Dynamic mobile cloud offloading prediction based on statistical regression

1 Introduction

Smart devices like mobile phones and tablets are getting increasingly popular nowadays because of the rapid advancements in mobile and wireless communication technologies. These devices are equipped with lots sensors that assist the smart life of today’s younger generation. Along with this, the applications which can be used are also progressing in a dramatic way. Now the mobile applications are coming in abundance in various categories [6]. Gaming, virtual reality and social media applications are also being increasingly added and used in these devices. The data storage and processing power of these devices are also increasing.

These sophisticated and resource hungry applications are making our smart devices perform much weaker than what we expect. Even though the technology is advancing, the battery life and processing capacity is still a headache for the users. For example, if a person who is using the Smart phone is travelling constantly the charging of battery becomes a major concern for him. For some of the users, Quality of Experience (QoE) of the application is important, which they may not get with a resource poor device. In order to find a solution for these problems, the mobile devices can be augmented with cloud technology using Mobile Cloud Computing (MCC) [10]. In MCC, the smart phones are allowed to access the cloud resources to augment with the mobile capacity and make the application less energy consuming or less time consuming or both. Mobile Cloud Offloading is a technique, where the energy and time consuming tasks of the smart devices can be offloaded to the cloud for execution. Since the network access now is abundant everywhere, due to the Smart City Project of the Government, it can be used for connectivity between the cloud and the device. Technologies like Wi-Fi or cellular connectivity like 3G/4G/5G which offer fast connectivity can be used for the connectivity [5]. Due to the high bandwidth a low latency connection can be obtained with these connections.

Although a good number of offloading methods are available in literature, they do not consider the energy consumption in smart devices which happens while offloading. They also do not consider the dynamic choice from the user and what optimization they want. In our proposed work, the user decides whether he want time efficiency, energy efficiency or both depending on the current context. A decision for offloading is taken based on this choice. Regression method is used in this work and this result in reduced energy consumption and increased accuracy of the decision making. If a wrong decision of offloading is taken, it will eventually affect the QoE of the user and in turn the time and the energy.

In this paper we propose the following.

We introduce an architecture where a light weight decision algorithm based on regression is selected to reduce the burden on the mobile devices.

This paper is organized in six sections. Section 2 describes related works, Section 3 is dealing with the proposed architecture, Section 4 deals with the regression technique used, Section 5 describes a real time application, Section 6 deals with the results and Section 7 gives the conclusion and finally Section 8 lists down the references.

2 Related works

The technique of offloading from a resource limited smart device to the unlimited resource cloud has been studied extensively in literature. Several frameworks [7] have been proposed and several techniques have been introduced [9]. Generally these can be classified as what, when, where and how to offload.

In what to offload scenario, a decision is made on which part of the application is offloaded to the cloud. Is it the whole application or parts of the application after partitioning is offloaded? In the decision of when to offload, the offloading decision is taken on the basis of at what time an offloading decision can be taken. The context information is considered and an offloading decision is taken based on this information [11]. If the situation is not favorable a local execution decision is taken. In the offloading decision of where to offload, the consideration is on whether to offload to the nearby cloudlets or distant cloud or if multiple clouds are available, which one should be selected [8]. Finally in how to offload decision, a decision on how are we going to offload, which network to select, which cloud to select and on what context to offload is considered.

The cloudlet [3] is a nearby computational resource which can be used for offloading. It is a miniature representation of a distant cloud, with the advantage of fast connection speed and low latency. Because of these advantages, we will get energy efficient and time efficient processing if a local cloudlet is available.

Clone Cloud [2] is a framework, where the code is partitioned before execution and a linear programming is used for solving. A set of input is generated and the partitioning is based on this input. When an input scenario comes, the most appropriate partition is taken from the already generated set. Since the application can take any inputs, this method lacks the flexibility and is not dynamic. Think Air [1] uses method level offloading. It predicts the next execution time from the previous observations. The main disadvantage of this is that it is not flexible with dynamic changes in the context. If the context varies largely, the averaging doesn’t take that context changes into consideration. CADA [4] is dealing with a context aware offloading algorithm. The movement of the mobile device is also taken into consideration in CADA. It considers the regular pattern of the user and takes an offloading decision based on that. If the user violates the regular pattern, CADA may take a wrong decision. The dynamic network changes and other dynamic behavior are not taken into consideration.

