# Development and Utilization of College Chinese Curriculum Resources Based on Cloud Computing Resource Scheduling Algorithm

Authors: Yanan Cui
Journal: Mathematical Problems in Engineering
 (2022)

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## Abstract

In the current situation of university education in China, there are some problems that need to be solved urgently in college Chinese teaching in China, which seriously restrict the realization and implementation of college Chinese teaching objectives. The emergence and development of cloud computing technology can integrate different information media, and through the integration of traditional text, pictures, videos, and other resource media, it can be presented in a clearer and more vivid way. In this paper, a cloud computing resource scheduling model is established for the realization of the platform for the development and utilization of university Chinese cloud computing curriculum resources. The multi-QoS (Quality of Service) objective constraint problem is transformed into a single objective constraint solving problem. The improved GA (genetic algorithm) is used to solve the single objective constraint problem, and the square of the objective function is used as a fitness function, and a better resource allocation strategy is obtained. Experimental results show that the allocation time of the algorithm proposed in this paper is greatly reduced, and the QoS guarantee ability of cloud computing service providers is effectively improved. It has certain reference value for the further research of cloud computing technology in the development and utilization of college Chinese curriculum resources.


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## Body

## 1. Introduction

The development and utilization of Chinese curriculum resources is gradually attracting the attention of educational theorists and the front line of teaching. People increasingly realize that curriculum is the most important basis for organizing education and teaching activities, and any curriculum is based on certain curriculum resources [1], that is, the use of network information technology in college Chinese teaching, specifically, the use of multimedia equipment to enrich and enrich the teaching content in the form of pictures, videos, music, and words, in order to promote the formation of diversified teaching methods and visualized teaching content. In order to pursue the integrity of the undergraduate teaching process and improve the teaching quality, it is necessary to introduce the concept of research-based learning into college Chinese teaching, so as to stimulate students’ interest in literature, inspire their innovative consciousness, develop their inquiry ability, and make their inner world full and healthy. To achieve this goal, it is necessary to attach great importance to the development and utilization of relevant curriculum resources [2].Chinese classroom teaching is the main way to achieve the goal of Chinese education, and it is the central link to guide students’ learning and reflect teachers’ leading role and students’ subjectivity. Cloud computing, as a new technology and business development model, has gradually entered people’s field of vision. The emergence and development of cloud computing technology has a great influence on the process of network information industry and information infrastructure service [3, 4]. Literature [5] is configured according to the actual needs of individual users. In this case, after the virtual machine runs, no matter what the running status is, no adjustment will be made later, so this operation process is relatively simple. However, from the actual work effect, the adaptability to the change of dynamic workload of related applications is generally relatively low. In [6, 7] the research on the development and utilization of curriculum resources has been recognized by everyone and provided guidance for subsequent related research. Since then, people have made more and more extensive explorations on the development and utilization of curriculum resources. Generally speaking, Chinese scholars have made abundant theoretical research on curriculum resources and their development and utilization from different aspects and angles, but there are still many researchers who are doing deeper and newer research and exploration in this field. In [8] the survey results show that, in the actual development and utilization of Chinese curriculum resources, there is still a contradiction between the richness and diversity of Chinese curriculum resources and the low utilization rate of Chinese curriculum resources. To solve this contradiction, it is necessary to do more in-depth and detailed research on the development and utilization of Chinese curriculum resources.Cloud computing is a newly emerging new model for efficient use of resources to solve problems [9]. Cloud computing resource scheduling is essentially a complex combinatorial optimization problem, and swarm intelligence algorithm has achieved ideal results in solving complex function optimization problems. The main content of this paper is to take virtualization technology and virtualized resources as the core and make them play a greater role in the cloud computing platform through the design and improvement of the curriculum resource management platform. At the same time, it makes a detailed analysis of the optimized management of virtualized curriculum resources, the improvement of the utilization rate of curriculum resources, the reduction of the energy consumption of the network platform system, and the application of cloud computing in the development and utilization of college Chinese curriculum resources.


### 1.1. The Originality of This Paper

(1)
Pay attention to the development and utilization of college Chinese course resources, and combine the nature and characteristics of Chinese subject to explain the relevant principles and strategies of developing and utilizing Chinese curriculum resources, so as to provide sufficient theoretical premise and operational path for concrete practice.(2)
In the process of resource scheduling strategy of multiobjective optimization, a simple and effective method is proposed to select candidate target physical hosts according to the past demand behavior of virtual machines; that is, the past behavior characteristics of virtual machines are counted and used as the basis for resource allocation of virtual machines in the future.This paper is divided into five chapters, and the organizational structure is as follows:The first chapter expounds the research background and significance of this paper and analyzes the research status and shortcomings of the development and utilization of college Chinese curriculum resources and resource scheduling in cloud computing. The second chapter is related technology analysis, which summarizes the cloud computing technology and analyzes the related technologies of resource scheduling. The third chapter puts forward the resource scheduling strategy of multiobjective optimization and the realization research of the platform for the development and utilization of university Chinese cloud computing curriculum resources. In Chapter 4, the simulation experiment of resource scheduling strategy of multiobjective optimization is carried out, which proves that the algorithm can effectively weigh each subobjective. The fifth chapter summarizes the full text and looks forward to the future work.


