are executed. That is, the fitness that needs to be calculated in A4 is the fitness of the strategy after processing A2 and A3 for the first group of strategies.

S203. Determine the strategy with the smallest median fitness value among the N determined for the Wth time as the deployment strategy for the K tasks.

Among them, W is a positive integer.

It can be understood that the smallest fitness value can indicate that the total deployment cost of the strategy is the smallest.

Embodiments of the present disclosure provide a method for deploying business processes in a cloud computing environment. First, N policies that satisfy physical constraints and are deployed on multiple cloud resources can be initially generated for a total of K tasks in at least one business process. The physical Constraints include storage capacity and computing capacity constraints; then, repeat the following steps W times: divide the N strategies into 2 groups according to fitness, and then preprocess the group of strategies with larger fitness values. Determine whether the new policy obtained after preprocessing satisfies the physical constraints. If it does not, continue to regenerate until all policies in the group meet the physical constraints, and then preprocess again based on fitness grouping. Finally, after the Wth processing, the strategy with the smallest fitness value among the N strategies is determined as the deployment strategy for each task in the business process. Based on the above method, a low-cost deployment strategy that meets task requirements can be comprehensively determined based on the timing requirements of each task in the business process, the computing power, storage capacity, deployment cost, and default cost of cloud resources.

Optionally, in the method for deploying business processes in a cloud computing environment provided by embodiments of the present disclosure, "determining whether each policy satisfies physical constraints" in the above S21 can be performed by the following S11:

S11. Determine whether the storage capacity and computing capacity of the cloud resources in each policy meet the corresponding constraints.

Combined with Strategy 1 in Table 1, it is necessary to calculate whether the storage capacity and computing capacity of each cloud resource meet the corresponding constraints when executing the corresponding tasks. For example, it is necessary to calculate whether the storage capacity and computing capacity of cloud resource 1 satisfy the corresponding constraints when executing tasks 1, 5, and 6, and whether the storage capacity and computing capacity of cloud resource 2 satisfy the corresponding constraints when executing task 3. Conditions, whether the storage capacity and computing capacity of cloud resource 3 meet the corresponding constraints when executing tasks 2, 4, and 7.

Based on this solution, after generating strategies for deploying each task on cloud resources, it can be determined whether the storage capacity of the cloud resources involved in each strategy meets the requirements of the tasks deployed on the resource and whether the computing capacity meets the deployment requirements. The requirements of the task on the resource can be eliminated from the randomly generated strategies that do not meet the physical constraints.

Optionally, in the method of business process deployment in a cloud computing environment provided by embodiments of the present disclosure, when determining whether the storage capacity of cloud resources in each policy satisfies the corresponding constraints, the above-mentioned S11 may include the following S11a:

S11a. Based on the storage space required by the task, the storage space of the cloud resource, and the decision-making situation of deploying the task on the cloud resource, determine whether the storage capacity of the cloud resource in each policy meets the corresponding constraints.

For example, it can be determined based on the following formula (1) whether the storage capacity of the cloud resource under the policy satisfies the storage constraints.

Among them, X _ij represents the decision-making situation of deploying task _tsk _i on cloud resource CR _j . X _ij =1 indicates that task _{tsk i} _is deployed on cloud resource CR _j _. , CR _j .str represents the storage space of cloud resource CR _j .

It can be understood that if the storage capacity of the cloud resources in a policy satisfies the above formula (1), then the storage capacity of the cloud resources in the policy satisfies the corresponding constraints. If the storage capacity of the cloud resources in a policy does not meet the above Formula (1), then the strategy does not satisfy the physical constraints.

Based on this solution, the storage space of each cloud resource involved in the policy can be determined based on the storage space required for each task in the business process, the deployment of each task on cloud resources in a policy, and the storage space of each cloud resource. Whether the capabilities meet the requirements of the task can be determined to determine whether the policy is a policy that meets the storage requirements, so as to facilitate subsequent screening.

Optionally, in the method of business process deployment in a cloud computing environment provided by embodiments of the present disclosure, when determining whether the storage capacity of cloud resources in each policy meets the corresponding constraints, the above S11 may include the following S11b:

S11b. Based on the amount of computing required for the task, the computing power of the cloud resource, and the decision-making situation of deploying the task on the cloud resource, determine whether the computing power of the cloud resource in each strategy meets the corresponding constraints.