MAUI [13] works on the principle of code annotations. Offloading decision is taken based on these annotations which are added with the method. An annotation can be clubbed with the source code, which is similar to the metadata information. These annotations allow easy identification of the resource intensive tasks that can be offloaded to the cloud. This can be added with the help of the program developer. Once the annotations are made, it converts in the form of offloadable format. Techniques similar to RMI can be used to invoke methods from the cloud. Device dependent dynamic adaptations cannot be executed with this method. The issue of scalability is also a disadvantage of this approach since it requires a specific server to be attached to the mobile device which acts as the cloud server.

Our proposed work is dealing with offloading decision making using regression. A dynamic metric selection is also included in this work. The user selects the mode in which he wants to do the operation and based on this a metric is selected. A metric called ERWS [15] is introduced to evaluate the energy and time together. The selection of the weightage parameter is based on the remaining battery charge of the device.

3 System architecture and problem formulation

In offloading architecture, the computationally intensive parts of the mobile application are run at the cloud server. We assume that the smart devices are always connected to the internet via cellular connection like 3G/4G/5G or a high speed Wi-Fi connection. The intensive parts are executed at the cloud server and the results are sent back to the mobile devices for integration. The main goals of the architecture are as follows.

a) Adaptation to dynamic network changes:

The connected network is heterogeneous and the properties may change dynamically. The network bandwidth is a dynamic factor, based on which the offloading decision may change. If the bandwidth is too low, the decision of offloading is local execution and depending on the bandwidth value, parts which can be offloaded may change. Instead of two classes, four classes can be offloaded, if enough bandwidth is available.

b) Adaptation to dynamic properties of the device:

The next feature which will affect the offloading decision is the properties of the mobile device. Depending on the context information the offloading decision may change.

Real time application (OCR application) is explained in Section 5. c) Quality of Experience of the user:

The QoE of the user is the measure of how well his criterion is getting satisfied by the application. Based on the above three scenarios, the user will get the optimization of his choice and hence the QoE will be improved.

Offloading is not always beneficial. The metrics associated with mobile execution and for cloud execution is compared and a decision is taken for offloading. The following are the criteria to be considered.

Offloading is beneficial only if it takes less time or energy or both, depending on the metrics under consideration and it should consider all dynamic factors which affect offloading. The decision of offloading will deteriorate the system if the context is not favorable.

Considering all these limitations offloading decision can be considered as a decision making problem, which is highly dynamic, low biased and tolerant to the noise. As this works on a mobile device, the classifier should be light weight, adaptive and should be self learning. It should also consume less energy and time.

The decision algorithm assesses a processing task and determines the location for execution that would maximize energy conservation and minimize execution time on the mobile device. The trade-offs between local and remote execution influence the decision made. In other words, if local computation cost is larger than communication cost, offloading is advised and vice versa.

The decision engine selects the most energy saving task execution option. For this, the context data is parsed and processed for fine tuning the decision of local or remote execution. Following parameters are considered for decision making which is obtained from the context database. < Bandwidth, Battery Charge, CPU load at mobile side, CPU load at cloud side>

A comparison of different classification algorithm is given in Table 1. The time, energy and accuracy vary slightly. Even though SVM and MLP has higher accuracy ratio, the time and energy consumed for regression is less. In this paper the classification technique used is Multiple Logistic Regression. SVM is having high computational burden which is not desirable for this system because time and energy are constraints for our mobile device. This disadvantage can be overcome by LS-SVM, which solves linear equations rather than quadratic programming. The independent variables are the above tuple from data set and the dependent variable is the decision of local/remote execution. Since logistic regression takes categorical data, it is suitable for offloading decision. The two categories of decision are local or remote execution.

Advantage of regression analysis is that it is simple and less time and energy consuming. From the previous data which is available in the data set, the classifier will predict the fate of current execution. Table 1 shows the energy, time and accuracy for regression analysis compared to other classification and prediction techniques. Compared to Threshold, SVM (Support Vector Machine), MLP (Multilayer Perceptron) and decision tree, regression takes less time and less energy. But classification accuracy is slightly lower than some of the other methods. As the offloading decision should be executed in the resource limited smart phones, time and energy are the major factors and hence regression analysis is selected for classification.

Based on the collected information in the dataset, the classifier makes the decision on either to offload the method or to execute it locally. The classifier has a feedback channel so that it can learn by itself through the entire decision process.