## 1.1. The Originality of This Paper

(1)
Pay attention to the development and utilization of college Chinese course resources, and combine the nature and characteristics of Chinese subject to explain the relevant principles and strategies of developing and utilizing Chinese curriculum resources, so as to provide sufficient theoretical premise and operational path for concrete practice.(2)
In the process of resource scheduling strategy of multiobjective optimization, a simple and effective method is proposed to select candidate target physical hosts according to the past demand behavior of virtual machines; that is, the past behavior characteristics of virtual machines are counted and used as the basis for resource allocation of virtual machines in the future.This paper is divided into five chapters, and the organizational structure is as follows:The first chapter expounds the research background and significance of this paper and analyzes the research status and shortcomings of the development and utilization of college Chinese curriculum resources and resource scheduling in cloud computing. The second chapter is related technology analysis, which summarizes the cloud computing technology and analyzes the related technologies of resource scheduling. The third chapter puts forward the resource scheduling strategy of multiobjective optimization and the realization research of the platform for the development and utilization of university Chinese cloud computing curriculum resources. In Chapter 4, the simulation experiment of resource scheduling strategy of multiobjective optimization is carried out, which proves that the algorithm can effectively weigh each subobjective. The fifth chapter summarizes the full text and looks forward to the future work.


## 2. Related Work

Curriculum resources emphasize that curriculum resources have a very wide range of development and utilization space. As long as they can stimulate students’ learning motivation, help students understand teaching objectives, and play a role of feedback and encouragement, they should be paid attention to and developed and utilized in the process of planning curriculum and teaching activities. Literature [10] analyzes the source, nature, and form of curriculum resources in detail and puts forward the principles of development and utilization such as pertinence, individuality, and openness, which provides theoretical guidance for people to develop and utilize curriculum resources. Document [11] expounds the viewpoint that “the development and utilization of curriculum resources is directly related to the effectiveness of curriculum implementation” while document [12] analyzes the deficiencies in the development and utilization of curriculum resources and puts forward some solutions according to these deficiencies. Literature [13] holds that it is implemented jointly by teachers and students in schools, aiming at improving students’ listening, speaking, reading, writing, and other abilities and humanistic spirit, and is based on local resources. Literature [14] puts forward the development strategy of local curriculum resources, which mainly includes the following points: establishing multicultural curriculum concept; making a perfect curriculum policy; encouraging Chinese teachers to participate in curriculum development; forming a reasonable curriculum goal. All of the above are positive attempts made by Chinese educators and researchers to develop and utilize local Chinese curriculum resources. They strive to combine local resources with Chinese curriculum teaching to serve local Chinese education.In cloud computing infrastructure, virtual machine resource allocation is an important research topic. At present, there are a series of research results, such as a load balancing scheduling strategy based on GA (genetic algorithm) proposed in [15]. According to the historical data and current status of the system, the impact on the system after deploying the requested virtual machine resources is calculated, and the solution with the least impact is selected to avoid online migration. However, there is a problem of resource waste in the process of resource allocation. In document [16], it is proposed that, by transferring workload among virtual machines, the resource requirements of each virtual machine can be optimized, and the migration of virtual machines can be reduced after the task is executed, so as to avoid the degradation of system performance, but there is also the problem of resource waste. Literature [17] points out that CPU should be regarded as an important bottleneck parameter in server deployment besides bandwidth resources. Literature [18] puts forward that the server load should be taken into account, but only the bandwidth load of the server is considered, and the multiparameter server selection when memory becomes the bottleneck is not considered. Literature [19] defines cloud computing resources according to this feature of cloud computing, introduces the resource scheduling management of cloud data center, and expounds the scheduling strategy of cloud computing resources. ACA (ant colony algorithm) is also often used in the research of cloud computing resource scheduling methods. Literature [20] proposed an improved artificial firefly algorithm and applied it to the scheduling of allocated resources, which better solved the problems of balancing network load and extending network.As a matter of fact, many algorithms will consider many factors to ensure the users’ demands and optimize the performance and economic indexes of scheduling as much as possible. The existing cloud computing resource scheduling system does not support scalable resource optimization objectives. To solve this problem, the resource scheduling system based on objective management proposed in this paper supports user-defined resource optimization objectives.


## 3. Methodology

### 3.1. Principle Analysis of Development and Utilization of Chinese Curriculum Resources

The development of local Chinese curriculum resources cannot be arbitrary. It needs certain principles to guide and standardize. Based on the various types and basic characteristics of college Chinese curriculum resources, the author believes that the development and utilization of college Chinese curriculum resources should mainly follow the following principles.


#### 3.1.1. Principle of Subjectivity

The development of college Chinese curriculum resources must reflect students’ subjectivity according to their actual situation. The new curriculum emphasizes being student-oriented, and students are the main body of learning. Similarly, the development and utilization of college Chinese curriculum resources must be carried out according to students’ situation, and the appropriateness of curriculum resources to students’ age, hobbies, learning needs, existing knowledge, experience, and ability level should be sought. Through their own personal experience and participation, they can actively form learning results and achieve cognition, feeling, and understanding of Chinese courses. Teachers only play the role of guidance, organization, and coordination in this process.


#### 3.1.2. Language Principle

The principle of sex is put forward to distinguish the principle of “development and utilization of curriculum resources” put forward at the level of curriculum theory, reflect the characteristics of Chinese discipline, tap the charm of Chinese discipline itself, and make full use of the content and characteristics of Chinese discipline itself to select classroom teaching resources. In this kind of Chinese classroom, Chinese teachers become the players of music and pictures, the spectators of students’ performances, and even the supporting roles of teachers of other disciplines. Therefore, adhering to the principle of “Chinese” in the development and utilization of Chinese classroom teaching resources is of great significance to sticking to the position of Chinese teaching and highlighting the value of Chinese discipline.


#### 3.1.3. Principle of Openness

It is necessary to creatively develop and utilize the local resources that are beneficial to Chinese teaching, so as to serve the local Chinese education. The opening of channels means that the development and utilization of college Chinese curriculum resources should not be limited to one channel or way but should be developed and utilized in multiple ways or ways and should be comprehensively used as much as possible, specifically including the opening of development channels and the opening of utilization channels.


