Mobile Microlearning, a novel fusion form of the mobile Internet, cloud computing, and microlearning, becomes more prevalent in recent years. However, its high deployment and operational costs make energy saving in cloud become a concerning issue. In this paper, to save energy consumption, a resource deployment approach to cloud service provision for Mobile Microlearning is proposed. Chinese Lexical Analysis System and Dynamic Term Frequency-Inverse Document Frequency (D-TF-IDF) are adopted to implement resource classification. Resources are deployed to the 2-tier cloud architecture according to the classification results. Grey Wolf Optimization (GWO) algorithm is used to forecast real-time energy consumption per byte. The simulation results show that, compared to traditional algorithm, the classification accuracy of small sample categories was significantly improved; the forecast energy consumption value and the standard values are 7.67% in private cloud and 2.93% in public cloud; the energy saving reaches 2.22% to 16.23% in 3G and 7.35% to 20.74% in Wi-Fi.
Millions of people are participating in Mobile Microlearning and the number of students who are enrolled in a single course at the same time can be as high as tens of thousands [
Introducing cloud computing into microlearning to solve the obstacles in the process of Mobile Microlearning by high computing power and huge storage of cloud is a promising method. However, current researchers focus on deploying the system to the existing cloud platform [
An effective way to solve energy consumption problem is to offload computing and data intensive tasks from private cloud with poor resource to public cloud with rich resource. With the collaboration of private cloud and public cloud, the goal of low energy consumption can be accomplished in a certain extent, but it also faces some problems. We mainly describe these problems of Mobile Microlearning process in mobile environment, Mobile Microlearning users, and service migration. In the wireless mobile communication network, there are some limitations for private cloud platforms to access public cloud platforms, such as long delay time, poor stability, and predictability, which will make the environment of wireless mobile communication network have poly clusters, fluctuations, and other nonstationary characteristics. At the same time, mobile terminal users are advanced creatures with rich thinking awareness, so their requests are usually personalized. For example, when the battery is low, some users may want to get the best application performance, while others are willing to sacrifice some application performance for longer standby time. Therefore, the relationship between benefits and costs in Mobile Microlearning should be accurately measured. In addition, in the service migration process, the private cloud platform and the public cloud platform will inevitably generate many times of information exchange and transmission. The device status and network environment may change during the execution of the application, which will further increase wireless traffic instability, bandwidth consumption, latency, network congestion, and cloud downtime.
Aiming at users’ dynamic and personalized demands for Mobile Microlearning, it is particularly important to research how to effectively deploy rich and colourful learning resources in the cloud platform so as to provide low-cost, efficient, and continuous cloud services to mobile users. Based on this consideration, we propose a new resource deployment approach to cloud service provision for Mobile Microlearning in this paper.
Firstly, we propose a resource deployment framework for Mobile Microlearning, which consists of classification module, 2-tier cloud architecture module, and GWO forecast module. The classification module and the 2-tier cloud architecture module achieve the resources classification and deployment, and the GWO forecast module can find the server with the lowest energy consumption cost which will provide users with energy-saving services. Secondly, we propose a D-TF-IDF algorithm, which reduces the influence of the uneven distribution of training set on the classification accuracy of test set. Finally, we propose a green cloud resource deployment method for Mobile Microlearning based on the framework, and the simulation results prove the superiority.
The rest of the paper is organized as follows. Related works in Section
Microlecture mobile learning system (MMLS) allows learners to access microlecture videos and other high-quality microlecture resources wherever and whenever they like [
Although the low cost and ease of use of mobile cloud computing have great potential for mobile cloud learning, it still faces the heavy load of large-scale user application calls. Rapid growth of the demand for computational power by scientific, business, and web applications has led to the creation of large-scale data centres consuming enormous amounts of electrical power [
The above research provides a good idea for cloud resource allocation in the process of Mobile Microlearning. For the rich Mobile Microlearning resources and highly personalized customer demand, this paper focuses on data processing, data storage, and service migration in the process of Mobile Microlearning, to research to energy consumption of Mobile Microlearning by referring to the previous research results. The D-TF-IDF algorithm is used to improve the classified accuracy. The GWO algorithm and 2-tier cloud architecture are used to deploy resources. On a service basis, we minimize the cost of energy consumed during the resource deployment.
In this section, we first briefly describe fundamental method that we will use in the Mobile Microlearning framework, and then we introduce the resource deployment framework for Mobile Microlearning based on cloud in detail.
Known as one of the most effective keyword extraction technologies, the Term Frequency-Inverse Document Frequency algorithm (TF-IDF algorithm) is proposed by Saltond [
The Grey Wolf Optimizer algorithm (GWO algorithm, for short) is a new biological heuristic algorithm that is inspired by the strict hierarchy and hunting process of natural grey wolves [
Position updating in GWO.
