# International Trade Balance Algorithm Based on the Ownership Principle of Mobile Edge Computing

Authors: Fangfang Du
Journal: Mathematical Problems in Engineering
 (2021)

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## Abstract

As an emerging mobile computing technology, mobile edge computing is an important key technology to improve the computing services of mobile devices. This paper mainly studies the balance of international trade algorithm based on the principle of moving edge computing ownership. In order to obtain all the data needed to perform the task, each mobile device can exchange data information with its connected base station through the wireless network. On the basis of satisfying the quality of service of users, including considering the user connection and service configuration, the network energy consumption is minimized in continuoust period by shutting down some servers whose resources are not fully utilized. At the same time, in order to reduce the switching cost of edge server and ensure the stability of service, frequent switching of edge server should be avoided. At the beginning, there is division of labor economy. With the development of specialized production, the degree of international division of labor is increasing due to the effect of experience accumulation. The trade efficiency is growing endogenously. The international division of labor is further deepened, and the types and quantity of products participating in the international division of labor are greatly increased, so as to realize the upgrading of trade structure. Before constructing the structural VAR model of Bti, R/W, K/L, and TFP, we need to test its stationarity. Using Eviews 5.0 software, ADF test and PP test were carried out on the unit root of BTI, r/w, K/L, and TFP time series data. With the increase of user task arrival rate, the average time revenue increases continuously. However, when the arrival rate is greater than 3 kbit/slot, the average time revenue increases slowly. The results show that the research results in system model and resource optimization algorithm will provide reliable theoretical and technical support for the practical application of mobile edge computing.


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## Body

## 1. Introduction

Since the reform and opening up, China has achieved high-quality development in all economic fields. Even after the subprime mortgage crisis in the United States, China’s economy has maintained a stable and rapid development, and its GDP growth rate ranks first in the world [1]. In this background, in order to meet the requirements of wireless data transmission and mobile devices to solve the problems [2], this article will research on the edge of the mobile computing network weighted bits and maximize and safety calculation efficiency maximization problem, so as to further enhance the security of the mobile computing performance and energy efficiency.The current world is a highly competitive and closely connected world, and as a large trading country, China's every move will not only affect the changes in the domestic economy but also the economic changes in the countries and regions related to it. For this reason, under the mobile edge computing architecture, we can further optimize resource management, allocate resources reasonably to improve power efficiency, and use the limited power to perform more tasks as much as possible. On the other hand, lower energy consumption may slow down the task execution speed [3], which cannot meet the user's service quality requirements.Active caching and processing and multilayer interference cancellation are discussed in [4]. He demonstrated that the cross-border trade statistics system based on the territorial principle has defects, which greatly interferes with the authenticity of trade balance statistics. This highlights the importance of ownership adjustment. Tran T X believes that MEC is an emerging paradigm, which provides computing, storage and network resources within the edge of the mobile RAN. He envisioned a real-time, context-sensitive collaboration framework that is located at the edge of the RAN, composed of MEC servers and mobile devices and fused heterogeneous resources at the edge. Specifically, he introduced and studied three representative use cases, ranging from mobile edge orchestration, collaboration the promising benefits of the proposed method in facilitating the evolution to 5G networks. Although his research is more accurate, it is not comprehensive [5]. Yulun believes that the virtualized small cell network integrated with mobile edge computing is a promising example that can economically provide broadband access and intensive computing for user equipment in the case of multiple mobile virtual network operators and infrastructure providers. By comprehensively considering offloading time slice and power allocation, he formulates the UE energy consumption reduction in virtualized SCN as a mixed integer nonlinear programming. His goal is to minimize the UE’s total energy consumption when the total network throughput is the smallest. In order to effectively solve the problem, he converted it into a double convex problem by adding auxiliary variables, so that an effective iterative algorithm can be derived from two subproblems. Although his research algorithm is innovative, it lacks precision [6]. Wu H believes that the rapid advancement of wireless power transmission and mobile edge computing provides a promising method for the Industrial Internet of Things to improve manufacturing quality and productivity [7]. Due to wireless channel congestion, time-related energy constraints, complex device heterogeneity, and annoying signaling overhead, scheduling in this situation is challenging. He first proposed an online algorithm called energy-aware resource scheduling to maximize system utility including throughput and fairness while taking into account system sustainability and stability. Subsequently, he extended the ERS algorithm to more realistic scenarios [8]. He provided the optimal scheduling decision of the scene and analyzed the optimal loss of system utility under the outdated NSI [9]. Although the algorithm he used is more effective, it lacks necessary experimental data [10]. Lin C J believes that the next-generation mobile network 5G aims to support lower end-to-end latency, higher reliability, and higher throughput, which can be improved by MEC and multi-RAT offloading, respectively. He introduced a dual offloading mechanism called LCCOP, which offloads incoming traffic to the best wireless pair and edge pair, depending on the end-to-end latency of the request connection. He ran simulations to compare LCCOP with traditional unloading solutions [11]. Although his algorithm is very reliable, there are still big loopholes [12].Based on the theory of international industrial transfer, international trade theory, and spatial economics, this paper links the development of international trade with the evolution of international industrial transfer organically with the research method of new economic geography. In this paper, cross-section data of each country are also adopted, and the combination of time series and cross-section can more scientifically explain the impact of RMB exchange rate fluctuations on the trade balance between China and various trading partners.