#### 3.1.4. Economic Principle

The development and utilization of Chinese classroom teaching resources should achieve the best classroom teaching effect with the least expenditure and energy as much as possible. When there is no ability to develop and utilize conditional resources, material resources should be exploited as much as possible to make up for resources and develop and utilize classroom teaching resources that save the most money or those that do not need too much money or those that can complete the task of development and utilization without too much energy from Chinese teachers, so as to achieve time economy.The above four principles are a reference for the experience of developing and utilizing Chinese classroom teaching resources in the past and also a summary of the author’s efforts to follow the objective laws of student development and Chinese classroom teaching. Developing Chinese classroom teaching resources is an important component of developing and utilizing Chinese curriculum resources, and it is a process of continuous exploration and development of Chinese teaching.


### 3.2. Design of Platform for Development and Utilization of University Chinese Cloud Computing Curriculum Resources

The service architecture of cloud computing has the following characteristics: cloud computing has powerful computing power and can provide safe and reliable data storage function, provide users with fast and convenient services, save costs for service organizations, and maximize economic benefits. Teaching resources refer to information resources that store a large amount of educational data and information, which are transmitted on the Internet by digital signals and create educational value for society. Through the centralized storage of data and information resources in digital form, a systematic and perfect teaching resource pool is constructed, and with the advantage of resources, it provides efficient management and use functions, thus improving the quality of teaching resource pool.As for the cloud service platform, what users touch is no longer the independent, generalized, and systematic network information system, but the integrated information system with good computing function and large-scale storage space, which can effectively improve the disadvantages and defects existing in the current network course construction process, such as the failure of virtual teaching equipment to play a good role and the unreasonable use of software resources [21].For the terminal of cloud server, there is a high-level team to promote the stable operation of this series of resources, so that teachers and students can use related applications and software conveniently by using network technology. While reducing the cost, it can also provide stable information service, which can be widely used in schools.In cloud computing, QoS (Quality of Service) refers to a set of parameters that can describe users’ characteristic requirements for services provided by their cloud computing providers. Different types of users need to use different cloud computing services, which requires cloud computing to be able to provide diversified services and meet different service requirements [22].The ability to guarantee QoS is an important element for users to choose cloud computing providers, and it is also an important condition for transactions between providers and users. It can not only distinguish cloud computing service providers with similar functions, but also measure the degree to which cloud computing service providers guarantee their services to users.Throughput refers to the number of times that cloud services can serve in a unit time. Use formula (1) to express(1)Performancethr=∑i=0tNm,itm,where Nm,i represents the number of requests from the m th service at the i th time, and tm is the total time period of the whole statistical service throughput.The robustness of cloud computing is a very important index, and evaluating and improving the robustness is very helpful to improve the service quality of cloud computing. Use formula (2) to express(2)Availabilityrob=TavaTtol.Among them,Tava is the normal running time of the whole cloud service cycle, and Ttol is the total time of cloud services used by users.The data management ability of cloud computing services is the guarantee of data security, and sometimes it is the actual experience of users using cloud computing services. Generally speaking, the value of data management ability can be defined as the average value of the service scored by the overall users, which is expressed by formula (3):(3)securitydata=∑i=1mDMim.DMi indicates the user’s rating of this cloud computing service, and m refers to the number of times this service has been rated.Extensibility is generally used to indicate the ability of resources to meet the growing needs of users and whether they can be easily modified according to the changes of users’ needs. The scalability of cloud services can be expressed by formula (4):(4)Expandability=∑i=1kRSik.k represents the number of times the user changes the scheduling of the service he uses in use. If the call is successful, the value of RS is 1; otherwise, it is 0.The simulated running environment of cloud management platform is based on the architecture, and the display layer, business layer, and data layer can all choose the corresponding system according to their own needs and system composition. Because the management of each layered system is relatively independent and simple, and each layer can support different types of databases, the system availability is very high. Combining with the software engineering design concept of the curriculum resource management platform studied in this paper, through the demand analysis and research of the curriculum resource management system, the system running architecture studied in this paper is put forward, as shown in Figure1.Figure 1
System operation architecture.As can be seen from the figure, the system is divided into three levels, namely, display level, business level, and data level, and each system level contains many client types. The design of this system fully takes into account the overall requirements of the curriculum resource management system and closely combines the relevant standards of curriculum resource construction and uses the three-tier system operation architecture to develop the system.On the one hand, it can give full play to the functions of various advanced technologies in different layers; on the other hand, it can also support different servers to realize cooperative operation; in addition, each structural layer has its own channel for system upgrade, expansion, and maintenance, which is conducive to the later maintenance of the curriculum resource management platform and ensures the safe and stable operation of the system.


