# Construction of Water Conservancy and Energy Engineering Structure Platform Based on Cloud Computing

Authors: Xiaoqi Yu; Jing Zhai; Jingui Ye
Journal: Wireless Communications and Mobile Computing
 (2022)

---

## Abstract

In view of the current water conservancy engineering system in solving the problem of water conservancy spatial information sharing and repeated investment in the construction of GIS systems by various water conservancy business departments, the research on the construction of water conservancy engineering structure platform based on cloud computing is proposed. We discussed the key technologies to realize the platform: pooled services, elastic cloud services, and spatial information services. The aggregation pooling service mainly uses the pooling mechanism and improves the responsiveness of platform services by creating object instances in advance. Based on the Cloud API provided by the cloud computing platform (such as creating a virtual machine, starting a virtual machine at runtime, and obtaining the real-time status of a virtual machine) and the REST Admin API provided by the water conservancy basic service module, using cloud computing technology ideas, we designed and implemented elastic cloud service technology. Enable the water conservancy spatial information service platform to be deployed in the cloud computing environment, support cloud architecture, and provide flexible computing capabilities, because the three types of services have different requirements for CPU and network utilization, the weight coefficient can be assigned to them. The CPU slice map service is 0.1, the spatial query service is 0.3, and the spatial analysis processing service is 0.6. The network bandwidth slice map service is 0.5, the spatial query service is 0.3, and the spatial analysis processing service is 0.2. Reduce the waste of infrastructure resources; meet the concurrent access needs of a large number of users. Spatial information service aggregation is one of the core service capabilities of the platform; it can effectively aggregate multisource spatial information, generate new services, and provide new information.


---

## Body

## 1. Introduction

The cloud computing platform is dynamic and easy to expand, and the resources are obtained through the network through virtualization technology; users do not need to have professional knowledge; there is no need to participate in construction and management [1]. Different from other application platforms, cloud platforms are data-centric; it has its own characteristics in data storage and management, programming mode, and many other aspects. In the field of infrastructure construction in China, there has been a lack of effective system management and comprehensive supervision during the construction of water conservancy projects [2]. The water conservancy engineering industry has its own particularity and complexity, usually involving a wide area; many majors have strong timeliness and strong decision-making interaction. Water conservancy informatization involves a wide range of content; the construction project is very professional and technical. Therefore, if cloud computing technology can be used to build an efficient and convenient water conservancy project information management system, that will make a qualitative leap in the management of water conservancy projects. The establishment of the system will to a large extent solve the difficult integration of basic resources in the current water conservancy informatization field; the legacy system is difficult to reuse; the business system is difficult to coordinate and other issues; at the same time, it can effectively integrate the existing management information resources, utilize existing infrastructure, fully improve the speed of information feedback and improve the efficiency of decision-making, and standardize business behavior to provide guarantee for the steady and rapid progress of water conservancy projects [3]. The application practice of cloud platform system built by cloud computing technology in many industry fields in recent years shows that such systems are generally highly recognized and welcomed by users [4]. Therefore, it is feasible to create an automated engineering project information management system based on a cloud platform. From a methodological point of view, the establishment of the system, it not only expands the breadth of cloud computing research and application in the field of water conservancy engineering construction, it also makes the development of this technology in the field of water conservancy engineering construction have far-reaching inspiring significance, and it also has a certain practical value [5]. In response to this research question, Loh and others believe that cloud computing is driven by economies of scale. Provide a set of abstract, virtualized, dynamically scalable, a large-scale distributed computing aggregate of manageable computing resource capabilities, storage capabilities, platforms, and services [6]. Han and others believe that cloud computing systems mainly store information permanently on servers in the cloud. When using information, it is only cached on the client side. Clients can be desktops, notebooks, handheld devices, etc. [7]. From a market-oriented perspective, Wu and others believe that cloud computing is a parallel set of internally interconnected virtual machines, and the distributed computing system can dynamically provide computing resources according to the service level agreement negotiated between the service provider and the customer [8]. In order to get better platform access performance, for the design of the data buffer and database, a more in-depth optimization design is needed. At the same time, information security is also a top priority in the Internet era; security mechanisms and data encryption are also key issues that need to be resolved. Finally, with the continuous reform and progress of the water conservancy industry system, the system must also be able to be adjusted and improved in time. On the basis of current research, a research on the construction of a cloud computing-based hydraulic engineering structure platform is proposed [9]. We discussed the key technologies to realize the platform: pooled service, elastic cloud service, and spatial information service. Spatial information service aggregation is one of the core service capabilities of the platform; it can effectively aggregate multisource spatial information, generate new services, and provide new information.