Exemplarily, whether the storage capacity of cloud resources under each policy meets the storage constraint condition can be determined based on the following formula (2).

Among them, CR _j .cmp represents the computing capability of cloud resource CR _j .

It can be understood that if the computing power of the cloud resources in a policy satisfies the above formula (2), then the computing power of the cloud resources in the policy satisfies the corresponding constraints. If the computing power of the cloud resources in a policy does not meet the above Formula (2), then the strategy does not satisfy the physical constraints.

Based on this solution, the computing power of each cloud resource involved in the strategy can be determined based on the amount of computing required for each task in the business process, the deployment of each task on cloud resources in a strategy, and the computing capabilities of each cloud resource. Whether the capabilities meet the requirements of the task can be determined to determine whether the strategy is a strategy that meets the computing requirements, so as to facilitate subsequent screening.

Optionally, in the method for deploying business processes in a cloud computing environment provided by embodiments of the present disclosure, the preprocessing of the cloud resources in A2 may be at least one of the following two methods:

Preprocessing method 1: Exchange the cloud resources of at least one task in each two strategies of the first group to obtain the exchanged strategy.

For example, the cloud resources of tasks 4 to 6 in the policy "123 123 2" and the policy "321 232 3" are exchanged to obtain two new policies "123 232 2" and "321 123 3".

Preprocessing method 2: Randomly replace the cloud resources of at least one task in each strategy of the first group to obtain the replaced strategy.

For example, the cloud resources of task 3 in the policy "12 3 2322" are randomly changed, for example, to the new policy "12 1 2322".

For example, if the preprocessing of cloud resources includes the above two methods, the above preprocessing method 1 can be performed first, and then the above preprocessing method 2 can be performed, or the above preprocessing method 2 can be performed first, and then the above preprocessing method can be performed Preprocessing method 1, the embodiment of the present disclosure does not specifically limit this.

It can be understood that in the embodiment of the present disclosure, the policy obtained after preprocessing is actually a brand new deployment policy. Therefore, it is necessary to continue to determine whether the new policy satisfies the physical constraints.

Based on this solution, after grouping N policies based on fitness, the policies with higher deployment costs and default costs indicated by fitness can be preprocessed, thereby generating a new set of policies based on this group of policies to facilitate subsequent policy implementation. Screen and select the strategy with the best fitness for task deployment.

Optionally, the method for business process deployment in a cloud computing environment provided by the embodiment of the present disclosure further includes the following A5 to A7 before the above-mentioned A1:

A5: Calculate the deployment cost of each strategy.

A6: Calculate the default cost of each strategy.

A7: Determine the fitness of each strategy based on the deployment cost and default cost.

For example, the fitness of a policy = deployment cost + default cost.

Based on this solution, before executing A1, the fitness of the N strategies generated by S201 above that meet the physical constraints can be judged first to evaluate the deployment cost and default cost of each strategy, so as to facilitate the subsequent evaluation of the deployment cost and default cost. The more expensive strategy is preprocessed, and subsequent processing is performed based on the strategy obtained after preprocessing, so as to obtain the strategy with optimal fitness.

Optionally, in the method of business process deployment in a cloud computing environment provided by the embodiments of the present disclosure, the above-mentioned A5 can be specifically performed by the following A51:

A51: Calculate the deployment cost of each strategy based on the maximum execution time of the task, the unit time price of cloud resource pricing, and the decision-making situation of the target pricing strategy in the cloud resources selected by the task.

For example, the deployment cost of each strategy can be determined based on the following formula (3).

Among them, Max(tsk _i.dur ) represents the maximum execution time of task tsk _i , c _ij represents the unit time price of cloud resource CR _j selected by task tsk _i , and X _ijm represents the pricing of cloud resource CR _j selected by task tsk _i . The decision-making situation under strategy m can be a binary variable. For example, X _ijm =1 indicates that task tsk _i selects pricing strategy m for cloud resource CR _j , and X _ijm =0 indicates that task tsk _i does not select pricing strategy m for cloud resource CR _j .

Based on this solution, the deployment cost of each strategy can be calculated based on the maximum execution time of each task requirement, the pricing strategy of each cloud resource, the unit time price of cloud resource pricing, and the decision-making situation of the target pricing strategy for task selection, so that it can be based on The deployment cost is comprehensively selected to select a better deployment strategy.