### 3.3. Cloud Computing Resource Scheduling Strategy

People pay more and more attention to the research of cloud computing resource scheduling methods. In the cloud computing environment, computing tasks are distributed on its resource pool, and users can obtain computing resources as needed [23]. This feature determines that users often pay more attention to the economic cost of using these resources. Because the service mode provided by cloud computing is pay as a service. Users need to pay for using all kinds of resources in cloud computing, so the goal of resource scheduling in cloud computing needs to enable cloud providers and users to achieve the expected benefits.Scheduling algorithm refers to the resource scheduling algorithm determined according to the optimization goal of resource scheduling. Different scheduling algorithms will be used for different optimization objectives. Scheduling algorithms can be classified according to different scheduling strategies and objective functions. Traditional scheduling algorithms: traditional scheduling methods are mainly used to schedule resources, such as random scheduling algorithms. Heuristic scheduling algorithm: It mainly uses heuristic algorithms to schedule resources, such as greedy algorithm, GA, ACA, etc. Other scheduling algorithms: these are algorithms other than traditional scheduling algorithms and heuristic scheduling algorithms, such as some comprehensive scheduling algorithms: virtual machine dynamic scheduling.As a business model, cloud computing is always driven by business interests of cloud service providers, so if there is another method that can significantly increase the potential revenue and offset the power saved by shutting down the server while ensuring the QoS of users, it must be something worth doing by cloud service providers.The virtual machines are divided inton parts, and formula (5) indicates that the processing speed of the virtual machines is not less than the ratio of the number of the mi-th virtual machines to all the virtual machines.(5)pi=mivmNum.At a given moment, the functionU is defined to measure the violation rate of the service level agreement. Formula (6) indicates that when the virtual machine set V is deployed on the physical host h, the function Uh,V,t measures the dissatisfaction with the CPU resource requirements of the physical host h at time t.(6)Uh,V,t=∑l∈Vrit−ait∑i∈Vrit,where rit represents the amount of CPU resources requested by the virtual machine i, and ait represents the amount of CPU resources allocated by the physical host to the virtual machine i request. Its range value is in [0, 1].At a given moment, the definition functionL measures the waste of resources on the physical host when the virtual machine set is deployed on the physical host h. As shown in formula (7):(7)Lh,V,t=∑k≠zRkt−Rzt,where Rk represents the normalized residual resources of the resource, that is, the ratio of residual resources to total resources, and subscript Z represents the resource with the smallest normalized residual capacity among multidimensional resources. The residual resources wasted on the server are calculated as the sum of the smallest normalized residual resources and other residual resources, and its value range is [0, 1].The process of virtual machine resource initialization allocation algorithm is shown in Figure2.(1)
Firstly, according to the specified filtering method, all available physical host nodes are filtered to obtain a set of physical host nodes that meet the filtering conditions.(2)
Calculate the comprehensive trade-off value of all physical hosts aft filtering, and then arrange all physical host nodes aft filtering in ascending order according to the size of the comprehensive trade-off value.(3)
Select the physical host with the smallest comprehensive trade-off value to allocate virtual machine resources to users.Figure 2
Virtual machine resource initialization allocation process.A core technology of cloud computing is to use virtualization technology to map the available resources of physical hosts to the virtual machine layer to perform users’ tasks, so task scheduling in cloud environment is based on application layer and virtual resource layer.“Virtualization is used to shield the physical host in the form of virtual machine, so the resource search process is encapsulated as the search of virtual machine.” Heuristic algorithm provides a new direction for solving this kind of problems and achieves good results. Therefore, this paper mainly improves it based on GA and applies it to resource scheduling in cloud environment, in order to find the best implementation scheme and make the load balance.The square of the objective function is selected as the fitness function, in which four indexes correspond to the weight coefficient in the objective function, which isw1,…,w4, respectively. And formula (8) needs to be satisfied.(8)∑k=0twk=1.In this paper, roulette is used for selection, and the population size isS. For a chromosome individual with fitness fi, the selection probability is pi:(9)pi=fi∑i=1Sfi.From the above formula, we can know that the probabilitypi represents the proportion of the fitness of individual i in the sum of all individual fitness in the population. The greater the value of individual fitness, the greater the chance of being selected.In this paper, an adaptive method is adopted to prevent the crossover probability from being too large or too small. The adaptive adjustment formula of crossover probability is as follows:(10)pc=k1fmax−ffmax−favg,f≥favgk2,f<favg.Among them,favg refers to the average fitness value of the population, fmax refers to the maximum fitness value of the population, and f refers to the fitness value of the side with relatively larger cross fitness.The algorithm flow is shown in Figure3.Figure 3
Improved algorithm flow.Through the action of the above three operators, GA has both global and local search capabilities, so that the problem can seek the optimal solution or approach the optimal solution.


## 3.1. Principle Analysis of Development and Utilization of Chinese Curriculum Resources

The development of local Chinese curriculum resources cannot be arbitrary. It needs certain principles to guide and standardize. Based on the various types and basic characteristics of college Chinese curriculum resources, the author believes that the development and utilization of college Chinese curriculum resources should mainly follow the following principles.


### 3.1.1. Principle of Subjectivity

The development of college Chinese curriculum resources must reflect students’ subjectivity according to their actual situation. The new curriculum emphasizes being student-oriented, and students are the main body of learning. Similarly, the development and utilization of college Chinese curriculum resources must be carried out according to students’ situation, and the appropriateness of curriculum resources to students’ age, hobbies, learning needs, existing knowledge, experience, and ability level should be sought. Through their own personal experience and participation, they can actively form learning results and achieve cognition, feeling, and understanding of Chinese courses. Teachers only play the role of guidance, organization, and coordination in this process.


### 3.1.2. Language Principle

The principle of sex is put forward to distinguish the principle of “development and utilization of curriculum resources” put forward at the level of curriculum theory, reflect the characteristics of Chinese discipline, tap the charm of Chinese discipline itself, and make full use of the content and characteristics of Chinese discipline itself to select classroom teaching resources. In this kind of Chinese classroom, Chinese teachers become the players of music and pictures, the spectators of students’ performances, and even the supporting roles of teachers of other disciplines. Therefore, adhering to the principle of “Chinese” in the development and utilization of Chinese classroom teaching resources is of great significance to sticking to the position of Chinese teaching and highlighting the value of Chinese discipline.


### 3.1.3. Principle of Openness

It is necessary to creatively develop and utilize the local resources that are beneficial to Chinese teaching, so as to serve the local Chinese education. The opening of channels means that the development and utilization of college Chinese curriculum resources should not be limited to one channel or way but should be developed and utilized in multiple ways or ways and should be comprehensively used as much as possible, specifically including the opening of development channels and the opening of utilization channels.