## 2. Method

### 2.1. Pooling Service

The service object refers to the coarse-grained component object that provides the service; it is mainly based on basic component design, in order to provide a coarse-grained interface pooling service object to create multiple instances of the service object in advance and put it into the “object pool,” waiting for user requests. When a user request arrives, the system first accesses the “object pool” of the service, if there is a service instance processing the request in the pool, this instance is returned from the pool, and its method is called by the requester to obtain the processing result. When the request is processed, the caller will not destroy this instance; instead, it will return the instance to the “object pool,” in order to process the next user request: if when the request arrives, it happens that there is no service instance available in the “object pool”; the request thread will be blocked until there is an idle instance or the timeout ends [10].Figure1 is the design class diagram of the map service MapService and the pyramid slice service TiledMapService; they all inherit the AbstractService abstract service class; AbstractService uses generic classes to represent service instance components associated with the service; for example, the component associated with MapService is MapServerInstance, and the component associated with TiledMapService is TiledMapServerInstance; other services are similar; for example, SAService is associated with SAServerInstance, so I will not repeat them here.Figure 1
Map service and slice service design.AbstactService is the base class of all service classes, it defines Start and Stop methods, indicates to start and stop a service. In its implementation, it mainly provides the operation of “Object Pool” (0ObjectPool); for example: Start method will create an object pool and allocate resources; Stop method will destroy the pool and release resources [11].


### 2.2. Elastic Cloud Service

#### 2.2.1. Overview of Cloud Computing

Cloud computing is a computing model based on the Internet, by distributed computing (DistributedComputing). Grid computing (GridComputing), parallel computing (ParallelComputing), and utility computing (UtilityComputing) are developed; it is an emerging business computing model and innovative ideas for resource integration. Through this calculation model, shared software and hardware resources are provided to computers and other devices in an on-demand and easily expandable manner through a “resource pool”; the whole operation mode is similar to the power grid. More specifically, the cloud is a service composed of computing, network, storage, and information that is a “resource pool” and can quickly complete deployment, recycling, expansion, and reduction, able to allocate resources in a humane, on-demand, and efficient manner, according to the computing model and definition of cloud computing [12].There are three main types of cloud computing services: Infrastructure as a service (InsfrasturctureasaService) mainly virtualizes hardware resources and infrastructure, and network resources, storage resources, and computing resources are provided in the form of services, for example, Amazon’s elastic computing cloud EC2; Platform as a service (PlatformasaService) provides application building framework and API, such as Google’s AppEngine and water conservancy spatial information service platform; Software as a service (SoftwareasaService) provides the software as a service, in order to allow users to use standard browsers to access software features; for example, from the perspective of Salesforce’s cloud computing construction model, there are three main types: public cloud, private cloud, and hybrid cloud.


#### 2.2.2. OnApp Cloud Computing Platform

OnApp has complete resource integration and cloud computing service capabilities, in the entire cloud computing architecture; it is equivalent to a cloud operating system, providing a supporting environment for building a cloud computing-based water conservancy spatial information service platform. OnApp consists of controller, virtualization platform, storage management area, and backup area. The controller contains a control panel interface, through which users and administrators can perform various operations in the cloud; the platform also provides a RESTCloud API that can be called by third-party applications to programmatically control cloud resources and output resource utilization, in addition; the OnApp controller also contains a backup subsystem, virtual machine mirroring engine, resource monitor, and network management program [13]. A Hypervisor is also a cloud server and contains a virtual power controller, a virtual disk controller, a virtual network controller, a virtual machine manager, and a built-in firewall. All cloud servers use a shared SAN network to store data. Finally, all virtual machines can be open to the Internet or private networks. In the OnApp network architecture, the Hypervisor cloud server requires three network cards, connecting the management network, public or private network, and storage area network, respectively; the entire network can be divided into four parts: management network, public or private network, storage network, and backup network; in actual deployment, these networks belong to different network segments; this logical division is more manageable and safer. OnApp provides a complete REST-based CloudAPI to realize user management, charging management, IP management, storage area management, network area management, data storage area management, virtual firewall management, virtualization platform management, data management, virtual machine management, template management, backup management, log management, and flexible cluster management; it can effectively support the development of PaaS (PlatformasaService, platform as a service) [14].


