Chapter 1 Introduction
1.5 Thesis organization
The rest of this paper is organized as follows. In Chapter 2, we introduce the basic concept of SVM. In Chapter 3, we review an existing prediction-based real-time resource provisioning scheme for MMOGs. We present the proposed SVM-based load prediction scheme in detail in Chapter 4. In Chapter 5, we show simulation results that include comparison with neural
VM VM VM VM VM VM VM VM VM Figure 1: Resource allocation for each region [13].
network-based load prediction [13] in terms of prediction accuracy. In Chapter 6, we conclude this paper and outline future work.
Chapter 2 Preliminaries
2.1 SVM
The support vector machine (SVM) was developed by Vapnik (1995) [1]. The main idea of SVM comes from the binary classification, namely to find a hyperplane as a segmentation of the two classes to minimize the classification error. The maximum margin hyperplane gives the maximum separation between decision classes. The training data are closest to the maximum margin hyperplane; it is called support vectors [1]. SVM analyzes data and recognize patterns for classification and regression analysis (i.e. time sequence analysis).
SVM concentrates on applying regression to short-term electricity load forecasting. Its forecasting accuracy outperforms other forecasting models. SVM has good performance on reducing the outliers and improves the prediction accuracy efficiently. The SVM parameters are defined as follows: , where Xi is a training vector, Yi represents a class. Figure 2 shows the SVM hyperplane and the support vector w [9].
}, 2
2.2 The Applications of SVM
SVM can be applied to some applications, such as flood forecasting, power prediction [18], image recognition (large dataset), three-dimensional classification, text classification, and time series prediction[1] [18].
2.3 Pros and cons comparison of the proposed SVM-based and neural network-based load prediction
Table I shows the pros and cons between SVM-based and the NN-based load prediction.
For the SVM-based load prediction, it has low performance with a small amount of data. The NN-based load prediction has slow training speed, complicated neural nodes, and high
Figure 2: SVM hyperplane and the support vector [9].
Table I. Pros and cons comparison of the proposed SVM-based and neural network-based load prediction
Approach Pros Cons
SVM-based (proposed)
• Fast training speed[18]
• Easy to implement [1] [18]
• Optimal solution [17]
• Low performance with a small amount of data
Neural network (NN)-based [5]
[16]
• Accepting different input data formats [15]
• Ambiguous inference ability [16]
• High resource consumption [15]
• Slow training speed [15]
• Complicated neural node
Chapter 3
Related Work
3.1 Neural network-based load prediction
In [5], it proposed a neural network (NN) prediction-based method for dynamic resource provisioning and scaling of MMOGs in distributed grid environments. The load prediction method predicts the future game world entity distribution from history trace data by using the neural network-based method. It developed generic analytical game load models used to forecast the future hot-spots that congest the game servers and make the overall environment fragmented and unplayable [5]. Finally, a resource allocation service performs dynamic load distribution, balancing, and migration of entities that keep the game servers reasonably loaded such that the real-time QoS requirements are maintained[5].
3.2 Qualitative comparison of proposed SVM-based with neural network-based load prediction
The NN-based scheme [5] bases neural network to foresee future hot-spots to allocate resources for the grid MMOG. However, it did not propose any mechanism to allocate resources and had low prediction accuracy to predict the MMOG load. The above problems motivate us to design a more efficient SVM-based load prediction for MMOGs. We propose an efficient resource allocation mechanism to predict the load level of each game region and
proposed SVM-based load prediction scheme will be described in Chapter 4. In Table II, the prediction technique indicates the operation type of the prediction [5] [16]. The fairness indicates that the same number of VMs is allocated to each game region at first. The prediction accuracy indicates the fraction of prediction correctness for each load prediction scheme.
Table II. Qualitative comparison of the proposed SVM-based with neural network-based load prediction.
Chapter 4
Proposed SVM-based Load Prediction for Resource Allocation
4.1 SVM-based resource allocation architecture for MMOGs
The proposed SVM-based resource allocation architecture for MMOGs is shown in Figure 3.
We obtain MMOG player history datasets from a character server. In a monitor server, there are three main components preprocessing module, SVM-based prediction module, and resource assignment module. The first component, preprocessing module, calculates the
SVM-based prediction
Resource Assignment MMOG player history dataset
Character server
Preprocessing
Monitor server Resource
Allocation Datacenter
Figure 3: Proposed SVM-based resource allocation architecture for MMOGs.
players in MMOGs. After load prediction, we allocate the resources to each region in the datacenters of the MMOG cloud.
4.2 Preprocessing
MMOG loads are dynamic because of the player interaction [8]. Thus, accurate and fast load prediction is necessary to dynamically allocate resources for MMOGs. In our MMOG cloud system, we obtain MMOG player history datasets from the character server. Figure 4 shows the preprocessing procedure and the MMOG player datasets format. The detailed preprocessing procedure is described as follows:
Preprocessing procedure
1) Read MMOG player history datasets from the character server.
2) Count the number of players (NP) in each region at each time interval.
3) Calculate the loading class of each region.