### 3.1.4. Economic Principle

The development and utilization of Chinese classroom teaching resources should achieve the best classroom teaching effect with the least expenditure and energy as much as possible. When there is no ability to develop and utilize conditional resources, material resources should be exploited as much as possible to make up for resources and develop and utilize classroom teaching resources that save the most money or those that do not need too much money or those that can complete the task of development and utilization without too much energy from Chinese teachers, so as to achieve time economy.The above four principles are a reference for the experience of developing and utilizing Chinese classroom teaching resources in the past and also a summary of the author’s efforts to follow the objective laws of student development and Chinese classroom teaching. Developing Chinese classroom teaching resources is an important component of developing and utilizing Chinese curriculum resources, and it is a process of continuous exploration and development of Chinese teaching.


## 3.1.1. Principle of Subjectivity

The development of college Chinese curriculum resources must reflect students’ subjectivity according to their actual situation. The new curriculum emphasizes being student-oriented, and students are the main body of learning. Similarly, the development and utilization of college Chinese curriculum resources must be carried out according to students’ situation, and the appropriateness of curriculum resources to students’ age, hobbies, learning needs, existing knowledge, experience, and ability level should be sought. Through their own personal experience and participation, they can actively form learning results and achieve cognition, feeling, and understanding of Chinese courses. Teachers only play the role of guidance, organization, and coordination in this process.


## 3.1.2. Language Principle

The principle of sex is put forward to distinguish the principle of “development and utilization of curriculum resources” put forward at the level of curriculum theory, reflect the characteristics of Chinese discipline, tap the charm of Chinese discipline itself, and make full use of the content and characteristics of Chinese discipline itself to select classroom teaching resources. In this kind of Chinese classroom, Chinese teachers become the players of music and pictures, the spectators of students’ performances, and even the supporting roles of teachers of other disciplines. Therefore, adhering to the principle of “Chinese” in the development and utilization of Chinese classroom teaching resources is of great significance to sticking to the position of Chinese teaching and highlighting the value of Chinese discipline.


## 3.1.3. Principle of Openness

It is necessary to creatively develop and utilize the local resources that are beneficial to Chinese teaching, so as to serve the local Chinese education. The opening of channels means that the development and utilization of college Chinese curriculum resources should not be limited to one channel or way but should be developed and utilized in multiple ways or ways and should be comprehensively used as much as possible, specifically including the opening of development channels and the opening of utilization channels.


## 3.1.4. Economic Principle

The development and utilization of Chinese classroom teaching resources should achieve the best classroom teaching effect with the least expenditure and energy as much as possible. When there is no ability to develop and utilize conditional resources, material resources should be exploited as much as possible to make up for resources and develop and utilize classroom teaching resources that save the most money or those that do not need too much money or those that can complete the task of development and utilization without too much energy from Chinese teachers, so as to achieve time economy.The above four principles are a reference for the experience of developing and utilizing Chinese classroom teaching resources in the past and also a summary of the author’s efforts to follow the objective laws of student development and Chinese classroom teaching. Developing Chinese classroom teaching resources is an important component of developing and utilizing Chinese curriculum resources, and it is a process of continuous exploration and development of Chinese teaching.


## 3.2. Design of Platform for Development and Utilization of University Chinese Cloud Computing Curriculum Resources

The service architecture of cloud computing has the following characteristics: cloud computing has powerful computing power and can provide safe and reliable data storage function, provide users with fast and convenient services, save costs for service organizations, and maximize economic benefits. Teaching resources refer to information resources that store a large amount of educational data and information, which are transmitted on the Internet by digital signals and create educational value for society. Through the centralized storage of data and information resources in digital form, a systematic and perfect teaching resource pool is constructed, and with the advantage of resources, it provides efficient management and use functions, thus improving the quality of teaching resource pool.As for the cloud service platform, what users touch is no longer the independent, generalized, and systematic network information system, but the integrated information system with good computing function and large-scale storage space, which can effectively improve the disadvantages and defects existing in the current network course construction process, such as the failure of virtual teaching equipment to play a good role and the unreasonable use of software resources [21].For the terminal of cloud server, there is a high-level team to promote the stable operation of this series of resources, so that teachers and students can use related applications and software conveniently by using network technology. While reducing the cost, it can also provide stable information service, which can be widely used in schools.In cloud computing, QoS (Quality of Service) refers to a set of parameters that can describe users’ characteristic requirements for services provided by their cloud computing providers. Different types of users need to use different cloud computing services, which requires cloud computing to be able to provide diversified services and meet different service requirements [22].The ability to guarantee QoS is an important element for users to choose cloud computing providers, and it is also an important condition for transactions between providers and users. It can not only distinguish cloud computing service providers with similar functions, but also measure the degree to which cloud computing service providers guarantee their services to users.Throughput refers to the number of times that cloud services can serve in a unit time. Use formula (1) to express(1)Performancethr=∑i=0tNm,itm,where Nm,i represents the number of requests from the m th service at the i th time, and tm is the total time period of the whole statistical service throughput.The robustness of cloud computing is a very important index, and evaluating and improving the robustness is very helpful to improve the service quality of cloud computing. Use formula (2) to express(2)Availabilityrob=TavaTtol.Among them,Tava is the normal running time of the whole cloud service cycle, and Ttol is the total time of cloud services used by users.The data management ability of cloud computing services is the guarantee of data security, and sometimes it is the actual experience of users using cloud computing services. Generally speaking, the value of data management ability can be defined as the average value of the service scored by the overall users, which is expressed by formula (3):(3)securitydata=∑i=1mDMim.DMi indicates the user’s rating of this cloud computing service, and m refers to the number of times this service has been rated.Extensibility is generally used to indicate the ability of resources to meet the growing needs of users and whether they can be easily modified according to the changes of users’ needs. The scalability of cloud services can be expressed by formula (4):(4)Expandability=∑i=1kRSik.k represents the number of times the user changes the scheduling of the service he uses in use. If the call is successful, the value of RS is 1; otherwise, it is 0.The simulated running environment of cloud management platform is based on the architecture, and the display layer, business layer, and data layer can all choose the corresponding system according to their own needs and system composition. Because the management of each layered system is relatively independent and simple, and each layer can support different types of databases, the system availability is very high. Combining with the software engineering design concept of the curriculum resource management platform studied in this paper, through the demand analysis and research of the curriculum resource management system, the system running architecture studied in this paper is put forward, as shown in Figure1.Figure 1
System operation architecture.As can be seen from the figure, the system is divided into three levels, namely, display level, business level, and data level, and each system level contains many client types. The design of this system fully takes into account the overall requirements of the curriculum resource management system and closely combines the relevant standards of curriculum resource construction and uses the three-tier system operation architecture to develop the system.On the one hand, it can give full play to the functions of various advanced technologies in different layers; on the other hand, it can also support different servers to realize cooperative operation; in addition, each structural layer has its own channel for system upgrade, expansion, and maintenance, which is conducive to the later maintenance of the curriculum resource management platform and ensures the safe and stable operation of the system.