#### 2.2.3. Elastic Cloud Service Technology

With the help of virtualized computing clusters, it can dynamically organize heterogeneous computing resources, storage resources, and network resources; shield the diversity of hardware platforms and operating systems; build a computing environment that meets the diverse needs of high-performance computing; improve the overall utilization of resources; and achieve flexible computing. The water conservancy spatial information service platform can effectively integrate the cloud computing environment, according to the flexible architecture design, and provide flexible cloud services. It can adjust the service flexibility according to the changes of user demand, greatly reducing the software and hardware investment and operating costs.The elastic cloud service technology of the water conservancy spatial information service platform is in a virtual cluster environment; three service nodes that mirror each other are deployed on separate virtual machines to provide water conservancy spatial information services; each virtual machine is installed with basic water conservancy service modules, monitoring components, security components, log component, service component, aggregation component, etc., and provides core functions such as service monitoring, authorization, request processing, service aggregation, and log management. Under such a cluster architecture, the services provided by the service nodes are exactly the same, which facilitates the realization of load balancing and disaster tolerance processing of requests.


#### 2.2.4. Implementation of Elastic Cloud Service

In the water conservancy spatial information service platform, it is necessary to achieve elastic expansion; first, you need to understand the content of elastic expansion. In the cloud computing environment, it mainly refers to the virtual machine hosting and running water conservancy basic service modules. By preinstalling the basic water conservancy service module in the virtual machine template, when the system reaches the load condition, according to the virtual machine template, start its virtual machine instance and add it to the platform cluster environment to achieve flexible changes.In the entire platform, whether the elastic expansion is reasonable, mainly rely on elastic scaling algorithms, these algorithms need to rely on the performance parameters of the service and the real-time status parameters of the virtual machine operating system, in order to make a comprehensive judgment. For example, the performance parameters of the service include the following: (1) the number of services processed per unit time and (2) the number of service instances. The performance parameters of the virtual machine operating system include the following: (1) CPU usage, (2) memory usage, (3) network bandwidth usage, and (4) 10 performance conditions. Therefore, it is very important to establish an elastically scalable load model, among them; the performance parameters of the service can be obtained by the platform REST AdminAPI. The real-time performance parameters of the virtual machine operating system can be obtained through the OnApp Cloud API. From an applicable point of view, passive polling methods are more suitable for flexible architectures. The platform needs to be based on the requested load situation and automatic horizontal and horizontal expansion of virtual machines, since the virtual machine is hosting and running the water conservancy space basic service system and related components; it can greatly improve the response performance of platform services [15]. There are three main types of services provided by the water conservancy spatial information service platform: slice map service, spatial query service, and spatial analysis processing service. After observation and experimentation, the service processing performance threshold acceptable to users is shown in Table 1.Table 1
Service processing performance threshold.
Service typeSingle request processing time thresholdTransaction processing threshold per second(i) Slice map service0.244.1(ii) Space query service1-3, take 1.510.68(iii) Space analysis and processing services5-10, take 7.490.14For a virtual machine, its load mainly depends on the CPU and network I0 to determine; a certain time interval, such as 30 minutes, can be used to calculate the load threshold of the virtual machine as shown in Table2.Table 2
Virtual machine elastic change threshold.
ProjectFlexible expansion utilization thresholdFlexible reduction, utilization thresholdCPUMore than 80%Less than 30%Network bandwidthMore than 80%Less than 30%Since the three types of services have different degrees of CPU and network utilization requirements, they can be assigned weight coefficients as shown in Figure2.Figure 2
Resource utilization weight coefficients of different service types.Under the premise of knowing the performance threshold of the service and the elastic change threshold of the virtual machine, combining the real-time operating parameters of the virtual machine of the cloud computing platform and the service performance parameters obtained by the service monitoring module, using formulas (1)–(3) algorithm can calculate the comprehensive performance.
(1)ps=∑i=1n∑j=1nTSij/S′/mn,where ps is the overall performance of the service, Sij is the number of transactions processed by the j-th service unit in the i-th virtual machine, S′ is the transaction processing threshold of a service, m is the total number of services in a single virtual machine, and n is the total number of virtual machines.
(2)Ucpu=∑i=1nUicpun.In the formula,Ucpu is the overall CPU utilization; Uicpu is the CPU utilization of the i-th virtual machine.
(3)Unet=∑i=1nUinetn.In the formula,Unet is the overall utilization of the network; Uinet is the network utilization of the i-th virtual machine.According to the above formula, you can get the CPU and network utilization of a single service; if you want to achieve precise elastic changes, you can also adjust the number of instances and running status of a single service based on this, because CPU and network bandwidth are the core resources provided by a cloud computing infrastructure [16].