Optionally, in the method of business process deployment in a cloud computing environment provided by the embodiments of the present disclosure, the above-mentioned A6 can be specifically performed by the following A61:

A61: Calculate the violation cost of each policy based on the violation cost caused by the task's violation of timing constraints during operation and the decision-making situation of whether the task violates the violation.

For example, the violation cost of each policy can be determined based on the following formula (4).

Among them, pen _i represents the violation cost caused by task tsk _i violating timing constraints during the deployment and running process, Y _i Indicates the decision-making status of whether task tsk _i violates the rules. Yi ₌ 1 indicates that the timing constraints of task tsk _i are not fully met. Yi ₌ 0 indicates that the timing constraints of task tsk _i are completely met and no violation is caused.

Furthermore, the fitness of each of the above strategies can be determined based on the following formula (5).
FIT＝COST+PEN formula (5)

For example, in the above-mentioned S204, the smallest fitness among the fitnesses of the N strategies may be determined based on the following formula (6).
Objt＝Min(COST+PEN) Formula (6)

Based on this solution, the violation cost of each policy can be determined based on the cost of the task to be deployed violating timing constraints during operation and whether the deployment of each task in the strategy on each cloud resource causes timing violations, so that it can be accurately judged Check whether each strategy to be deployed meets the timing requirements of the task, so that the best deployment strategy that meets the requirements of each task can be accurately determined based on timing.

Optionally, in the method of business process deployment in a cloud computing environment provided by embodiments of the present disclosure, in the above-mentioned A61, it can be determined whether the task violates the task based on the end time of the task, the start time of the task and the target timing constraint corresponding to the task. Timing constraints.

For example, whether the tasks in the policy violate timing constraints can be determined based on the following formula (7).
0≤F(tsk _i )-S(tsk _i )≤bp.stk _i .TC Formula (7)

Among them, F(tsk _i ) represents the end time of task tsk _i in the strategy, S(tsk _i ) represents the start time of task tsk _i in the strategy, bp.stk _i.TC represents the time corresponding to task tsk _i in the business process bp. Timing constraints.

Based on this solution, it can be determined whether the actual execution of tasks in the policy violates the timing constraints of task requirements based on the expected processing time of each task in each strategy and the timing constraints of task requirements in the business process, so as to facilitate the implementation of tasks based on timing requirements and deployment. Estimate timing conditions to determine a deployment strategy that meets mission requirements.

Example:

Figure 4 is a schematic diagram of a business process optimization deployment process in a cloud computing environment based on a genetic algorithm provided by an embodiment of the present disclosure. As shown in Figure 4, the above formula (6) can be used as the objective function of the genetic algorithm, that is, to find adaptation For the business deployment strategy with the smallest degree, the cloud resource location deployed by each task is recorded as a gene, the deployment locations of all tasks in the entire business process are regarded as a chromosome, the population size is set to 20, and the objective function of formula (6) is regarded as the Adaptation function to optimize the deployment of business processes in cloud computing environment.

S401. Initialize the population. Assume that cloud resources are randomly deployed for 7 tasks in 2 business processes. Examples of chromosome numbers corresponding to the deployed cloud resources are: 1323113, 2312321, 1231232, ..., 3212323 (a total of 20 chromosomes) . Among them, for each deployment strategy, it is judged according to formulas (1) and (2) whether the cloud resources selected in the strategy meet the service physical constraints, including storage constraints and computing constraints, and the chromosomes that cannot be satisfied are eliminated and re-initialized to generate new ones. chromosome, and repeatedly determine whether physical constraints are met.

S402. For all individuals in the initial population, calculate the deployment cost of the strategy involved in each chromosome according to formula (3), judge whether the deployment strategy corresponding to each chromosome will violate the timing constraints of the service according to formula (4), and calculate each The cost of default on chromosomes. The objective function of formula (5) is generated based on formula (3) and formula (4), and this objective function is used as the population fitness function to calculate the fitness function values of all individuals in the initial population, and substitute the strategies represented by different chromosomes into the adaptation functions. The fitness function is calculated and the fitness function value is obtained.

Among them, under this setting, the smaller the fitness function value, the better the deployment strategy.

S403. Determine whether the iteration ends.