To make an offloading decision, it is important to calculate estimated time and energy for local and remote execution for computationally intensive tasks. We use statistical regression technique to find the time of execution and energy utilization. Following are the variables that we are calculating. Energy and time can be calculated from the following Equation (10). E i or T i = m ( X ) + ɛ(10)

Where, m (X) is a function of all the context information such as bandwidth, battery charge, CPU utilization of mobile and cloud and data to be transferred. ɛ is the residual factor, which is caused due to any un-modeled parameters. To predict the value of m(X)for local execution time, we use a weighted model. The equation used for weighted model is given below Equation (11). T m ( m ( x ) ) = ∑ i = 1 n W i ( x ) * t i ∑ i = 1 n W i ( x )(11)

Where n is the number of previous observation to be considered, W _i  (x) is the weight of each observation and t _i is the ith local execution time. In our experiments, i value is taken as 10. 10 closest observations are considered for the calculation of a new value. For W _i  (x) a weight is given as 1 to the latest value, 0.9 to the previous value and so on.

Similarly the residual factor ‘ɛ’ can be calculated as Equation (12). T m ( ɛ ) = ∑ i = 1 n W i ( x ) * ɛ i ∑ i = 1 n W i ( x )(12)

Prediction Accuracy:

For the statistical regression model the predicted execution time and energy can be compared with the actual execution time and energy. This will give a better view of the prediction accuracy. The prediction accuracy for time and energy can be calculated using the following equations Equations (13, 14). Time Accuracy = { 1 - | T p - T a | max ( T p , T a ) } × 100 %(13)Energy Accuracy = { 1 - | E p - E a | max ( E p , E a ) } × 100 %(14)

Where, T _p is the predicted execution time, T _a is the actual execution time, E _p is the predicted energy and E _a is the actual energy.

Consider a real world scenario of a traveller. He is going to a foreign country, where he does not know the language. For translating the local shop boards he can use a translator application which will convert the local language into English. For this application the translation engine takes majority of the computations and if it is done at the local device the battery will be drained quickly and the response time will be larger. So the translator engine can be offloaded to the cloud.

Depending on the context of the user mobile, he can have three choices of operation. In the first choice, if he is nearer to the hotel and battery charging is possible in a short period of time, the application choice can be taken as offloading with faster response time. In this case energy consumption is not considered. The next choice is, he doesn’t have a choice of battery recharging, and he can select an offloading technique which saves energy. In this case response time is not taken into consideration. A dynamic choice can be taken from the user based on the mode he wants the operation to be offloaded. ‘Is it energy efficient or time efficient or both’? The last choice is saving both energy and time. That is a trade-off between the execution energy and response time can be done and a decision of offloading can be taken if both saving energy and time is possible. The following Fig. 3 shows the decision making algorithm.

Figure 4 shows the OCR application architecture. The image is taken using the phone camera and it is forwarded to the cloud for character recognition and translation. The translated text is sent back to the device for display. Figure 5 shows a sample OCR picture.

6 Numeric results and discussions

The implementation of the decision making and partitioning algorithms uses a Gionee V4S Smart phone (Low End) and Samsung Galaxy S4 (high End). The server is installed on apache tomcat and Open-Shift cloud server. The applications are randomly executed and time and energy are calculated. Power Tutor is used for measuring the power consumption of the applications. A real world scenario described in the Section 5 is used for evaluation. The OCR application will convert the images of the sign boards to English text.

Figures 6 and 7 shows the comparison of our regression technique with some existing technologies. The time and energy increases as the file size increases. But compared to other methods, the regression technique decreases the execution time and energy in all cases. Figures 9 and 10 shows the energy and time saving depending on the bandwidth changes. If the bandwidth is too low, there is no energy and time savings. In such cases, local execution will give much better results. But as the bandwidth increases the advantages also increases. In high bandwidth cases the energy and time saving due to offloading is much higher. So the decision of offloading can be selected for high bandwidth cases and local execution in low bandwidth cases.

Figure 8 shows the prediction accuracy compared to the previously existing technologies. In all cases, regression shows better prediction accuracy. This is because regression considers all the important parameters related to offloading and takes the previous 10 values and gives weightage to them properly. The major factors are bandwidth, CPU load at the mobile and the cloud. All these factors make the prediction accuracy of our system much better than other technologies. Similarly for CPU load and saving energy and time, as the CPU load of the cloud increases the energy and time saving decreases. If the cloud is heavily loaded, instead of offloaded execution, the application can be run locally to obtain good performance.

7 Conclusion

The proposed method makes offloading decision based on regression analysis. Since regression analysis is simple and will take only less time and energy it can be easily adopted for mobile environment. It takes into consideration all the important parameters which affect the decision. More accurate prediction accuracy and energy and time saving can be obtained by this method. In future, more applications can be taken into consideration. More real time applications can be used to verify the results.