## 2.1. Pooling Service

The service object refers to the coarse-grained component object that provides the service; it is mainly based on basic component design, in order to provide a coarse-grained interface pooling service object to create multiple instances of the service object in advance and put it into the “object pool,” waiting for user requests. When a user request arrives, the system first accesses the “object pool” of the service, if there is a service instance processing the request in the pool, this instance is returned from the pool, and its method is called by the requester to obtain the processing result. When the request is processed, the caller will not destroy this instance; instead, it will return the instance to the “object pool,” in order to process the next user request: if when the request arrives, it happens that there is no service instance available in the “object pool”; the request thread will be blocked until there is an idle instance or the timeout ends [10].Figure1 is the design class diagram of the map service MapService and the pyramid slice service TiledMapService; they all inherit the AbstractService abstract service class; AbstractService uses generic classes to represent service instance components associated with the service; for example, the component associated with MapService is MapServerInstance, and the component associated with TiledMapService is TiledMapServerInstance; other services are similar; for example, SAService is associated with SAServerInstance, so I will not repeat them here.Figure 1
Map service and slice service design.AbstactService is the base class of all service classes, it defines Start and Stop methods, indicates to start and stop a service. In its implementation, it mainly provides the operation of “Object Pool” (0ObjectPool); for example: Start method will create an object pool and allocate resources; Stop method will destroy the pool and release resources [11].


## 2.2. Elastic Cloud Service

### 2.2.1. Overview of Cloud Computing

Cloud computing is a computing model based on the Internet, by distributed computing (DistributedComputing). Grid computing (GridComputing), parallel computing (ParallelComputing), and utility computing (UtilityComputing) are developed; it is an emerging business computing model and innovative ideas for resource integration. Through this calculation model, shared software and hardware resources are provided to computers and other devices in an on-demand and easily expandable manner through a “resource pool”; the whole operation mode is similar to the power grid. More specifically, the cloud is a service composed of computing, network, storage, and information that is a “resource pool” and can quickly complete deployment, recycling, expansion, and reduction, able to allocate resources in a humane, on-demand, and efficient manner, according to the computing model and definition of cloud computing [12].There are three main types of cloud computing services: Infrastructure as a service (InsfrasturctureasaService) mainly virtualizes hardware resources and infrastructure, and network resources, storage resources, and computing resources are provided in the form of services, for example, Amazon’s elastic computing cloud EC2; Platform as a service (PlatformasaService) provides application building framework and API, such as Google’s AppEngine and water conservancy spatial information service platform; Software as a service (SoftwareasaService) provides the software as a service, in order to allow users to use standard browsers to access software features; for example, from the perspective of Salesforce’s cloud computing construction model, there are three main types: public cloud, private cloud, and hybrid cloud.