The formula of estimating the loading level of region j at time interval i, Lj :
ij The loading state of a region can be: overloading, underloading, or normal.
Overloading: LijTu
(4)
Underloading: LijTl
(5)
Normal: Tl LijTu
(6)
In addition the loading class of a region is either: class 1 or class 2. Class 1 means the loading state is overloading or underloading and has the value +1 in SVM. Class 2 means the loading state is normal and has the value -1 in SVM. The actions taken by each region based on its loading class is as follows:
If the loading class of a region is class 1, then the number of VMs in this region should be adjusted,
else if the loading class of a region is class 2, then the number of VMs in this region does not need to be adjusted.
Figure 5 shows the preprocessing procedure for deriving the loading class of each region j at time interval i.
START
Count the number of players in each region
at each time interval
(b): MMOG player datasets format for recording the number of players in each region at each time interval.
Figure 4: Preprocessing procedure and the MMOG player datasets format.
.
4.3 SVM-based load prediction
In this section, we describe the proposed SVM-based load prediction for MMOG resource allocation. As shown in Figure 6, the SVM-based prediction bases on the loads in
For each region j (j =1~56), derive the load (Lij ) of region j at time interval i
Tl ≦Lij ≦ Tu
Region j at the interval i belongs to class 2
Region j at the time interval i belongs to
class 1 No
End A
Figure 5: Preprocessing procedure for deriving the loading class of each game region j at time interval i.
transformed to the SVM format, as shown in section 2.1. Then resources can be assigned to each region based on predicted loading class. The flowchart of the SVM-based prediction and resource assignment is shown in Figure 7. Table III summarizes MMOG load levels and their associated SVM-based loading classes.
tn+1
tn
tn-1
t1 Time
SVM-based Prediction
OUT
IN
. . .
Figure 6: SVM using the loads in previous time intervals to predict the future load of a region.
Transform the loading class of each
region into SVM format
Train the history game datasets in
SVM format
Adjust the number of VMs in this region
Figure 7: Flowchart of the proposed SVM-based load prediction and resource assignment.
4.4 Resource assignment
Based on the results of load prediction, we propose the rule of resource assignment to add or release VMs in each region. To allocate resources in an MMOG cloud efficiently, we assume that the VMs in the cloud have the same specification. The resource assignment objectives are to meet the QoS, such as the response time requirement of players and to reduce the waste of resources. We adjust the number of VMs used in region j at time interval i+1 (NVij), as follows:
NVi+1j = NPij / MAX_LOADvm (7)
Note that NPij is number of players in region j at time interval i and MAX_LOADvm is the maximum number of players in the VM.
Table III. MMOG loading level and its SVM category.
MMOG load level Load level
range SVM category Resource
assignment type
Overloading LjTu Class 1 Adjustable
Normal Tl LjTu Class 2 Unchanged
Underloading LjTl Class 1 Adjustable
Chapter 5
Performance Evaluation
In this chapter, we first describe simulation setup and evaluation metrics. Then, we compare the proposed SVM-based load prediction with the NN-based load prediction [5] in terms of prediction accuracy.
5.1 Simulation setup and evaluation metrics
We ran libsvm[1] for SVM-based load prediction. SVM related simulation parameters are shown in Table IV. MMOG running environment setting are as follows:
(1) We ran the libsvm [1] software package for MMOG load prediction in Intel 2.67 GHz CPU, 4GB, Windows 7 OS.
Table IV. Simulation parameters [1,19].
Parameter Value
Kernel type Radius Basic Function
Gamma 125
Degree 2
Scale -1 ~ 1
Label 1-12 (time interval)
Index 1-56 (region number)
5.1.1 Prediction accuracy
The prediction accuracy is defined as the number of correct game region load predictions divided by the total number of game region load predictions which is expressed as follows:
(7)
5.2 Comparison between SVM-based and NN-based load prediction
As shown in Figure 8, the proposed SVM-based load prediction scheme achieves 12.24% and 26.77% better prediction accuracy than the NN-based load prediction and the last value-based load prediction, respectively. The proposed SVM-based load prediction reduces 8% and 78.3% of the number of VMs needed compared with the neural network-based load prediction and the last value-based load prediction, respectively.
s
Chapter 6 Conclusion
6.1 Concluding remarks
In this thesis, we have presented an efficient SVM-based load prediction scheme for resource allocation in MMOG clouds. We use historic load data of players in each region to predict the future load. By using the load prediction results, we can allocate (or release) an
Figure 8: Prediction accuracy comparison of the three load prediction schemes.
0
1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016
Prediction accuracy (%)
Time
Last value-based
SVM-based (Proposed) NN-based
SVM-based load prediction reduces 8% of the number of VMs used compared with the neural network-based load prediction. With a higher prediction accuracy and a smaller number of VMs used, our SVM-based load prediction is feasible for efficient resource allocation in MMOG clouds.
6.2 Future work
We will integrate the proposed SVM load prediction scheme to an efficient resource allocation method for MMOG clouds. In addition, we will combine a fuzzy membership function to each input datum of SVM and design a fuzzy-based SVM load prediction scheme to further increase prediction accuracy.
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