## 2. Ownership Principle of Mobile Edge Computing

### 2.1. Mobile Edge Computing

The task scheduling process in mobile edge computing is shown in Figure1. When the mobile device has tasks that need to be processed by the MEC server, the device first perceives and collects the current network environment information, including the status of the nearby MEC server, the server’s computing and storage capabilities [13], and wireless channel status. For the traditional MCC system, the task offloading transmission process needs to go through the wireless access network and the core network of multi-hop routing, which leads to a significant increase in transmission delay and consumes a lot of energy. However, under normal circumstances, because the edge cloud is located on the side of the wireless access network [14], the task offloading of MEC can avoid passing through the core network with large delay, thereby effectively reducing the delay and saving energy [15]. In addition, MEC servers are affected by factors such as uneven geographical distribution and user mobility, which will cause some servers to be overloaded, which will seriously affect the performance of MEC [16, 17].Figure 1
Task scheduling process.For users who choose to offload tasks to MEC execution, when usern uses subchannel k for data transmission, considering the interference caused by frequency reuse, the signal-to interference noise ratio of user n on subchannel k can be expressed as(1)SINRnk=pnkhn,nkω0+∑m=1,m≠nNpmkhm,nk,The rate when usern performs uplink transmission through subchannel k is(2)rnk=cnk⋅Blog21+SINRnk,where B represents the subchannel bandwidth and cnk is the channel allocation variable.The coordinate descent method only changes the unloading decision of one user at a time during one iteration.Qnl is used to denote the revenue obtained by the system during the first iteration, which can be expressed as(3)Qnl=VAl−1−VAl−1n,where Al−1n represents the uninstall decision after user n changes status, and the specific update rules are as follows:(4)Al−1n=a1l−1,a2l−1,…,anl−1⊕1,aNl−1,where ⊕ represents the modulo two addition method.The update formula of particle flight speed and flight distance is as follows:(5)vit+1=wvit+c1r1pbesti−xit+c2r2gbest−xit,xit+1=xit+vit+1,The population diversity formula is as follows:(6)Dt+1=1n−1∑i=1ndit+1¯2−dit+1,whereD(t + 1) represents the population diversity of the particle swarm in the t + 1th iteration.Based on the inertia weight function of population diversity, the expression is as follows:(7)wit+1=wite1/Dt+1+1−1+1,Dt+1≥Dt,wite1/Dt+1+1−1,Dt+1<Dt.It is assumed that the particles tend to choose 0 or 1 in the discrete binary space, and at the same time, the speed change caused by the diversity of the population is used as a parameter to define a sigmoid function, which is expressed as follows:(8)Svit+1=11+e−vit+1.Define the Lagrangian functionℓx,μ,v, expressed as(9)ℓx,μ,v=fx+∑j=1Jμjgjx+∑r=1Rvrhrx.The dynamic update of each queue can be expressed as(10)Qit+1=maxQit−Yit+Zit,∀i∈1,2,…,M.The processing delay of the MEC serverEn scheduling part of the CNN layer to execute the UDm offloading task is modeled as(11)Dmnp=∑k=1KδmnkpnkSmβmnFn.The MEC server is as close as possible to the local equipment to provide computing, storage, and other services. As the transmission distance of the task on the network is shortened, the transmission time and energy consumption can be reduced, and the battery life of the user equipment can be extended [18]; and because the MEC server is deployed locally around the equipment, task data can be distributed to different servers for processing, which greatly reduces the network transmission pressure and server computing pressure [19, 20]. For the fixed strategy π, the Bellman equation is defined as(12)Vπs=Rs,a+γ∑s′∈SPss′aVπs′,where s represents the current state, after performing action a in state s, the state will be converted to the new state s′.The optimal strategy corresponding to the optimal value function is defined as(13)π∗s=argmaxa∈A∑s′∈SPss′aV∗s′.


### 2.2. Principle of Ownership

The development of processing trade expands China’s export trade, and the continuous improvement of the industrial chain and the development of the domestic market make the import substitution effect play out, which promotes the rapid growth of the surplus. Trade and industry complement each other. Industry is the basis of trade [21], and foreign-invested enterprises are an integral part of industry, especially an important part of export industry, which constitutes the main cause of surplus. Trade is the extension of industry, and surplus and foreign enterprise production promote each other and develop together [22]. Most developing countries are on this list, while most developed countries are on the contrary. FDI outflow needs technology and management knowledge, as well as a large amount of foreign exchange reserves, which developing countries do not have. After considering the sales, import and exports of foreign-invested enterprises and adjusting the traditional trade volume, the re-estimation results show that China's trade balance does not show a trade surplus but a large trade deficit [23].


### 2.3. International Trade Balance

The most important feature of a country’s production and trade model of the division of labor in the global value chain is vertical specialized production. In the form of global production segmentation, the production process concentrated in one country or region is divided into different links and distributed around the world. Therefore, there will be many countries participating in production, but most of the products are consumed by one country, which will produce an asymmetry between imports and exports, which will cause a trade surplus/deficit phenomenon [24].In general, when the vertical specialization is low, the trade balance will be small; but when a country’s export trade scale expands rapidly, the total trade surplus will increase significantly. From the perspective of traditional trade statistics, as long as a product is exported from one country to another, its total value is considered as the corresponding trade volume, and the intermediate products will be calculated in this way for many times. Specifically, if a country is at the last point of producing final consumer goods, the value of its exported products includes not only the added value of its own country but also the accumulated value of intermediate goods, which will lead to the amplification of the country’s exports [25, 26].When the input factors are imported, raw materials, the exchange rate changes not only affect the overseas market prices of exported goods but also transmit to the prices of imported factors. To calculate the impact of exchange rate changes on trade, it is necessary to examine the proportion of imported factors in export production. The greater the proportion of production raw materials in export production, the less obvious the promotion effect. That is, if most of the raw materials are imported and the product is completely used for export, then the exchange rate promotion effect is not obvious for the export commodity [27].