S404. Perform a selection operation to directly inherit the optimized individuals in the population to the next generation population. For example, the fitness function value corresponding to chromosome 2312321 is smaller, indicating that its deployment strategy is better and can be directly inherited to the next generation population; OK The crossover operation sorts the individuals in the population except the above-mentioned optimized individuals according to their fitness, exchanges some genes of adjacent individuals, and evolves the individuals in the population; performs mutation operation on the evolved deployment strategy, and performs mutation operation on the deployment strategy in the population. Some gene values in the individual are randomly changed, and the individuals in the population are further evolved.

For example, the 4-6 genes in chromosomes 1231232 and 3212323 are exchanged to obtain 1232322 and 3211233. And for each deployment strategy after the exchange, according to formulas (1) and (2), it is judged whether the exchanged cloud resources meet the service physical constraints, including storage constraints and computing constraints, and the chromosomes that cannot be satisfied are eliminated and re-initialized. .

For example, the third gene in chromosome 1232322 mutates to 1212322. For each deployment strategy after mutation, we use formulas (1) and (2) to determine whether the cloud resources after mutation operation meet the service physical constraints, including storage constraints and computing constraints. We eliminate chromosomes that cannot meet the constraints and reinitialize them.

S406. Obtain the next generation population after the first evolution, use the fitness function to evaluate the population, and continue operations from S402 to S405 until the maximum number of iterations is met.

S407. Obtain the optimized deployment strategy of the business process in the cloud computing environment based on the individual deployment method with the smallest fitness function value in the last generation population.

For example, if the chromosome with the smallest fitness function value is finally selected as 1122323, it means: tsk ₁ of bp ₁ is deployed in CR ₁ , tsk ₂ of bp ₁ is deployed in CR ₁ , tsk ₃ of bp ₁ is deployed in CR ₂ , and tsk ₁ of bp is deployed in CR 1. ₄ is deployed in CR ₂ , tsk ₁ of bp ₂ is deployed in CR ₃ , tsk ₂ of bp ₂ is deployed in CR ₂ , and tsk ₃ of bp ₂ is deployed in CR ₃ .

It should be noted that, in the method of business process deployment in a cloud computing environment provided by embodiments of the present disclosure, the execution subject may also be a device for business process deployment, or a device in the business process deployment device for executing business processes in a cloud computing environment. The control module of the deployed method. In the embodiment of the present disclosure, a method of using a business process deployment device to perform a business process deployment in a cloud computing environment is taken as an example to illustrate the device for business process deployment provided by the embodiment of the present disclosure.

Figure 5 is a schematic structural diagram of a business process deployment device provided by an embodiment of the present disclosure. As shown in Figure 5, the business process deployment device 500 includes: a generation module 501, an execution module 502 and a determination module 503; the generation module 501 , used to generate N strategies that satisfy physical constraints for deploying K tasks on multiple cloud resources. The K tasks are tasks in at least one business process. The physical constraints include constraints on storage capabilities and computing capabilities. K and N are both integers greater than 1; the execution module 502 is used to repeatedly execute the following A1 to A4 W times: A1: Divide N strategies that satisfy physical constraints into the first group and the second group based on fitness , the fitness of the strategies of the first group is greater than the fitness of the strategies of the second group, and the fitness indicates the deployment cost and default cost of the strategy; A2: Preprocessing of cloud resources for each strategy of the first group, the pre-processed After processing the strategy, determine whether the preprocessed strategy satisfies the physical constraints; A3: If there are Q strategies that do not satisfy the physical constraints, regenerate Q strategies and determine whether the regenerated strategies satisfy the physical constraints until Obtain Q strategies that satisfy physical constraints, and Q is a positive integer; A4: Determine the second group of strategies, Q strategies that satisfy physical constraints, and each strategy in the preprocessed strategy that satisfies physical constraints. The fitness; the determination module 503 is used to determine the strategy with the smallest median fitness among the N calculations for the Wth time as the deployment strategy for the K tasks, where W is an integer greater than 1.

Optionally, the generation module is specifically used to: generate N strategies for deploying K tasks on multiple cloud resources, and determine whether each strategy satisfies the physical constraints; if there are P strategies that do not satisfy the physical constraints, regenerate P strategies, and determine whether the regenerated strategies satisfy the physical constraints until N strategies that satisfy the physical constraints are obtained, where P is a positive integer less than or equal to N.