In Figure
According to the social hierarchy of grey wolf, we know the hunting (optimization) is guided by three wolves,
In this paper, cloud is introduced into Mobile Microlearning environment, and cloud-based resource deployment framework is built. In the process of cloud services providing, different resource allocation strategies determine different service performance, while users have the same request and response processes for cloud services. Therefore, the process of users requesting service resources in this paper is referred to in the literature [
Resource deployment framework of Mobile Microlearning based on cloud.
First of all, the processing capability of the cloud data centre is not unlimited, so the system needs to collect the user’s microlearning request, and the request is temporarily stored in the database of user requests. Secondly, to remove meaningless words and find the vocabulary set that is the most representative of the user’s request features, the system uses Institute of Computing Technology, Chinese Lexical Analysis System 2013(ICTCLAS2013) [
In this section, we first describe the sorting process of a large number of learning resources in Mobile Microlearning and deploy these resources using 2-tier cloud architecture. Then, we study the low energy consumption strategy and formulate the energy consumption problem in the Mobile Microlearning process.
In the classification module, there are two main reasons affecting the smooth completion of Mobile Microlearning. Firstly, with the advent of the era of big data, the learning resources of Mobile Microlearning are increasingly huge. Therefore, it is difficult for users to find the requested learning resources from the massive resource pool. Secondly, with diversified background of Mobile Microlearning users, cloud platform cannot accurately obtain users’ real requirements. The above reasons will lead to the inconsistency between the services provided by cloud platforms and the content requested by users, which will affect the satisfaction of users of Mobile Microlearning and increase the energy consumption in the process of Mobile Microlearning. Therefore, it is very important to extract the key information of content effectively from a large number of mobile learning resources and improve the classification accuracy. The classification module is shown in Figure
The classification module.
Many researchers believe that words are independent and meaningful elements of language component. For a resource, we can see it as a collection of words sequence when we ignore meaningless symbols. More importantly, keywords are considered by the public as the words that best embody the main ideas of the text. Therefore, we can use keywords as features of resource classification. Firstly, we use the category homogenizing method to process the training set. Secondly, because of its higher segmentation accuracy and analysis speed, we use ICTCLAS2013 segmentation system to classify learning resources. Thirdly, we use the Stop Words table to process the segmentation results. Its main purpose is to eliminate meaningless words and make keywords more representative. Fourthly, we count the frequency of each word appearing in each category and establish a keyword database. Finally, we use D-TF-IDF to calculate the weight of the keywords. This process involves two important methods: category homogenization and D-TF-IDF method. Next, we will describe the working process of these two methods in detail.
Due to the characteristics of resources distribution, resources of some category are relatively scarce. As a result, some keywords cannot accurately represent the classification of resources, which affects the accuracy of classification. So, the small sample categories are reorganized to form a new training set that is as balanced as possible, which is called the category homogenizing method. For example, training set is
The above process achieves training set preprocessing. However, for the resource deployment framework proposed in this paper, we focus on the research of classification accuracy, because it is the primary condition for deploying resources to 2-tier cloud modules and modelling energy consumption. In order to obtain the optimal classification accuracy that is relatively fair to both large and small sample categories, we proposed an improvement on the traditional TF-IDF method by dynamically adjusting
Dynamically adjusting
In summary, the classification accuracy of
Finally, we get the average classification accuracy of the entire sample set by formula (
In the 2-tier cloud architecture module, we deploy Mobile Microlearning resources to the public cloud platform or the private cloud platform according to
The schematic diagram of the 2-tier cloud architecture module is shown in Figure
Resource deployment on 2-tier cloud module.
The main function of GWO module is to take the network environment and equipment capacity into account to find the server with the minimum energy consumption, and the server will complete the resource deployment process. In order to make the model proposed in this paper better reflect the network environment and device capability, the module needs the cloud platform to randomly generate probe bytes to simulate the user request process. However, in this process, in order to avoid the generation of extra energy consumption due to the large detection byte, we use 1 byte as the detection byte. Since the energy consumption of 1 byte is too small to measure and transmit, we can embed the probe byte into the network packet and add tags to the network packet. Then, the system only needs to track the network packet and its round-trip energy consumption, and its average value is the energy consumption of the probe byte. Next, we will use GWO algorithm to carry out the server optimization process.