## 2.1. Mobile Edge Computing

The task scheduling process in mobile edge computing is shown in Figure1. When the mobile device has tasks that need to be processed by the MEC server, the device first perceives and collects the current network environment information, including the status of the nearby MEC server, the server’s computing and storage capabilities [13], and wireless channel status. For the traditional MCC system, the task offloading transmission process needs to go through the wireless access network and the core network of multi-hop routing, which leads to a significant increase in transmission delay and consumes a lot of energy. However, under normal circumstances, because the edge cloud is located on the side of the wireless access network [14], the task offloading of MEC can avoid passing through the core network with large delay, thereby effectively reducing the delay and saving energy [15]. In addition, MEC servers are affected by factors such as uneven geographical distribution and user mobility, which will cause some servers to be overloaded, which will seriously affect the performance of MEC [16, 17].Figure 1
Task scheduling process.For users who choose to offload tasks to MEC execution, when usern uses subchannel k for data transmission, considering the interference caused by frequency reuse, the signal-to interference noise ratio of user n on subchannel k can be expressed as(1)SINRnk=pnkhn,nkω0+∑m=1,m≠nNpmkhm,nk,The rate when usern performs uplink transmission through subchannel k is(2)rnk=cnk⋅Blog21+SINRnk,where B represents the subchannel bandwidth and cnk is the channel allocation variable.The coordinate descent method only changes the unloading decision of one user at a time during one iteration.Qnl is used to denote the revenue obtained by the system during the first iteration, which can be expressed as(3)Qnl=VAl−1−VAl−1n,where Al−1n represents the uninstall decision after user n changes status, and the specific update rules are as follows:(4)Al−1n=a1l−1,a2l−1,…,anl−1⊕1,aNl−1,where ⊕ represents the modulo two addition method.The update formula of particle flight speed and flight distance is as follows:(5)vit+1=wvit+c1r1pbesti−xit+c2r2gbest−xit,xit+1=xit+vit+1,The population diversity formula is as follows:(6)Dt+1=1n−1∑i=1ndit+1¯2−dit+1,whereD(t + 1) represents the population diversity of the particle swarm in the t + 1th iteration.Based on the inertia weight function of population diversity, the expression is as follows:(7)wit+1=wite1/Dt+1+1−1+1,Dt+1≥Dt,wite1/Dt+1+1−1,Dt+1<Dt.It is assumed that the particles tend to choose 0 or 1 in the discrete binary space, and at the same time, the speed change caused by the diversity of the population is used as a parameter to define a sigmoid function, which is expressed as follows:(8)Svit+1=11+e−vit+1.Define the Lagrangian functionℓx,μ,v, expressed as(9)ℓx,μ,v=fx+∑j=1Jμjgjx+∑r=1Rvrhrx.The dynamic update of each queue can be expressed as(10)Qit+1=maxQit−Yit+Zit,∀i∈1,2,…,M.The processing delay of the MEC serverEn scheduling part of the CNN layer to execute the UDm offloading task is modeled as(11)Dmnp=∑k=1KδmnkpnkSmβmnFn.The MEC server is as close as possible to the local equipment to provide computing, storage, and other services. As the transmission distance of the task on the network is shortened, the transmission time and energy consumption can be reduced, and the battery life of the user equipment can be extended [18]; and because the MEC server is deployed locally around the equipment, task data can be distributed to different servers for processing, which greatly reduces the network transmission pressure and server computing pressure [19, 20]. For the fixed strategy π, the Bellman equation is defined as(12)Vπs=Rs,a+γ∑s′∈SPss′aVπs′,where s represents the current state, after performing action a in state s, the state will be converted to the new state s′.The optimal strategy corresponding to the optimal value function is defined as(13)π∗s=argmaxa∈A∑s′∈SPss′aV∗s′.


## 2.2. Principle of Ownership

The development of processing trade expands China’s export trade, and the continuous improvement of the industrial chain and the development of the domestic market make the import substitution effect play out, which promotes the rapid growth of the surplus. Trade and industry complement each other. Industry is the basis of trade [21], and foreign-invested enterprises are an integral part of industry, especially an important part of export industry, which constitutes the main cause of surplus. Trade is the extension of industry, and surplus and foreign enterprise production promote each other and develop together [22]. Most developing countries are on this list, while most developed countries are on the contrary. FDI outflow needs technology and management knowledge, as well as a large amount of foreign exchange reserves, which developing countries do not have. After considering the sales, import and exports of foreign-invested enterprises and adjusting the traditional trade volume, the re-estimation results show that China's trade balance does not show a trade surplus but a large trade deficit [23].


## 2.3. International Trade Balance

The most important feature of a country’s production and trade model of the division of labor in the global value chain is vertical specialized production. In the form of global production segmentation, the production process concentrated in one country or region is divided into different links and distributed around the world. Therefore, there will be many countries participating in production, but most of the products are consumed by one country, which will produce an asymmetry between imports and exports, which will cause a trade surplus/deficit phenomenon [24].In general, when the vertical specialization is low, the trade balance will be small; but when a country’s export trade scale expands rapidly, the total trade surplus will increase significantly. From the perspective of traditional trade statistics, as long as a product is exported from one country to another, its total value is considered as the corresponding trade volume, and the intermediate products will be calculated in this way for many times. Specifically, if a country is at the last point of producing final consumer goods, the value of its exported products includes not only the added value of its own country but also the accumulated value of intermediate goods, which will lead to the amplification of the country’s exports [25, 26].When the input factors are imported, raw materials, the exchange rate changes not only affect the overseas market prices of exported goods but also transmit to the prices of imported factors. To calculate the impact of exchange rate changes on trade, it is necessary to examine the proportion of imported factors in export production. The greater the proportion of production raw materials in export production, the less obvious the promotion effect. That is, if most of the raw materials are imported and the product is completely used for export, then the exchange rate promotion effect is not obvious for the export commodity [27].


## 3. Mobile Edge Computing Algorithm Simulation

### 3.1. Experimental Setup

This paper uses MATLAB simulation tool to verify the effectiveness of the algorithm proposed in this chapter and at the same time to better evaluate the performance of the proposed algorithm. MEC communication system simulation parameters are shown in Table1. Among them, each base station is equipped with an MEC server, and each server is connected to the SDN controller.Table 1
MEC communication system simulation parameters.
ParameterValueNumber of cells4MEC server capacity10 MHzCell radius1.6 kmNumber of cell channels20Macro base station capacity5 MHzSmall cell capacity1 MHzNumber of data subcarriers384Subcarrier bandwidth10.9375 kHzMaximum length of task queue5White noise power−173 dBm/HzDoppler deviation5–25 Hz


### 3.2. Edge Computing Resource Allocation

In order to obtain all data needed to perform the task, each mobile device can exchange data information with its connected base station through the wireless network. This can not only maximize the use of limited wireless resources of MSO but also realize the dynamic macro-control for different user needs, which can guide the dynamic changes of user’s consumption psychology and communication resources to achieve a dynamic balance [28] and make MEC network resource management achieve a dynamic balance. When the demand of users and the supply of network resources reach the balance, the system throughput reaches the maximum [29, 30].