Optionally, the physical constraints include constraints on storage capacity and computing capacity; the determination module is specifically configured to: determine whether the storage capacity and computing capacity of the cloud resources in each policy satisfy the corresponding constraints.

Optionally, the execution module is specifically used to: determine whether the storage capacity of the cloud resources in each policy meets the corresponding constraints based on the storage space required by the task, the storage space of the cloud resource, and the decision-making situation of the task deployment in the cloud resource. condition.

Optionally, the execution module is specifically used to: based on the amount of calculation required by the task, the computing power of the cloud resource, and the task The decision-making situation of deploying cloud resources determines whether the computing capabilities of cloud resources in each strategy meet the corresponding constraints.

Optionally, the execution module is specifically configured to: perform at least one of the following: exchanging cloud resources for at least one task in every two strategies of the first group to obtain an exchanged strategy; The cloud resources of at least one task in the task are randomly replaced, and the replacement strategy is obtained.

Optionally, the execution module is also used to calculate the deployment cost of each strategy before dividing the N strategies that meet the physical constraints into the first group and the second group based on fitness; calculate the default cost of each strategy; according to The deployment cost and default cost determine the fitness of each policy.

Optionally, the execution module is specifically used to: calculate the deployment cost of each strategy based on the maximum execution time of the task, the unit time price of cloud resource pricing, and the decision-making situation of the target pricing strategy in the cloud resources selected by the task.

Optionally, the execution module is specifically used to calculate the violation cost of each policy based on the violation cost caused by the task's violation of timing constraints during operation and the decision-making situation of whether the task violates the violation.

Optionally, the execution module is also used to determine whether the task violates timing constraints based on the end time of the task, the start time of the task, and the target timing constraints corresponding to the task.

Embodiments of the present disclosure provide a device for business process deployment. First, N policies that satisfy physical constraints and are deployed on multiple cloud resources can be generated for a total of K tasks in at least one business process. The physical constraints include storage capabilities. and computing power constraints; then, repeat the following steps W times: divide the N strategies into 2 groups according to their fitness, and then preprocess the group of strategies with larger fitness values. After judging the preprocessing, we get Whether the new policy satisfies the physical constraints, and if it does not, continue to regenerate until all policies in the group satisfy the physical constraints, and then re-preprocess based on fitness grouping. Finally, after the Wth processing, the strategy with the smallest fitness value among the N strategies is determined as the deployment strategy for each task in the business process. Based on the above method, a low-cost deployment strategy that meets task requirements can be comprehensively determined based on the timing requirements of each task in the business process, the computing power, storage capacity, deployment cost, and default cost of cloud resources.

The business process deployment device 500 provided by the embodiment of the present disclosure can implement each process implemented by the method embodiments in Figures 1 to 4. To avoid duplication, they will not be described again here.

Optionally, as shown in Figure 6, this embodiment of the present disclosure also provides a business process deployment device 600, including a processor 601, a memory 602, and programs or instructions stored on the memory 602 and executable on the processor 601. , when this program or instruction is executed by the processor 601, it implements each process of the above-mentioned business process deployment method embodiment in a cloud computing environment, and can achieve the same technical effect. To avoid duplication, it will not be described again here.

It should be noted that the network entity or electronic device 700 shown in FIG. 7 is only an example, and should not bring any limitations to the functions and scope of use of the embodiments of the present disclosure.

As shown in Figure 7, the electronic device 700 includes a central processing unit (Central Processing Unit, CPU) 701, which can be loaded into the RAM (Read Only Memory) 702 according to the program stored in the ROM (Read Only Memory) 702 or from the storage part 708. Random Access Memory (Random Access Memory) 703 program to perform various appropriate actions and processing. In RAM 703, various programs and data required for system operation are also stored. CPU 701, ROM 702 and RAM 703 are connected to each other through bus 704. I/O (Input/Output, 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, etc.; an output section 707 including a CRT (Cathode Ray Tube, cathode ray tube), LCD (Liquid Crystal Display), etc., and speakers, etc. ; a storage part 708 including a hard disk, etc.; and a communication part 709 including a network interface card such as a LAN (Local Area Network, wireless network) card, a modem, etc. The communication section 709 performs communication processing via a network such as the Internet. Driver 710 is also connected to I/O interface 705 as needed. Removable media 711, such as magnetic disks, optical disks, magneto-optical disks, semiconductor memories, etc., are installed on the drive 710 as needed, so that a computer program read therefrom is installed into the storage portion 708 as needed.