### 2.2.2. OnApp Cloud Computing Platform

OnApp has complete resource integration and cloud computing service capabilities, in the entire cloud computing architecture; it is equivalent to a cloud operating system, providing a supporting environment for building a cloud computing-based water conservancy spatial information service platform. OnApp consists of controller, virtualization platform, storage management area, and backup area. The controller contains a control panel interface, through which users and administrators can perform various operations in the cloud; the platform also provides a RESTCloud API that can be called by third-party applications to programmatically control cloud resources and output resource utilization, in addition; the OnApp controller also contains a backup subsystem, virtual machine mirroring engine, resource monitor, and network management program [13]. A Hypervisor is also a cloud server and contains a virtual power controller, a virtual disk controller, a virtual network controller, a virtual machine manager, and a built-in firewall. All cloud servers use a shared SAN network to store data. Finally, all virtual machines can be open to the Internet or private networks. In the OnApp network architecture, the Hypervisor cloud server requires three network cards, connecting the management network, public or private network, and storage area network, respectively; the entire network can be divided into four parts: management network, public or private network, storage network, and backup network; in actual deployment, these networks belong to different network segments; this logical division is more manageable and safer. OnApp provides a complete REST-based CloudAPI to realize user management, charging management, IP management, storage area management, network area management, data storage area management, virtual firewall management, virtualization platform management, data management, virtual machine management, template management, backup management, log management, and flexible cluster management; it can effectively support the development of PaaS (PlatformasaService, platform as a service) [14].


### 2.2.3. Elastic Cloud Service Technology

With the help of virtualized computing clusters, it can dynamically organize heterogeneous computing resources, storage resources, and network resources; shield the diversity of hardware platforms and operating systems; build a computing environment that meets the diverse needs of high-performance computing; improve the overall utilization of resources; and achieve flexible computing. The water conservancy spatial information service platform can effectively integrate the cloud computing environment, according to the flexible architecture design, and provide flexible cloud services. It can adjust the service flexibility according to the changes of user demand, greatly reducing the software and hardware investment and operating costs.The elastic cloud service technology of the water conservancy spatial information service platform is in a virtual cluster environment; three service nodes that mirror each other are deployed on separate virtual machines to provide water conservancy spatial information services; each virtual machine is installed with basic water conservancy service modules, monitoring components, security components, log component, service component, aggregation component, etc., and provides core functions such as service monitoring, authorization, request processing, service aggregation, and log management. Under such a cluster architecture, the services provided by the service nodes are exactly the same, which facilitates the realization of load balancing and disaster tolerance processing of requests.


### 2.2.4. Implementation of Elastic Cloud Service

In the water conservancy spatial information service platform, it is necessary to achieve elastic expansion; first, you need to understand the content of elastic expansion. In the cloud computing environment, it mainly refers to the virtual machine hosting and running water conservancy basic service modules. By preinstalling the basic water conservancy service module in the virtual machine template, when the system reaches the load condition, according to the virtual machine template, start its virtual machine instance and add it to the platform cluster environment to achieve flexible changes.In the entire platform, whether the elastic expansion is reasonable, mainly rely on elastic scaling algorithms, these algorithms need to rely on the performance parameters of the service and the real-time status parameters of the virtual machine operating system, in order to make a comprehensive judgment. For example, the performance parameters of the service include the following: (1) the number of services processed per unit time and (2) the number of service instances. The performance parameters of the virtual machine operating system include the following: (1) CPU usage, (2) memory usage, (3) network bandwidth usage, and (4) 10 performance conditions. Therefore, it is very important to establish an elastically scalable load model, among them; the performance parameters of the service can be obtained by the platform REST AdminAPI. The real-time performance parameters of the virtual machine operating system can be obtained through the OnApp Cloud API. From an applicable point of view, passive polling methods are more suitable for flexible architectures. The platform needs to be based on the requested load situation and automatic horizontal and horizontal expansion of virtual machines, since the virtual machine is hosting and running the water conservancy space basic service system and related components; it can greatly improve the response performance of platform services [15]. There are three main types of services provided by the water conservancy spatial information service platform: slice map service, spatial query service, and spatial analysis processing service. After observation and experimentation, the service processing performance threshold acceptable to users is shown in Table 1.Table 1
Service processing performance threshold.
Service typeSingle request processing time thresholdTransaction processing threshold per second(i) Slice map service0.244.1(ii) Space query service1-3, take 1.510.68(iii) Space analysis and processing services5-10, take 7.490.14For a virtual machine, its load mainly depends on the CPU and network I0 to determine; a certain time interval, such as 30 minutes, can be used to calculate the load threshold of the virtual machine as shown in Table2.Table 2
Virtual machine elastic change threshold.
ProjectFlexible expansion utilization thresholdFlexible reduction, utilization thresholdCPUMore than 80%Less than 30%Network bandwidthMore than 80%Less than 30%Since the three types of services have different degrees of CPU and network utilization requirements, they can be assigned weight coefficients as shown in Figure2.Figure 2
Resource utilization weight coefficients of different service types.Under the premise of knowing the performance threshold of the service and the elastic change threshold of the virtual machine, combining the real-time operating parameters of the virtual machine of the cloud computing platform and the service performance parameters obtained by the service monitoring module, using formulas (1)–(3) algorithm can calculate the comprehensive performance.
(1)ps=∑i=1n∑j=1nTSij/S′/mn,where ps is the overall performance of the service, Sij is the number of transactions processed by the j-th service unit in the i-th virtual machine, S′ is the transaction processing threshold of a service, m is the total number of services in a single virtual machine, and n is the total number of virtual machines.
(2)Ucpu=∑i=1nUicpun.In the formula,Ucpu is the overall CPU utilization; Uicpu is the CPU utilization of the i-th virtual machine.
(3)Unet=∑i=1nUinetn.In the formula,Unet is the overall utilization of the network; Uinet is the network utilization of the i-th virtual machine.According to the above formula, you can get the CPU and network utilization of a single service; if you want to achieve precise elastic changes, you can also adjust the number of instances and running status of a single service based on this, because CPU and network bandwidth are the core resources provided by a cloud computing infrastructure [16].