### 3.3. Task Scheduling

After setting the task priority for each task set,n task queues are obtained. Each mobile device can arrange the processors or processors to execute tasks according to the corresponding queue order. However, the number of processors on the edge cloud is limited. If all mobile devices schedule tasks concurrently, there may be conflicts for the processing resources of the edge cloud, and it causes some tasks to finish overtime, which affects the completion of the whole task set, so a centralized scheduling scheme is needed to schedule all tasks reasonably, so that all task sets can be completed between the deadline [31, 32]. On the basis of satisfying the quality of service of users, including considering the user connection and service configuration, the network energy consumption is minimized in a continuous t period by shutting down some servers whose resources are not fully utilized. At the same time, in order to reduce the switching cost of the edge server switch state and ensure the stability of the service, frequent switching of the edge server switch state should be avoided [33, 34].


### 3.4. Statistics of Trade Balance

There is a large surplus in China’s foreign trade, and the surplus is increasing year by year. However, there is an unbalanced distribution pattern of trade balance in China’s foreign trade. The distribution of trade balance of foreign trade partners is not balanced. There is a large annual surplus for some countries and regions, and there is also an annual deficit for some countries and regions [35, 36]. At the beginning, there is a division of labor economy. With the development of specialized production, the degree of international division of labor is increasing due to the effect of experience accumulation, and the trade efficiency is growing endogenously. The international division of labor is further deepened, and the types and quantity of products participating in the international division of labor are greatly increased, so as to realize the upgrading of the trade structure [37]. Before constructing the structural VAR model of BTI, r/w, K/L, and TFP, we need to test its stationarity. Using Eviews5.0 software, ADF test and PP test were performed on the unit root of Bti, R/W, K/L, and TFP time series data [38, 39].


### 3.5. Construction of the Ownership Principle Model

The cross-border trade statistics system based on the territorial principle has defects, which greatly interferes with the authenticity of trade balance statistics. This highlights the importance of ownership adjustment. The statistical adjustment model of ownership trade is a process of reunderstanding the existing world trade forms and patterns based on the principle of ownership statistics [40].


## 3.1. Experimental Setup

This paper uses MATLAB simulation tool to verify the effectiveness of the algorithm proposed in this chapter and at the same time to better evaluate the performance of the proposed algorithm. MEC communication system simulation parameters are shown in Table1. Among them, each base station is equipped with an MEC server, and each server is connected to the SDN controller.Table 1
MEC communication system simulation parameters.
ParameterValueNumber of cells4MEC server capacity10 MHzCell radius1.6 kmNumber of cell channels20Macro base station capacity5 MHzSmall cell capacity1 MHzNumber of data subcarriers384Subcarrier bandwidth10.9375 kHzMaximum length of task queue5White noise power−173 dBm/HzDoppler deviation5–25 Hz


## 3.2. Edge Computing Resource Allocation

In order to obtain all data needed to perform the task, each mobile device can exchange data information with its connected base station through the wireless network. This can not only maximize the use of limited wireless resources of MSO but also realize the dynamic macro-control for different user needs, which can guide the dynamic changes of user’s consumption psychology and communication resources to achieve a dynamic balance [28] and make MEC network resource management achieve a dynamic balance. When the demand of users and the supply of network resources reach the balance, the system throughput reaches the maximum [29, 30].


## 3.3. Task Scheduling

After setting the task priority for each task set,n task queues are obtained. Each mobile device can arrange the processors or processors to execute tasks according to the corresponding queue order. However, the number of processors on the edge cloud is limited. If all mobile devices schedule tasks concurrently, there may be conflicts for the processing resources of the edge cloud, and it causes some tasks to finish overtime, which affects the completion of the whole task set, so a centralized scheduling scheme is needed to schedule all tasks reasonably, so that all task sets can be completed between the deadline [31, 32]. On the basis of satisfying the quality of service of users, including considering the user connection and service configuration, the network energy consumption is minimized in a continuous t period by shutting down some servers whose resources are not fully utilized. At the same time, in order to reduce the switching cost of the edge server switch state and ensure the stability of the service, frequent switching of the edge server switch state should be avoided [33, 34].


## 3.4. Statistics of Trade Balance

There is a large surplus in China’s foreign trade, and the surplus is increasing year by year. However, there is an unbalanced distribution pattern of trade balance in China’s foreign trade. The distribution of trade balance of foreign trade partners is not balanced. There is a large annual surplus for some countries and regions, and there is also an annual deficit for some countries and regions [35, 36]. At the beginning, there is a division of labor economy. With the development of specialized production, the degree of international division of labor is increasing due to the effect of experience accumulation, and the trade efficiency is growing endogenously. The international division of labor is further deepened, and the types and quantity of products participating in the international division of labor are greatly increased, so as to realize the upgrading of the trade structure [37]. Before constructing the structural VAR model of BTI, r/w, K/L, and TFP, we need to test its stationarity. Using Eviews5.0 software, ADF test and PP test were performed on the unit root of Bti, R/W, K/L, and TFP time series data [38, 39].


## 3.5. Construction of the Ownership Principle Model

The cross-border trade statistics system based on the territorial principle has defects, which greatly interferes with the authenticity of trade balance statistics. This highlights the importance of ownership adjustment. The statistical adjustment model of ownership trade is a process of reunderstanding the existing world trade forms and patterns based on the principle of ownership statistics [40].