## 3.3. Cloud Computing Resource Scheduling Strategy

People pay more and more attention to the research of cloud computing resource scheduling methods. In the cloud computing environment, computing tasks are distributed on its resource pool, and users can obtain computing resources as needed [23]. This feature determines that users often pay more attention to the economic cost of using these resources. Because the service mode provided by cloud computing is pay as a service. Users need to pay for using all kinds of resources in cloud computing, so the goal of resource scheduling in cloud computing needs to enable cloud providers and users to achieve the expected benefits.Scheduling algorithm refers to the resource scheduling algorithm determined according to the optimization goal of resource scheduling. Different scheduling algorithms will be used for different optimization objectives. Scheduling algorithms can be classified according to different scheduling strategies and objective functions. Traditional scheduling algorithms: traditional scheduling methods are mainly used to schedule resources, such as random scheduling algorithms. Heuristic scheduling algorithm: It mainly uses heuristic algorithms to schedule resources, such as greedy algorithm, GA, ACA, etc. Other scheduling algorithms: these are algorithms other than traditional scheduling algorithms and heuristic scheduling algorithms, such as some comprehensive scheduling algorithms: virtual machine dynamic scheduling.As a business model, cloud computing is always driven by business interests of cloud service providers, so if there is another method that can significantly increase the potential revenue and offset the power saved by shutting down the server while ensuring the QoS of users, it must be something worth doing by cloud service providers.The virtual machines are divided inton parts, and formula (5) indicates that the processing speed of the virtual machines is not less than the ratio of the number of the mi-th virtual machines to all the virtual machines.(5)pi=mivmNum.At a given moment, the functionU is defined to measure the violation rate of the service level agreement. Formula (6) indicates that when the virtual machine set V is deployed on the physical host h, the function Uh,V,t measures the dissatisfaction with the CPU resource requirements of the physical host h at time t.(6)Uh,V,t=∑l∈Vrit−ait∑i∈Vrit,where rit represents the amount of CPU resources requested by the virtual machine i, and ait represents the amount of CPU resources allocated by the physical host to the virtual machine i request. Its range value is in [0, 1].At a given moment, the definition functionL measures the waste of resources on the physical host when the virtual machine set is deployed on the physical host h. As shown in formula (7):(7)Lh,V,t=∑k≠zRkt−Rzt,where Rk represents the normalized residual resources of the resource, that is, the ratio of residual resources to total resources, and subscript Z represents the resource with the smallest normalized residual capacity among multidimensional resources. The residual resources wasted on the server are calculated as the sum of the smallest normalized residual resources and other residual resources, and its value range is [0, 1].The process of virtual machine resource initialization allocation algorithm is shown in Figure2.(1)
Firstly, according to the specified filtering method, all available physical host nodes are filtered to obtain a set of physical host nodes that meet the filtering conditions.(2)
Calculate the comprehensive trade-off value of all physical hosts aft filtering, and then arrange all physical host nodes aft filtering in ascending order according to the size of the comprehensive trade-off value.(3)
Select the physical host with the smallest comprehensive trade-off value to allocate virtual machine resources to users.Figure 2
Virtual machine resource initialization allocation process.A core technology of cloud computing is to use virtualization technology to map the available resources of physical hosts to the virtual machine layer to perform users’ tasks, so task scheduling in cloud environment is based on application layer and virtual resource layer.“Virtualization is used to shield the physical host in the form of virtual machine, so the resource search process is encapsulated as the search of virtual machine.” Heuristic algorithm provides a new direction for solving this kind of problems and achieves good results. Therefore, this paper mainly improves it based on GA and applies it to resource scheduling in cloud environment, in order to find the best implementation scheme and make the load balance.The square of the objective function is selected as the fitness function, in which four indexes correspond to the weight coefficient in the objective function, which isw1,…,w4, respectively. And formula (8) needs to be satisfied.(8)∑k=0twk=1.In this paper, roulette is used for selection, and the population size isS. For a chromosome individual with fitness fi, the selection probability is pi:(9)pi=fi∑i=1Sfi.From the above formula, we can know that the probabilitypi represents the proportion of the fitness of individual i in the sum of all individual fitness in the population. The greater the value of individual fitness, the greater the chance of being selected.In this paper, an adaptive method is adopted to prevent the crossover probability from being too large or too small. The adaptive adjustment formula of crossover probability is as follows:(10)pc=k1fmax−ffmax−favg,f≥favgk2,f<favg.Among them,favg refers to the average fitness value of the population, fmax refers to the maximum fitness value of the population, and f refers to the fitness value of the side with relatively larger cross fitness.The algorithm flow is shown in Figure3.Figure 3
Improved algorithm flow.Through the action of the above three operators, GA has both global and local search capabilities, so that the problem can seek the optimal solution or approach the optimal solution.