## 2.2.1. Overview of Cloud Computing

Cloud computing is a computing model based on the Internet, by distributed computing (DistributedComputing). Grid computing (GridComputing), parallel computing (ParallelComputing), and utility computing (UtilityComputing) are developed; it is an emerging business computing model and innovative ideas for resource integration. Through this calculation model, shared software and hardware resources are provided to computers and other devices in an on-demand and easily expandable manner through a “resource pool”; the whole operation mode is similar to the power grid. More specifically, the cloud is a service composed of computing, network, storage, and information that is a “resource pool” and can quickly complete deployment, recycling, expansion, and reduction, able to allocate resources in a humane, on-demand, and efficient manner, according to the computing model and definition of cloud computing [12].There are three main types of cloud computing services: Infrastructure as a service (InsfrasturctureasaService) mainly virtualizes hardware resources and infrastructure, and network resources, storage resources, and computing resources are provided in the form of services, for example, Amazon’s elastic computing cloud EC2; Platform as a service (PlatformasaService) provides application building framework and API, such as Google’s AppEngine and water conservancy spatial information service platform; Software as a service (SoftwareasaService) provides the software as a service, in order to allow users to use standard browsers to access software features; for example, from the perspective of Salesforce’s cloud computing construction model, there are three main types: public cloud, private cloud, and hybrid cloud.


## 2.2.2. OnApp Cloud Computing Platform

OnApp has complete resource integration and cloud computing service capabilities, in the entire cloud computing architecture; it is equivalent to a cloud operating system, providing a supporting environment for building a cloud computing-based water conservancy spatial information service platform. OnApp consists of controller, virtualization platform, storage management area, and backup area. The controller contains a control panel interface, through which users and administrators can perform various operations in the cloud; the platform also provides a RESTCloud API that can be called by third-party applications to programmatically control cloud resources and output resource utilization, in addition; the OnApp controller also contains a backup subsystem, virtual machine mirroring engine, resource monitor, and network management program [13]. A Hypervisor is also a cloud server and contains a virtual power controller, a virtual disk controller, a virtual network controller, a virtual machine manager, and a built-in firewall. All cloud servers use a shared SAN network to store data. Finally, all virtual machines can be open to the Internet or private networks. In the OnApp network architecture, the Hypervisor cloud server requires three network cards, connecting the management network, public or private network, and storage area network, respectively; the entire network can be divided into four parts: management network, public or private network, storage network, and backup network; in actual deployment, these networks belong to different network segments; this logical division is more manageable and safer. OnApp provides a complete REST-based CloudAPI to realize user management, charging management, IP management, storage area management, network area management, data storage area management, virtual firewall management, virtualization platform management, data management, virtual machine management, template management, backup management, log management, and flexible cluster management; it can effectively support the development of PaaS (PlatformasaService, platform as a service) [14].