In particular, according to embodiments of the present disclosure, the processes described below with reference to the flowcharts may be implemented as a computer software program. For example, embodiments of the present disclosure include a computer program product including a computer program product hosted on a computer-readable medium. A computer program containing program code for performing the method illustrated in the flowchart. In such embodiments, the computer program may be downloaded and installed from the network via communication portion 709 and/or installed from removable media 711 . When the computer program is executed by the central processing unit (CPU 701), various functions defined in the system of the present application are executed.

Embodiments of the present disclosure also provide a readable storage medium on which a program or instructions are stored. When the program or instructions are executed by a processor, each of the above method embodiments of business process deployment in a cloud computing environment is implemented. The process can achieve the same technical effect. To avoid repetition, it will not be described again here.

Wherein, the processor is the processor in the electronic device described in the above embodiment. The readable storage media includes computer-readable storage media, such as ROM, RAM, magnetic disks or optical disks.

An embodiment of the present disclosure further provides a chip. The chip includes a processor and a communication interface. The communication interface is coupled to the processor. The processor is used to run programs or instructions to implement the business process in the cloud computing environment. Each process of the deployment method embodiment can achieve the same technical effect. To avoid duplication, it will not be described again here.

It should be understood that the chip mentioned in the embodiments of the present disclosure may also be called a system-on-chip, a system-on-a-chip, a system-on-a-chip or a system-on-chip, etc.

Embodiments of the present disclosure provide a computer program product containing instructions that, when run on a computer, cause the computer to execute the steps of the method for deploying business processes in a cloud computing environment, and can achieve the same technical effect. To avoid repetition, we will not go into details here.

It should be noted that, in this document, the terms "comprising", "comprises" or any other variations thereof are intended to cover a non-exclusive inclusion, such that a process, method, article or device that includes a series of elements not only includes those elements, It also includes other elements not expressly listed or inherent in the process, method, article or apparatus. Without further limitation, an element defined by the statement "comprises a..." does not exclude the presence of additional identical elements in a process, method, article or apparatus that includes that element. In addition, it should be pointed out that the scope of the methods and apparatuses in the embodiments of the present disclosure is not limited to performing functions in the order shown or discussed, but may also include performing functions in a substantially simultaneous manner or in the reverse order according to the functions involved. Functions may be performed, for example, the methods described may be performed in an order different from that described, and various steps may be added, omitted, or combined. Additionally, features described with reference to certain examples may be combined in other examples.

Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus the necessary general hardware platform. Of course, it can also be implemented by hardware, but in many cases the former is better. implementation. Based on this understanding, the technical solution of the present disclosure can be embodied in the form of a software product in essence or that contributes to the existing technology. The computer software product is stored in a storage medium (such as ROM/RAM, disk, CD), including several instructions to cause a terminal (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in various embodiments of the present disclosure.

The embodiments of the present disclosure have been described above in conjunction with the accompanying drawings. However, the present disclosure is not limited to the above-mentioned specific implementations. The above-mentioned specific implementations are only illustrative and not restrictive. Those of ordinary skill in the art will Inspired by this disclosure, many forms can be made without departing from the purpose of this disclosure and the scope protected by the claims, all of which fall within the protection of this disclosure.

Claims

A method of business process deployment in a cloud computing environment, the method includes:

Initialization generates N strategies that satisfy physical constraints for deploying K tasks on multiple cloud resources. The K tasks are tasks in at least one business process. The physical constraints include constraints on storage capabilities and computing capabilities. , K and N are both integers greater than 1;

Repeat the following A1 to A4 W times:

A1: Divide N strategies that satisfy the physical constraints into a first group and a second group based on fitness. The fitness of the strategies of the first group is greater than the fitness of the strategies of the second group. The fitness indicates the strategy. The extent of deployment costs and default costs;

A2: Preprocess the cloud resources of each policy of the first group, obtain the preprocessed policy, and determine whether the preprocessed policy satisfies the physical constraints;