## 4. Experiment and Results

### 4.1. Prepare for the Experiment

The simulation platform used in this paper is Core CPU 3.0 GHz, 2 GB DDＲ 3, Windows XP operating system, and the simulation experiment is carried out with CloudSim simulation software. In order to run the algorithm and check it. Specific parameters in the program need to be set. No matter how large the resources are, the operation process and principle are the same.The system test includes a complete set of functional processes, such as the call and response of the resource organization interface, the collection of resource dynamic information, task submission, resource matching and scheduling, task distribution and execution and result return, resource information release, information interaction and information display with the operation and maintenance support layer, etc.Number of tasks, weight coefficient, number of resource nodes, and the size of the population are the key factors which influence the system. However, data such as resource node attributes and tasks are randomly generated, and the iteration number parameters of GA are set in the algorithm program. Table1 shows the specific attribute values of seven randomly generated tasks.Table 1
Task attribute value.
IDRq-cpu (MIPS)Rq-men (GB)Rq-comm (MB/S)Rq-size (MB)040.6325130.1221230.5523350.5518410.2116520.1612630.2824


### 4.2. Experimental Analysis

In order to describe the effect of the algorithm more clearly, this section gives an example to explain the operation process of the algorithm. First, suppose there is a user task:T0,⋯,T4. Calculate the corresponding estimated execution completion time by two algorithms, and Figure 4 is a comparison chart of the execution time of a single task.Figure 4
Comparison chart of single task execution time.As can be seen from Figure4 above, in the total task execution span time, using GA can greatly shorten the resource scheduling time, which is much more advantageous than polling algorithm. Moreover, from the point of view of the execution time of a single task, the execution time of T0,T3 has been shortened by more than one time, while the execution time of T1,T2,T4 has little growth. Therefore, compared with polling algorithm, using GA can achieve better time load balance among tasks.Then, this research algorithm is compared with traditional GA and ACA. Under the condition of ensuring the normal completion of user tasks, the lower the cost that users want to use, the better. Analyze this from the index of cost, as shown in Figure5.Figure 5
Algorithm cost comparison.With the increasing number of tasks, the cost of this algorithm and the other two algorithms is gradually increasing, which indicates that, with the increase of tasks, the cost is increasing. But the cost of this research algorithm is lower than that of traditional GA algorithm.The advantages and disadvantages of the objective function affect the effectiveness of the algorithm. Analyze this from the objective function value, because the fitness function adopted is the square of the objective function. The larger the fitness function value, the greater the probability of being selected. Therefore, the algorithm with the larger objective function value has better relative performance, as shown in Figure6.Figure 6
Comparison of objective function values.With the increasing number of tasks, the objective function values of this research algorithm and the other two algorithms are gradually increasing. Compared with the traditional GA algorithm, the objective function values of this research algorithm are always higher, which shows that this algorithm can achieve better performance and lower operation cost in data.In this paper, trust is regarded as one of the factors that influence decision-making. Users give dynamic feedback on the trust of resources and then run the subset tree function Subset TreeSched (pTrTree) to get Table2.Table 2
Decision-making factors obtained when trust degrees are different.
The state of trustTrust degreeCorresponding decision factorOriginal value0.50.0186Increase0.60.0213Decrease0.40.0158By introducing the concept of operation mode, the algorithm combines the trust degree with the economic principle, which strengthens the role of users and embodies the user-centered scheduling strategy in algorithm design. The algorithm can reasonably schedule resources, find the optimal solution to meet users’ needs, and maximize users’ personal utility. The advantages of setting the weight values hierarchically are also obvious. This preprocessing method to avoid invalid search can improve the algorithm efficiency and reduce the time complexity.Figure7 shows that the 235 nodes evenly distribute the 24 resource information collection tasks of the 230 nodes to the other two management nodes. It shows that the test distributed algorithm has completed the migration of resource monitoring tasks.Figure 7
Comparison of system resource monitoring performance.Obviously, it takes less time for distributed resource management system to complete dynamic information collection for all resources than for single-node system. It can be predicted that, with the increase of resources, this ratio will show a decreasing trend, because when there are more resources, the bottleneck of database connection will be encountered.Multiobjective optimization algorithm effectively solves the problem of conflicts among multiple objectives. In order to evaluate the performance of the objective of improving resource utilization under the system, four experiments were done as in the scenario of load balancing objective. The four experimental modes are no-load generation mode, low-load generation mode, high-load generation mode, and migration module prohibition mode. The experimental results are shown in Figure8.Figure 8
Load imbalance and migration times under different modes.It can be seen that, firstly, the higher the load generation, the higher the resource utilization rate. As a result, it is expected that the higher the resource load generation, the more the resources allocated by the system and the higher the resource utilization. Secondly, under the condition of the same high-load generation, the resource utilization rate of the forbidden mode of virtual machine migration function is low, which is also expected, because the system optimizes the resource distribution more reasonably through virtual machine migration, and the less the resource waste, the higher the resource utilization rate.Therefore, the resource scheduling system based on objective management supports self-customized scheduling strategies to improve resource utilization and achieves the resource optimization goal of improving resource utilization.