## 2.2.3. Elastic Cloud Service Technology

With the help of virtualized computing clusters, it can dynamically organize heterogeneous computing resources, storage resources, and network resources; shield the diversity of hardware platforms and operating systems; build a computing environment that meets the diverse needs of high-performance computing; improve the overall utilization of resources; and achieve flexible computing. The water conservancy spatial information service platform can effectively integrate the cloud computing environment, according to the flexible architecture design, and provide flexible cloud services. It can adjust the service flexibility according to the changes of user demand, greatly reducing the software and hardware investment and operating costs.The elastic cloud service technology of the water conservancy spatial information service platform is in a virtual cluster environment; three service nodes that mirror each other are deployed on separate virtual machines to provide water conservancy spatial information services; each virtual machine is installed with basic water conservancy service modules, monitoring components, security components, log component, service component, aggregation component, etc., and provides core functions such as service monitoring, authorization, request processing, service aggregation, and log management. Under such a cluster architecture, the services provided by the service nodes are exactly the same, which facilitates the realization of load balancing and disaster tolerance processing of requests.


## 2.2.4. Implementation of Elastic Cloud Service

In the water conservancy spatial information service platform, it is necessary to achieve elastic expansion; first, you need to understand the content of elastic expansion. In the cloud computing environment, it mainly refers to the virtual machine hosting and running water conservancy basic service modules. By preinstalling the basic water conservancy service module in the virtual machine template, when the system reaches the load condition, according to the virtual machine template, start its virtual machine instance and add it to the platform cluster environment to achieve flexible changes.In the entire platform, whether the elastic expansion is reasonable, mainly rely on elastic scaling algorithms, these algorithms need to rely on the performance parameters of the service and the real-time status parameters of the virtual machine operating system, in order to make a comprehensive judgment. For example, the performance parameters of the service include the following: (1) the number of services processed per unit time and (2) the number of service instances. The performance parameters of the virtual machine operating system include the following: (1) CPU usage, (2) memory usage, (3) network bandwidth usage, and (4) 10 performance conditions. Therefore, it is very important to establish an elastically scalable load model, among them; the performance parameters of the service can be obtained by the platform REST AdminAPI. The real-time performance parameters of the virtual machine operating system can be obtained through the OnApp Cloud API. From an applicable point of view, passive polling methods are more suitable for flexible architectures. The platform needs to be based on the requested load situation and automatic horizontal and horizontal expansion of virtual machines, since the virtual machine is hosting and running the water conservancy space basic service system and related components; it can greatly improve the response performance of platform services [15]. There are three main types of services provided by the water conservancy spatial information service platform: slice map service, spatial query service, and spatial analysis processing service. After observation and experimentation, the service processing performance threshold acceptable to users is shown in Table 1.Table 1
Service processing performance threshold.
Service typeSingle request processing time thresholdTransaction processing threshold per second(i) Slice map service0.244.1(ii) Space query service1-3, take 1.510.68(iii) Space analysis and processing services5-10, take 7.490.14For a virtual machine, its load mainly depends on the CPU and network I0 to determine; a certain time interval, such as 30 minutes, can be used to calculate the load threshold of the virtual machine as shown in Table2.Table 2
Virtual machine elastic change threshold.
ProjectFlexible expansion utilization thresholdFlexible reduction, utilization thresholdCPUMore than 80%Less than 30%Network bandwidthMore than 80%Less than 30%Since the three types of services have different degrees of CPU and network utilization requirements, they can be assigned weight coefficients as shown in Figure2.Figure 2
Resource utilization weight coefficients of different service types.Under the premise of knowing the performance threshold of the service and the elastic change threshold of the virtual machine, combining the real-time operating parameters of the virtual machine of the cloud computing platform and the service performance parameters obtained by the service monitoring module, using formulas (1)–(3) algorithm can calculate the comprehensive performance.
(1)ps=∑i=1n∑j=1nTSij/S′/mn,where ps is the overall performance of the service, Sij is the number of transactions processed by the j-th service unit in the i-th virtual machine, S′ is the transaction processing threshold of a service, m is the total number of services in a single virtual machine, and n is the total number of virtual machines.
(2)Ucpu=∑i=1nUicpun.In the formula,Ucpu is the overall CPU utilization; Uicpu is the CPU utilization of the i-th virtual machine.
(3)Unet=∑i=1nUinetn.In the formula,Unet is the overall utilization of the network; Uinet is the network utilization of the i-th virtual machine.According to the above formula, you can get the CPU and network utilization of a single service; if you want to achieve precise elastic changes, you can also adjust the number of instances and running status of a single service based on this, because CPU and network bandwidth are the core resources provided by a cloud computing infrastructure [16].