A3: If there are Q strategies that do not satisfy the physical constraints, regenerate Q strategies and determine whether the regenerated strategies satisfy the physical constraints until Q strategies that satisfy the physical constraints are obtained, Q is a positive integer;

A4: Determine the fitness of each strategy among the strategies of the second group, the Q strategies that satisfy the physical constraints, and the strategies that satisfy the physical constraints in the preprocessed strategies;

The strategy with the smallest median fitness among the N calculations for the Wth time is determined as the deployment strategy for the K tasks, where W is an integer greater than 1.
The method according to claim 1, wherein the initialization generates N strategies that satisfy physical constraints for deploying K tasks on multiple cloud resources, including:

Generate N strategies for deploying K tasks on multiple cloud resources, and determine whether each strategy satisfies physical constraints;

If there are P strategies that do not satisfy the physical constraints, regenerate P strategies and determine whether the regenerated strategies satisfy the physical constraints until N strategies that satisfy the physical constraints are obtained, and P is less than or equal to N positive integer.
The method of claim 1, wherein the physical constraints include storage capacity and computing capacity constraints;

Determining whether each policy satisfies physical constraints includes:

Determine whether the storage capabilities and computing capabilities of the cloud resources in each policy meet the corresponding constraints.
The method according to claim 3, wherein determining whether the storage capacity of the cloud resources in each policy satisfies corresponding constraints includes:

Based on the storage space required by the task, the storage space of the cloud resource, and the decision-making situation of deploying the task in the cloud resource, it is determined whether the storage capacity of the cloud resource in each policy meets the corresponding constraints.
The method according to claim 3, wherein determining whether the computing capabilities of the cloud resources in each policy satisfy corresponding constraints includes:

Based on the amount of computing required for the task, the computing power of the cloud resource, and the decision-making situation of deploying the task on the cloud resource, it is determined whether the computing power of the cloud resource in each strategy meets the corresponding constraints.
The method according to claim 1, wherein the preprocessing of cloud resources for each policy of the first group to obtain a preprocessed policy includes at least one of the following:

Exchange the cloud resources of at least one task in every two policies of the first group to obtain the exchanged policy;

The cloud resources of at least one task in each strategy of the first group are randomly replaced to obtain a replaced strategy.
The method according to claim 1, wherein before dividing the N strategies that satisfy the physical constraints into the first group and the second group based on fitness, the method further includes:

Calculate the deployment cost of each strategy;

Calculate the default cost of each strategy;

The fitness of each strategy is determined according to the deployment cost and the default cost.
The method according to claim 7, wherein calculating the deployment cost of each strategy includes:

Based on the maximum execution time of the task, the unit time price of cloud resource pricing, and the decision-making situation of the target pricing strategy in the cloud resources selected by the task, the deployment cost of each strategy is calculated.
The method according to claim 7, wherein the calculating the violation cost of each policy includes:

Based on the violation cost caused by the task's violation of timing constraints during operation and the decision-making situation of whether the task violates the violation, the violation cost of each policy is calculated.
A device for business process deployment, which includes: a generation module, an execution module and a determination module;

The generation module is used to generate N policies that satisfy physical constraints for deploying K tasks on multiple cloud resources. The K tasks are tasks in at least one business process. The physical constraints include storage capacity. and computing power constraints, K and N are both integers greater than 1;

The execution module is used to repeatedly execute the following A1 to A4 W times: A1: divide N strategies that satisfy the physical constraints into a first group and a second group based on fitness, and the first group The fitness of the policy is greater than the fitness of the second group of policies, and the fitness indicates the deployment cost and default cost of the policy; A2: Preprocess the cloud resources of each policy of the first group to obtain the preprocessed strategy, determine whether the preprocessed strategy satisfies the physical constraint; A3: If there are Q strategies that do not satisfy the physical constraint, regenerate Q strategies, and determine whether the regenerated strategy satisfies all the physical constraints. The physical constraints are obtained until Q strategies that satisfy the physical constraints are obtained, Q is a positive integer; A4: Determine the second group of strategies, Q strategies that satisfy the physical constraints, and the preprocessed strategies The fitness of each strategy in the strategy that satisfies the physical constraints;

The determination module is used to determine the strategy with the smallest median fitness value among the N calculated by the execution module for the Wth time as the deployment strategy for the K tasks; W is an integer greater than 1.