## 4.1. Prepare for the Experiment

The simulation platform used in this paper is Core CPU 3.0 GHz, 2 GB DDＲ 3, Windows XP operating system, and the simulation experiment is carried out with CloudSim simulation software. In order to run the algorithm and check it. Specific parameters in the program need to be set. No matter how large the resources are, the operation process and principle are the same.The system test includes a complete set of functional processes, such as the call and response of the resource organization interface, the collection of resource dynamic information, task submission, resource matching and scheduling, task distribution and execution and result return, resource information release, information interaction and information display with the operation and maintenance support layer, etc.Number of tasks, weight coefficient, number of resource nodes, and the size of the population are the key factors which influence the system. However, data such as resource node attributes and tasks are randomly generated, and the iteration number parameters of GA are set in the algorithm program. Table1 shows the specific attribute values of seven randomly generated tasks.Table 1
Task attribute value.
IDRq-cpu (MIPS)Rq-men (GB)Rq-comm (MB/S)Rq-size (MB)040.6325130.1221230.5523350.5518410.2116520.1612630.2824


## 4.2. Experimental Analysis

In order to describe the effect of the algorithm more clearly, this section gives an example to explain the operation process of the algorithm. First, suppose there is a user task:T0,⋯,T4. Calculate the corresponding estimated execution completion time by two algorithms, and Figure 4 is a comparison chart of the execution time of a single task.Figure 4
Comparison chart of single task execution time.As can be seen from Figure4 above, in the total task execution span time, using GA can greatly shorten the resource scheduling time, which is much more advantageous than polling algorithm. Moreover, from the point of view of the execution time of a single task, the execution time of T0,T3 has been shortened by more than one time, while the execution time of T1,T2,T4 has little growth. Therefore, compared with polling algorithm, using GA can achieve better time load balance among tasks.Then, this research algorithm is compared with traditional GA and ACA. Under the condition of ensuring the normal completion of user tasks, the lower the cost that users want to use, the better. Analyze this from the index of cost, as shown in Figure5.Figure 5
Algorithm cost comparison.With the increasing number of tasks, the cost of this algorithm and the other two algorithms is gradually increasing, which indicates that, with the increase of tasks, the cost is increasing. But the cost of this research algorithm is lower than that of traditional GA algorithm.The advantages and disadvantages of the objective function affect the effectiveness of the algorithm. Analyze this from the objective function value, because the fitness function adopted is the square of the objective function. The larger the fitness function value, the greater the probability of being selected. Therefore, the algorithm with the larger objective function value has better relative performance, as shown in Figure6.Figure 6
Comparison of objective function values.With the increasing number of tasks, the objective function values of this research algorithm and the other two algorithms are gradually increasing. Compared with the traditional GA algorithm, the objective function values of this research algorithm are always higher, which shows that this algorithm can achieve better performance and lower operation cost in data.In this paper, trust is regarded as one of the factors that influence decision-making. Users give dynamic feedback on the trust of resources and then run the subset tree function Subset TreeSched (pTrTree) to get Table2.Table 2
Decision-making factors obtained when trust degrees are different.
The state of trustTrust degreeCorresponding decision factorOriginal value0.50.0186Increase0.60.0213Decrease0.40.0158By introducing the concept of operation mode, the algorithm combines the trust degree with the economic principle, which strengthens the role of users and embodies the user-centered scheduling strategy in algorithm design. The algorithm can reasonably schedule resources, find the optimal solution to meet users’ needs, and maximize users’ personal utility. The advantages of setting the weight values hierarchically are also obvious. This preprocessing method to avoid invalid search can improve the algorithm efficiency and reduce the time complexity.Figure7 shows that the 235 nodes evenly distribute the 24 resource information collection tasks of the 230 nodes to the other two management nodes. It shows that the test distributed algorithm has completed the migration of resource monitoring tasks.Figure 7
Comparison of system resource monitoring performance.Obviously, it takes less time for distributed resource management system to complete dynamic information collection for all resources than for single-node system. It can be predicted that, with the increase of resources, this ratio will show a decreasing trend, because when there are more resources, the bottleneck of database connection will be encountered.Multiobjective optimization algorithm effectively solves the problem of conflicts among multiple objectives. In order to evaluate the performance of the objective of improving resource utilization under the system, four experiments were done as in the scenario of load balancing objective. The four experimental modes are no-load generation mode, low-load generation mode, high-load generation mode, and migration module prohibition mode. The experimental results are shown in Figure8.Figure 8
Load imbalance and migration times under different modes.It can be seen that, firstly, the higher the load generation, the higher the resource utilization rate. As a result, it is expected that the higher the resource load generation, the more the resources allocated by the system and the higher the resource utilization. Secondly, under the condition of the same high-load generation, the resource utilization rate of the forbidden mode of virtual machine migration function is low, which is also expected, because the system optimizes the resource distribution more reasonably through virtual machine migration, and the less the resource waste, the higher the resource utilization rate.Therefore, the resource scheduling system based on objective management supports self-customized scheduling strategies to improve resource utilization and achieves the resource optimization goal of improving resource utilization.


## 5. Conclusion

College Chinese teaching not only is an important part of the education system, but also has a great influence on students’ future development. It is necessary to establish a screening mechanism for the utilization and development of college students’ activity resources. Only those resources with educational value and curriculum significance are necessary for development and utilization. This paper adopts the idea of multidimensional QoS, takes users as the center, and combines the improved GA to deal with the cloud resource scheduling problem. An adaptive method is introduced, which can effectively control the rationality of crossover probability and mutation probability, prevent the structure of excellent individuals from being destroyed or affecting the generation speed of individuals, and improve the effectiveness and robustness of the algorithm. The design scheme of the platform for the development and utilization of university Chinese cloud computing curriculum resources solves the problems of scattered management of curriculum resources, low resource utilization rate, and high construction cost.In the experiment of resource scheduling system based on management and objectives, only a few servers are used, and the next step is to verify the effectiveness of resource scheduling system based on management by objectives in large-scale server clusters.



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*Source: 3648678-2022-03-30.xml*