## 3. Results and Analysis

In fact, for a single water conservancy spatial information service, the function it provides is relatively single, the space is limited, and it is usually unable to meet the complex application needs of users in the water conservancy industry; for example, the information provided by the service is limited. Therefore, how to integrate and combine multiple services and provide new spatial information services with more powerful functions, wider spatial scope, and richer information has become a new research hotspot at present; it is also a problem that must be considered in the realization of water conservancy spatial information service platform [17]. Mashup (aggregation) originally meant that the artist combined the lyrics and music of two songs to create. In IT terminology, it is extended to a website that combines information and services from multiple sources. In the field of spatial information, the introduction of Mashup technology is also of great significance. Because Mashup can create new applications and services based on the APIs of multiple services, it can change the way and difficulty of system construction. It has become a new research hotspot to integrate and combine multiple services to provide new spatial information services with stronger functions, wider spatial scope, and richer information. Among them, map aggregation is the most common; it is an aggregation of services. The so-called service aggregation is to integrate and combine an existing set of services according to certain business rules, build new services, execute new processes, and provide users with new information. From the perspective of the assembly process of aggregation, it can be divided into static aggregation and dynamic aggregation, among them; dynamic aggregation includes semiautomatic aggregation and automatic aggregation. Therefore, spatial information service aggregation refers to the superposition and combination of multiple spatial information services to generate new services and form a service chain. It focuses on the key technologies to realize the platform: pooled service, elastic cloud service and spatial information service. Aggregated pooling services mainly use the pooling mechanism to improve the responsiveness of platform services by creating object instances in advance [9]. Cloud API provided on the cloud computing platform (such as creating a virtual machine, starting a virtual machine at runtime, and obtaining the real-time status of a virtual machine) and on the basis of the REST Admin API provided by the water conservancy basic service module, using cloud computing technology ideas, design and implementation of elastic cloud service technology; it enables the water conservancy spatial information service platform to be deployed in a cloud computing environment, supporting cloud architecture, provide flexible computing capabilities, reduce the waste of infrastructure resources, and meet the concurrent access needs of a large number of users [18]. As one of the core service capabilities of the platform, spatial information service aggregation can effectively aggregate multisource spatial information, generate new services, and provide new information [7].To study the aggregation of spatial information services, it is necessary to analyze its aggregation content, such as the following: (1) spatial data and data that can be spatialized, such as aggregated weather data in GeoRSS format; (2) basic map data, such as aggregating vector and image data from GoogleMap and BingMap, as a background map; and (3) spatial processing procedures, such as spatial analysis procedures such as aggregate projection conversion, thermal analysis, and interpolation analysis. From the perspective of technical realization, there are two main ways to realize spatial information service aggregation: (1) client aggregation refers to relying on the platform client to realize the access and combination of multisource services and generate new data and maps. Its characteristics are as follows: the client has done most of the work, and the server has less pressure; (2) server-side aggregation refers to the process of superimposing, combining, and orchestrating services from multiple sources on the server side to generate new services. Its characteristics are as follows: the server has done a lot of work, the client is not demanding, and it can also reduce the client’s understanding of multisource services.


## 4. Conclusion

In order to study the construction of a cloud computing-based hydraulic engineering structure platform, it focuses on the key technologies to realize the platform: pooled services and elastic cloud services. Using cloud computing technology ideas, design, and implementation of elastic cloud service technology, enable the water conservancy spatial information service platform to be deployed in the cloud computing environment, support cloud architecture, provide flexible computing capabilities, reduce the waste of infrastructure resources, and meet the concurrent access needs of a large number of users. Spatial information service aggregation is one of the core service capabilities of the platform; it can effectively aggregate multisource spatial information, generate new services, and provide new information.



---

*Source: 3262257-2022-04-29.xml*