## 4. Model Simulation Results

### 4.1. Task Cache Effect

For task scheduling algorithm, load is an important performance index. As the average size of tasks participating in the scheduling increases, the system load corresponding to the three algorithms is shown in Figure2. It can be seen from the figure that, for the above three strategies, from the perspective of the algorithm itself, when the average task size reaches 1 MB/job, compared to the other two algorithms, the overall growth of the iccga curve slows down and is at the bottom.Figure 2
System load situation.The impact of different channel transmission rates on equipment energy consumption is shown in Table2. When the task is processed locally, the energy consumption has nothing to do with the channel transmission rate, and the local processing energy consumption appears as a horizontal straight line. As the channel transmission rate increases, the energy consumption curve of the PD-BPSO algorithm and the greedy algorithm decreases. This is because the higher the channel transmission rate, the shorter the time spent on data transmission. When the transmission rate is greater than 2500 kb/s, the trend of PD-BPSO algorithm energy consumption reduction becomes slower and slower, and the energy consumption curve interval of the two algorithms becomes larger and larger. This is because computing resources mainly affect the execution of offloading tasks. The number of particles shows that the computational offloading performance of the proposed algorithm is 48.3% higher than that of the traditional greedy algorithm.Table 2
The impact of different channel transmission rates on device energy consumption.
Transmission rate50010001500200025003000Greedy algorithm15.026.3914.2220.137.1118.02PD-BPSO8.0112.339.3617.245.8511.24Local execution15.0123.359.258.5711.2316.07The relationship between the long-term average power consumption of all user tasks and the system time slot is shown in Figure3. It can be seen from the figure that the maximum data volume of users randomly arriving tasks increases, and the long-term average power consumption of all user tasks and the average user task buffer queue length increase. This is because the maximum data volume of users randomly arriving tasks increases, and users need to process, transmit, and buffer. The amount of task data will increase.Figure 3
The relationship between the long-term average power consumption of all user tasks and the system time slot.Figure4 shows the system utility results of the six algorithms under different task data sizes. According to the comparison between the algorithms, the DTORA algorithm has a better performance in improving the utility of the system and can more fully consider the interests of users and service providers. The performance of QL algorithm is second only to DTORA algorithm. The optimization performance of WRR algorithm ranks third, and its result is much worse than QL algorithm. The experimental results of RR and random algorithm are similar. Since the calculation of system utility considers the improvement of algorithm performance relative to the performance of task processing locally, the system utility of local algorithm is zero. In this experiment, it can be known that the task data size has no significant impact on the system utility value, and the overall optimization effect of the system depends on the performance of the algorithm. With the rapid economic development of a country, the appreciation of its asset prices is expected to attract the favor of international arbitrage capital, and the entry of international hot money will increase the liquidity and change the inflation rate. At the same time, the increase in consumer demand due to the wealth effect will increase the circulation of money and will also cause an increase in liquidity and an increase in inflation.Figure 4
System utility results.The relationship between energy-money conversion coefficient and system currency cost is shown in Figure5. When the system just starts to run, the battery energy gradually accumulates. After the system runs for a period of time, the battery energy stabilizes and does not exceed the battery capacity value. This is because when the system first started running, the battery energy was 0 and it was unable to perform computing tasks. The system captures energy in each time slot, so the battery energy gradually accumulates. When the battery energy can meet the needs of the computing task, the system starts to consume energy to perform the computing task and at the same time to capture energy, so the battery energy changes show a small range of fluctuations. When the channel changes slowly, the LSTM network can effectively predict the trend of channel gain. When the channel changes rapidly, the prediction error will increase. However, regardless of whether the channel is stable or changing rapidly, the gap between the predicted gain and the actual gain in this paper is within 5%. When the bandwidth resource is set to 100 MHz, compared with the algorithm based on game theory and the algorithm based on greedy search, the performance of the iTOA algorithm is improved by about 25% and 45%, respectively.Figure 5
The relationship between energy-money conversion coefficient and system currency cost.