We assume the number of request is
The energy consumption of the services is generated randomly according to changes in the supply and demand relationship. Due to the instability of the equipment state and network environment in the real cloud platform, it may cause the cloud platform generate higher energy consumption when it completes the mobile learning resource deployment process. So, we define a variable
We simulate and formulate the hunting process of grey wolf. The energy consumption of Mobile Microlearning in
In the process of optimization, our goal is to find the server with the least energy cost. Therefore, we need to find the minimum
In order to describe the general characteristics of the optimal value simply and directly, we define
In earlier researches, the execution rate was predetermined, which had certain limitations, because it could not truly reflect the network environment and equipment. In order to make the research closer to the real operating environment of Mobile Microlearning, the task execution rate is set according to the classification module in this paper.
According to the training set of the classification module, we get the total byte size of the training set. It is defined in formula (
At the same time, we get the byte size of the keywords, which are extracted from the training set. It can be defined as formula (
The process in which the cloud platform provides the service to Mobile Microlearning users mainly divides into two kinds of situations. The first situation is the ideal resource deployment process. In this case, the private cloud can satisfy all requests of all Mobile Microlearning users. The second situation is the nonideal resource deployment process. Under the case, the private cloud platform needs to perform service migration process to public cloud platform, which will result in additional energy consumption.
The time cost in ideal situation is shown in formula (
Therefore, the ideal learning energy consumption is described as formula (
It is well known that if a user request is not answered for a long time, then this will seriously affect the user’s Mobile Microlearning quality. To solve this problem, private cloud sets a time threshold based on experience. If the private cloud platform is unable to provide the required service for Mobile Microlearning users within the time threshold, the system model proposed in this paper will automatically cut off the service opportunity of the private cloud platform and execute the service migration process. Therefore, the time threshold can effectively alleviate the high energy consumption caused by the user’s excessive time consuming private resources. Therefore, the time cost of Mobile Microlearning in nonideal cases is shown in formula (
In practice, the bandwidth state, network status, and device status (such as CPU load) of the cloud platform may be different [
Energy consumption in nonideal case can be defined as formula (
For resources stored in private or public clouds, users can obtain the required services through collaboration between the two platforms. Therefore, on the premise of guaranteeing the service quality of Mobile Microlearning, the energy consumption of Mobile Microlearning process is shown in formula (
To verify the validity of the method, we will evaluate the performance of the proposed method from three aspects, the classification accuracy of D-TF-IDF, forecast accuracy of GWO algorithm, and energy consumption of Mobile Microlearning process. The experimental parameters and experimental results are as follows.
The experimental data set is corpus of Fudan University [
We conduct word segmentation and word frequency statistics on the training set, to build the word frequency table. After preprocessing the data set, we can get 20185 words. Of these words, the 20 words with the highest frequency were extracted as keywords for the text. The weight of each keyword in 20 categories is calculated by TF-IDF. Parts of the results are shown in Tables
The results of the large sample category obtained by TF-IDF (part).
word | C3 | C7 | C11 | C19 | C31 | C32 | C34 | C38 | C39 |
---|---|---|---|---|---|---|---|---|---|
0 | 0 | 0.99 | 0 | 0 | 0.01 | 0.01 | 0 | 0 | |
JOURNAL | 0 | 0 | 0.37 | 0.48 | 0.09 | 0.06 | 0.06 | 0 | 0 |
OF | 0 | 0 | 0.22 | 0.40 | 0.30 | 0.07 | 0.07 | 0 | 0 |
1999年 | 0 | 0 | 0.16 | 0.23 | 0.27 | 0.13 | 0.13 | 0 | 0.01 |
0.06 | 0 | 0.07 | 0.09 | 0.52 | 0.09 | 0.09 | 0 | 0.02 | |
0.02 | 0 | 0.19 | 0.01 | 0.75 | 0 | 0 | 0 | 0 | |
0 | 0 | 0.49 | 0 | 0.43 | 0.06 | 0.06 | 0 | 0 |
The results of the small sample category obtained by TF-IDF (part).
word | C4 | C5 | C6 | C15 | C16 | C17 | C23 | C29 | C35 | C36 | C37 |
---|---|---|---|---|---|---|---|---|---|---|---|
0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | |
JOURNAL | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
OF | 0.01 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
1999年 | 0.09 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.11 | 0 | 0 |
0.04 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.08 | 0 | 0 | |
0.01 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.02 | |
0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
It can be seen from Tables
In order to make the classified accuracy of small samples and large samples relatively fair,
The results of the large sample category obtained by D-TF-IDF (part).
| C3 | C7 | C11 | C19 | C31 | C32 | C34 | C38 | C39 |
---|---|---|---|---|---|---|---|---|---|
0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
2 | 0.89 | 0.89 | 0.94 | 0.62 | 0.80 | 0.89 | 0 | 0.52 | 0.43 |
2.2 | 0.93 | 0.83 | 0.94 | 0.90 | 0.88 | 0.92 | 0.31 | 0.73 | 0.67 |
2.4 | 0.94 | 0.76 | 0.92 | 0.93 | 0.91 | 0.90 | 0.77 | 0.82 | 0.77 |
2.6 | 0.94 | 0.71 | 0.87 | 0.94 | 0.91 | 0.84 | 0.90 | 0.85 | 0.81 |
2.8 | 0.95 | 0.64 | 0.85 | 0.95 | 0.91 | 0.75 | 0.95 | 0.83 | 0.82 |
The results of the small sample category obtained by D-TF-IDF (part).