### 4.2. Algorithm Performance Analysis

The influence of edge server computing power on the average service delay is shown in Figure6. When the number of drones increased from 10 to 60, based on the resource allocation algorithm of greedy search, the service delay increased significantly from 20 ms to 46 ms. Based on the resource allocation algorithm of game theory, the service delay increases from 15 ms to 33 ms. The iTOA algorithm proposed in this paper can keep the processing delay low, and the processing delay has been increased from 11 milliseconds to 18 milliseconds. Compared with greedy search and game theory algorithms, when there are 40 UAVs in the system, the average service delay performance of the iTOA algorithm increases by 33% and 60%, respectively. When the edge server has a higher task request, the unloading rate of the iTOA algorithm will decrease, because iTOA balances the computing load on the edge server well. In addition, as the number of service nodes increases, the benefits of these three algorithms also increase. When the number of service nodes becomes larger, the increase speed becomes slower and gradually becomes stable. This is caused by the limitation of node resources. When the number of service nodes is small, the resources are limited and only some users can be served. When more service nodes are deployed, the resources become sufficient, which can provide services to more users and improve the efficiency.Figure 6
The impact of edge server computing power on average service latency.The energy consumption of the four unloading strategies is shown in Table3. It can be seen from the table that the energy consumption is the highest when all tasks are placed on the terminal for calculation, and the energy consumption rises linearly as the amount of tasks increases. Under the condition of limited energy, it is difficult for drones to complete tasks with a large amount of calculation. In order to find an optimal position in terms of delay and energy consumption, the JTO algorithm needs to offload the computing task to the cloud and perform calculations at the terminal at the same time, so the energy consumption will be higher than that of all calculations in the cloud. The algorithm proposed in this paper takes into account the difference between cloud computing and edge computing compared to other strategies in completing the same number of tasks with lower energy consumption. Compared with the JTO strategy, this strategy reduces energy consumption by an average of 24%.Table 3
Energy consumption of four uninstall strategies.
LE2.632.662.592.592.642.672.572.662.582.58CC1.742.791.492.641.241.691.582.043.601.56PTSA2.113.834.482.693.331.441.213.282.810.79JTO0.541.662.822.953.082.801.683.392.512.52Incomplete exchange rate transfer theory analyzes the extent to which changes in exchange rate can be transferred to the price and quantity of import and export commodities, and why the adjustment of import and export prices and import and export quantities of import and export commodities is sometimes very slow when the exchange rate fluctuates greatly. The theory of incomplete exchange rate transfer shows that the impact of exchange rate changes on the balance of payments is limited, and the speed and extent of this impact are restricted by many factors. Changes in the RMB exchange rate and Sino-US trade are shown in Table4. In terms of the Sino-US trade balance, the average annual growth rate reached 28.17%. Except for 1999 and 2001, the rest of the year has maintained a growth rate of more than 10%. Due to the high quality of Chinese products, cheap prices, and adaptation to the needs of the international market, Chinese products are becoming increasingly competitive internationally, leading to a rapid increase in China’s exports to the United States. Second, after the Asian financial crisis, China has increased the export tax rebate rate, and other export encouragement policies have been implemented successively, which stimulated exports. Third, the United States has imposed an embargo on high-tech products against China. Although the value of US exports to China is also increasing, the growth rate is not high, which prevents the United States from exerting its competitive advantages. The combination of these two reasons has led to an increasing trade balance between China and the United States.Table 4
Changes in the RMB exchange rate and Sino-US trade.
100 USD to RMBMonthly importMonthly exportMonthly trade balance810.193018.46822.3653.897809.222320.10623.3073.201808.887820.51824.4293.911808.397318.49122.3803.889807.590016.29920.5144.215806.675017.91121.4053.494804.932413.84017.9274.087803.503915.57120.5264.955The balance of imports and exports under processing trade is shown in Table5. China’s processing trade surplus shows a trend of first rising and then falling. Combined with China’s overall trade balance, the proportion of processing trade surplus is more than 100%, which is the “chief contributor” of the overall trade surplus. In terms of the processing trade surplus, the 16th largest category of products is the main source of the surplus, accounting for an average of 77% of the processing trade balance in recent years. In addition, although the proportion of category 17 products in the balance of processing trade is far less than that of category 16 products, it is also relatively large compared with other products, with a proportion of about 5%.Table 5
Import and export balance under processing trade.
Commodity20062007200820092010201220132014Lump sum1888.82490.82967.32645.73227.973655.283814.023633.78Fifth category−40.87−27.67−39.4−47.27−59.42−92.72−115.66−169.18Sixth category−46.74−63.35−59.86−41.83−56.11−81.96−74.01−89.11Seventh category−71.31−60.25−44.71−32.19−63.94−21.4928.2257.42Eleven categories158.11199.69225.15193.73158.11246.26238.42252.77


### 4.3. Algorithm Energy Consumption Results

The influence of parameters on the calculation time is shown in Figure7. It can be seen from the figure that there are very few SESs in the correlated data set, so the calculation time of the two algorithms is very short. Although under this data set, the BNL algorithm is better than the SG algorithm, but the difference between the two is not much, only in the range of a few milliseconds, and the worst calculation time of the SG algorithm is also less than 8 milliseconds. At the same time, compared with the QWS data set, under the anticorrelated data set, the calculation time of BNL fluctuates greatly. When the number of candidate services is 2400 and the service quality dimension is 6, the calculation time increases from 30 milliseconds to 160 milliseconds. It can be seen that the performance of the BNL algorithm is greatly affected by the data set, while the performance of the SG algorithm is relatively stable. It can be seen that the SG algorithm has better robustness.Figure 7
The influence of parameters on calculation time.The influence of transmission power on the residual power is shown in Figure8. With the increase of the transmission power of the access point, the JOS curve always has the maximum residual energy, which indicates that the joint optimization scheme can make the power utilization rate higher, harvest more power, and consume less power, because JOS can find the best combination of unloading ratio and harvest time. In contrast, FHT has a fixed harvesting time, which determines the energy value of harvesting from the access point. The only thing the optimization process can do is to find the best unloading ratio, so as to complete the task with as little power as possible under the premise of meeting the time constraint. Similarly, FPR has a fixed unload ratio, which will consume a lot of energy if the ratio is not reasonable.Figure 8
Impact of transmission power on the remaining power.The energy efficiency of secure computing and the minimum computing speed of users under different schemes are shown in Figure9. When users have the same maximum transmission power, the energy efficiency of traditional computing bit maximization is lower than that of the proposed scheme. In addition, when the maximum transmission power reaches a certain value, the energy efficiency of traditional secure computing bit maximization begins to decrease. The results also prove the following facts: the increase rate of total computing energy consumption is greater than that of total computing bits, and there is a trade-off between secure computing energy efficiency and secure computing bit. With the increase of user task arrival rate, the average time revenue increases continuously. However, when the arrival rate is greater than 3 kbit/slot, the average time revenue increases slowly. This is because, when the task arrival rate increases, in order to ensure that the queue does not overflow, the processing rate of user computing tasks will increase accordingly, so the system will allocate more resources to serve users, and the average time revenue will also increase.Figure 9
Safe computing energy efficiency and users’ minimum computing rate under different schemes.


## 4.1. Task Cache Effect

For task scheduling algorithm, load is an important performance index. As the average size of tasks participating in the scheduling increases, the system load corresponding to the three algorithms is shown in Figure2. It can be seen from the figure that, for the above three strategies, from the perspective of the algorithm itself, when the average task size reaches 1 MB/job, compared to the other two algorithms, the overall growth of the iccga curve slows down and is at the bottom.Figure 2
System load situation.The impact of different channel transmission rates on equipment energy consumption is shown in Table2. When the task is processed locally, the energy consumption has nothing to do with the channel transmission rate, and the local processing energy consumption appears as a horizontal straight line. As the channel transmission rate increases, the energy consumption curve of the PD-BPSO algorithm and the greedy algorithm decreases. This is because the higher the channel transmission rate, the shorter the time spent on data transmission. When the transmission rate is greater than 2500 kb/s, the trend of PD-BPSO algorithm energy consumption reduction becomes slower and slower, and the energy consumption curve interval of the two algorithms becomes larger and larger. This is because computing resources mainly affect the execution of offloading tasks. The number of particles shows that the computational offloading performance of the proposed algorithm is 48.3% higher than that of the traditional greedy algorithm.Table 2
The impact of different channel transmission rates on device energy consumption.
Transmission rate50010001500200025003000Greedy algorithm15.026.3914.2220.137.1118.02PD-BPSO8.0112.339.3617.245.8511.24Local execution15.0123.359.258.5711.2316.07The relationship between the long-term average power consumption of all user tasks and the system time slot is shown in Figure3. It can be seen from the figure that the maximum data volume of users randomly arriving tasks increases, and the long-term average power consumption of all user tasks and the average user task buffer queue length increase. This is because the maximum data volume of users randomly arriving tasks increases, and users need to process, transmit, and buffer. The amount of task data will increase.Figure 3
The relationship between the long-term average power consumption of all user tasks and the system time slot.Figure4 shows the system utility results of the six algorithms under different task data sizes. According to the comparison between the algorithms, the DTORA algorithm has a better performance in improving the utility of the system and can more fully consider the interests of users and service providers. The performance of QL algorithm is second only to DTORA algorithm. The optimization performance of WRR algorithm ranks third, and its result is much worse than QL algorithm. The experimental results of RR and random algorithm are similar. Since the calculation of system utility considers the improvement of algorithm performance relative to the performance of task processing locally, the system utility of local algorithm is zero. In this experiment, it can be known that the task data size has no significant impact on the system utility value, and the overall optimization effect of the system depends on the performance of the algorithm. With the rapid economic development of a country, the appreciation of its asset prices is expected to attract the favor of international arbitrage capital, and the entry of international hot money will increase the liquidity and change the inflation rate. At the same time, the increase in consumer demand due to the wealth effect will increase the circulation of money and will also cause an increase in liquidity and an increase in inflation.Figure 4
System utility results.The relationship between energy-money conversion coefficient and system currency cost is shown in Figure5. When the system just starts to run, the battery energy gradually accumulates. After the system runs for a period of time, the battery energy stabilizes and does not exceed the battery capacity value. This is because when the system first started running, the battery energy was 0 and it was unable to perform computing tasks. The system captures energy in each time slot, so the battery energy gradually accumulates. When the battery energy can meet the needs of the computing task, the system starts to consume energy to perform the computing task and at the same time to capture energy, so the battery energy changes show a small range of fluctuations. When the channel changes slowly, the LSTM network can effectively predict the trend of channel gain. When the channel changes rapidly, the prediction error will increase. However, regardless of whether the channel is stable or changing rapidly, the gap between the predicted gain and the actual gain in this paper is within 5%. When the bandwidth resource is set to 100 MHz, compared with the algorithm based on game theory and the algorithm based on greedy search, the performance of the iTOA algorithm is improved by about 25% and 45%, respectively.Figure 5
The relationship between energy-money conversion coefficient and system currency cost.


## 4.2. Algorithm Performance Analysis

The influence of edge server computing power on the average service delay is shown in Figure6. When the number of drones increased from 10 to 60, based on the resource allocation algorithm of greedy search, the service delay increased significantly from 20 ms to 46 ms. Based on the resource allocation algorithm of game theory, the service delay increases from 15 ms to 33 ms. The iTOA algorithm proposed in this paper can keep the processing delay low, and the processing delay has been increased from 11 milliseconds to 18 milliseconds. Compared with greedy search and game theory algorithms, when there are 40 UAVs in the system, the average service delay performance of the iTOA algorithm increases by 33% and 60%, respectively. When the edge server has a higher task request, the unloading rate of the iTOA algorithm will decrease, because iTOA balances the computing load on the edge server well. In addition, as the number of service nodes increases, the benefits of these three algorithms also increase. When the number of service nodes becomes larger, the increase speed becomes slower and gradually becomes stable. This is caused by the limitation of node resources. When the number of service nodes is small, the resources are limited and only some users can be served. When more service nodes are deployed, the resources become sufficient, which can provide services to more users and improve the efficiency.Figure 6
The impact of edge server computing power on average service latency.The energy consumption of the four unloading strategies is shown in Table3. It can be seen from the table that the energy consumption is the highest when all tasks are placed on the terminal for calculation, and the energy consumption rises linearly as the amount of tasks increases. Under the condition of limited energy, it is difficult for drones to complete tasks with a large amount of calculation. In order to find an optimal position in terms of delay and energy consumption, the JTO algorithm needs to offload the computing task to the cloud and perform calculations at the terminal at the same time, so the energy consumption will be higher than that of all calculations in the cloud. The algorithm proposed in this paper takes into account the difference between cloud computing and edge computing compared to other strategies in completing the same number of tasks with lower energy consumption. Compared with the JTO strategy, this strategy reduces energy consumption by an average of 24%.Table 3
Energy consumption of four uninstall strategies.
LE2.632.662.592.592.642.672.572.662.582.58CC1.742.791.492.641.241.691.582.043.601.56PTSA2.113.834.482.693.331.441.213.282.810.79JTO0.541.662.822.953.082.801.683.392.512.52Incomplete exchange rate transfer theory analyzes the extent to which changes in exchange rate can be transferred to the price and quantity of import and export commodities, and why the adjustment of import and export prices and import and export quantities of import and export commodities is sometimes very slow when the exchange rate fluctuates greatly. The theory of incomplete exchange rate transfer shows that the impact of exchange rate changes on the balance of payments is limited, and the speed and extent of this impact are restricted by many factors. Changes in the RMB exchange rate and Sino-US trade are shown in Table4. In terms of the Sino-US trade balance, the average annual growth rate reached 28.17%. Except for 1999 and 2001, the rest of the year has maintained a growth rate of more than 10%. Due to the high quality of Chinese products, cheap prices, and adaptation to the needs of the international market, Chinese products are becoming increasingly competitive internationally, leading to a rapid increase in China’s exports to the United States. Second, after the Asian financial crisis, China has increased the export tax rebate rate, and other export encouragement policies have been implemented successively, which stimulated exports. Third, the United States has imposed an embargo on high-tech products against China. Although the value of US exports to China is also increasing, the growth rate is not high, which prevents the United States from exerting its competitive advantages. The combination of these two reasons has led to an increasing trade balance between China and the United States.Table 4
Changes in the RMB exchange rate and Sino-US trade.
100 USD to RMBMonthly importMonthly exportMonthly trade balance810.193018.46822.3653.897809.222320.10623.3073.201808.887820.51824.4293.911808.397318.49122.3803.889807.590016.29920.5144.215806.675017.91121.4053.494804.932413.84017.9274.087803.503915.57120.5264.955The balance of imports and exports under processing trade is shown in Table5. China’s processing trade surplus shows a trend of first rising and then falling. Combined with China’s overall trade balance, the proportion of processing trade surplus is more than 100%, which is the “chief contributor” of the overall trade surplus. In terms of the processing trade surplus, the 16th largest category of products is the main source of the surplus, accounting for an average of 77% of the processing trade balance in recent years. In addition, although the proportion of category 17 products in the balance of processing trade is far less than that of category 16 products, it is also relatively large compared with other products, with a proportion of about 5%.Table 5
Import and export balance under processing trade.
Commodity20062007200820092010201220132014Lump sum1888.82490.82967.32645.73227.973655.283814.023633.78Fifth category−40.87−27.67−39.4−47.27−59.42−92.72−115.66−169.18Sixth category−46.74−63.35−59.86−41.83−56.11−81.96−74.01−89.11Seventh category−71.31−60.25−44.71−32.19−63.94−21.4928.2257.42Eleven categories158.11199.69225.15193.73158.11246.26238.42252.77