| C4 | C5 | C6 | C15 | C16 | C17 | C23 | C29 | C35 | C36 | C37 |
---|---|---|---|---|---|---|---|---|---|---|---|
0 | 0.41 | 0.70 | 0.8 | 0.55 | 0.54 | 0.37 | 0.68 | 0.83 | 0.78 | 0.81 | 0.92 |
2 | 0 | 0.10 | 0.04 | 0.21 | 0.18 | 0.11 | 0.24 | 0.53 | 0.48 | 0.47 | 0.57 |
2.2 | 0 | 0.07 | 0.02 | 0.21 | 0.14 | 0.04 | 0.24 | 0.51 | 0.40 | 0.40 | 0.39 |
2.4 | 0 | 0.05 | 0.02 | 0.24 | 0.11 | 0.04 | 0.15 | 0.44 | 0.36 | 0.36 | 0.28 |
2.6 | 0 | 0.04 | 0.02 | 0.21 | 0.04 | 0.04 | 0.12 | 0.41 | 0.27 | 0.32 | 0.18 |
2.8 | 0 | 0 | 0 | 0.18 | 0 | 0 | 0.06 | 0.37 | 0.30 | 0.30 | 0.15 |
As can be seen from Table
In order to better display the effect of
The average classification accuracy of different categories.
Figure
Figures
The classification accuracy of large sample category.
The classification accuracy of small sample category.
From Figures
In order to bring the experiment closer to the real dynamic cloud environment, we use the GWO algorithm to forecast the energy consumption in the current environment. Because 1 byte energy consumption is too small to measure, we predict the energy consumption of 1kb data in the current environment. The experimental parameters are as follows:
Figures
Energy consumption of private cloud processing 1kb data in 3G.
Energy consumption of public cloud processing 1kb data in 3G.
Figures
Energy consumption of private cloud processing 1kb data in Wi-Fi.
Energy consumption of public cloud processing 1kb data in Wi-Fi.
At the same time, in the 20 experiments, the variance between forecast values in four cases is 9.70515E-07, 3.06072E-06, 1.11715E-07, and 1.29163E-06, respectively, which proves the stability of the forecast value. Therefore, we believe that these forecast values can really reflect energy consumption in Mobile Microlearning.
For the energy consumption of Mobile Microlearning resource deployment, the experimental results are mainly compared with the traditional algorithm proposed in [
As shown in Figure
Energy consumption in 3G.
As shown in Figure
Energy consumption in Wi-Fi.
In order to better verify the influence of environment on Mobile Microlearning, we compared the energy consumption of this algorithm in 3G and Wi-Fi environments. The experimental results are shown in Figure
Energy consumption of the proposed algorithm in different environment.
Figure
Energy consumption of proposed method in Wi-Fi and 3G.
Based on the green cloud service provisioning framework [
Meanwhile, in order to find the optimal classification accuracy for both large samples category (popular information) and small samples category (less popular information), we use category homogenization method and D-TF-IDF method, respectively. Second, a 2-tier cloud architecture is adopted to deploy resources according to the classification results of the classification module. Third, GWO module forecasts the energy consumption of each byte in current network environment and finds the server with the lowest energy cost. For the new user request, the classification module tries its best effort to get the highest classification accuracy. The 2-tier cloud architecture, through the collaboration between private cloud and public cloud, mitigates the drawbacks of the overload operation of a single cloud platform. On the other hand, it increases the probability that users find resources in private cloud and reduces the energy consumption brought by service migration. The experimental results show that the method we proposed can improve classification accuracy and achieve the purpose of saving energy. However, this algorithm can only make a rough estimation of resource classification and energy consumption. Therefore, in the future research work, the important modules involved in the Mobile Microlearning process need to be deeply processed to achieve the overall energy-saving treatment in the Mobile Microlearning process.
The corpus data used to support the findings of this study have been deposited in the Fudan University repository (http://nlp.fudan.edu.cn/).
The authors declare that they have no conflicts of interest.
This work was supported in part by the National Natural Science Foundation of China (NSFC) under Grants no. 61602155, no. U1604155, no. 61871430, and no. 61370221, in part by Henan Science and Technology Innovation Project under Grant no. 174100510010, and in part by the Industry University Research Project of Henan Province under Grant no. 172107000005.