## 4.3. Algorithm Energy Consumption Results

The influence of parameters on the calculation time is shown in Figure7. It can be seen from the figure that there are very few SESs in the correlated data set, so the calculation time of the two algorithms is very short. Although under this data set, the BNL algorithm is better than the SG algorithm, but the difference between the two is not much, only in the range of a few milliseconds, and the worst calculation time of the SG algorithm is also less than 8 milliseconds. At the same time, compared with the QWS data set, under the anticorrelated data set, the calculation time of BNL fluctuates greatly. When the number of candidate services is 2400 and the service quality dimension is 6, the calculation time increases from 30 milliseconds to 160 milliseconds. It can be seen that the performance of the BNL algorithm is greatly affected by the data set, while the performance of the SG algorithm is relatively stable. It can be seen that the SG algorithm has better robustness.Figure 7
The influence of parameters on calculation time.The influence of transmission power on the residual power is shown in Figure8. With the increase of the transmission power of the access point, the JOS curve always has the maximum residual energy, which indicates that the joint optimization scheme can make the power utilization rate higher, harvest more power, and consume less power, because JOS can find the best combination of unloading ratio and harvest time. In contrast, FHT has a fixed harvesting time, which determines the energy value of harvesting from the access point. The only thing the optimization process can do is to find the best unloading ratio, so as to complete the task with as little power as possible under the premise of meeting the time constraint. Similarly, FPR has a fixed unload ratio, which will consume a lot of energy if the ratio is not reasonable.Figure 8
Impact of transmission power on the remaining power.The energy efficiency of secure computing and the minimum computing speed of users under different schemes are shown in Figure9. When users have the same maximum transmission power, the energy efficiency of traditional computing bit maximization is lower than that of the proposed scheme. In addition, when the maximum transmission power reaches a certain value, the energy efficiency of traditional secure computing bit maximization begins to decrease. The results also prove the following facts: the increase rate of total computing energy consumption is greater than that of total computing bits, and there is a trade-off between secure computing energy efficiency and secure computing bit. With the increase of user task arrival rate, the average time revenue increases continuously. However, when the arrival rate is greater than 3 kbit/slot, the average time revenue increases slowly. This is because, when the task arrival rate increases, in order to ensure that the queue does not overflow, the processing rate of user computing tasks will increase accordingly, so the system will allocate more resources to serve users, and the average time revenue will also increase.Figure 9
Safe computing energy efficiency and users’ minimum computing rate under different schemes.


## 5. Conclusions

Based on the theory of international industrial transfer and the analysis of the rules and characteristics of global international industrial transfer, this paper studies the dynamic changes of international industrial transfer and Sino US trade balance. China’s large trade surplus has not brought China corresponding economic benefits and social welfare. In the long run, it will not only waste China’s resources but also be detrimental to the sustainable development of China’s economy and the improvement of its economic quality. China must strategically change the status quo of China’s foreign trade and the large low-quality trade surplus.As a key technology in the future 5 g mobile communication, MEC realizes the marginalization and localization of computing, communication, and cache resources, deploys various network functions on the access network side, accelerates various contents in the wireless network, and provides nearby services, which greatly reduces the delay of network service delivery. In the actual situation, the economic cost of processing tasks is also an important indicator for people to measure the switching decision, so we also consider the task execution cost.From the model empirical point of view, whether it is a single-country ownership trade adjustment model or a bilateral trade ownership adjustment model, it has accomplished the task of adjusting the trade volume very well. The MEC architecture uses intensive deployment of base stations and edge servers in places close to users and sinks the computing and storage capabilities of the cloud to provide services closer to user equipment. Therefore, while reducing the backbone network load, the network delay is reduced and the user experience is improved. However, when a large number of edge servers are kept on, it will inevitably lead to energy waste in many edge servers that are underutilized during low traffic periods.



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*Source: 5569833-2021-03-20.xml*