Chapter 2 Background
2.7 Comparison of different approaches qualitatively
In Table I, we compare the proposed RP-AFK with other approaches. Although WoW logs off and disconnects players when players are AFK for 30 minutes and 60 minutes, respectively, it causes AFK players to re-login and still wastes resources for the first 30 minutes. Remind that related work did not take AFK players into consideration when allocating resources.
Table I: Comparison of different approaches qualitatively. Architecture Multi-server Multi-server Cloud gaming Cloud gaming
Resource
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Chapter 3
Proposed Resource Provisioning Considering AFK Players
3.1 Resource provisioning considering AFK players
We propose an efficient resource provisioning scheme for MMOGs considering AFK players (RP-AFK) in cloud gaming. We leverage the characteristic of AFK players, which only need low QoS for gaming, to decrease the provision of resources (e.g., frame rates) to them and to support more players. The flowchart of the proposed RP-AFK is showed in Figure 3 and the details of the RP-AFK are described in the following.
1) Read historical game data that include total numbers of players and AFK players of a WoW realm in Taiwan.
2) Predict the total numbers of players and AFK players using a neural networks predictor based on historical game data.
3) Predict the number of VMs required at each time step during time to time (time period).
4) Find the maximum number of VMs required ( at the next time period.
Note that we need to create new VMs in advance to prevent the existing VMs from overloading.
5) Compare with the number of VMs used currently. If is equal to the number of VMs used currently, go to step 8. If is less than the
number of VMs used currently, go to step 6. Otherwise, go to step 7.
6) Close the VMs that aren’t required. Then go to step 8.
7) Create extra VMs required. Then go to step 8.
8) Collect current game data and store in historical game database.
3.2 Game data collection
We use WoW as a case study and describe how we collected game data that include the number of AFK players. The details of our game data collection method are described in the following.
1) Create an AddOn for WoW.
2) Use Who List to search online players by class and zone because Who List only Figure 3: The flowchart of the proposed RP-AFK.
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returns 50 avatars in one request.
3) Add the online players searched to our friend list because API UnitIsAFK only works for friends.
4) Use API UnitIsAFK to verify whether the online players are AFK.
5) Output the total numbers of players and AFK players to the chat window.
6) Use API LoggingChat to log chat data to a file (historical game database).
In MMOGs, players are usually AFK in towns or cities because they will not be attacked by oppositional players or monsters. We collected the total numbers of players and AFK players in six cities of a WoW realm in Taiwan, as shown in Figure 4. We found that most of players are staying and AFK in a city, called Orgrimmar.
Therefore, we just collected game data in this city.
Figure 5 shows the total numbers of players and AFK players in Orgrimmar. The number of AFK players is showed daily with variations and is about 25% of the total
Figure 4: The total numbers of players and AFK players in six cities of a WoW realm in Taiwan.
11:05 19:42 04:19 12:57 21:34 06:11
Number of players
number of players. That is, if there is more number of total players, there will have more number of AFK players so as to save more cloud resources. We collected game data from 2012/03/10 to 2012/5/20 and predicted the total numbers of players and AFK players in the near future by using a neural network-based predictor.
3.3 Neural network-based predictor
The neural network-based predictor provides a robust and highly accurate approach to approximating a real target function [20]. A neural network-based predictor with a time series input is showed in Figure 6 [21]. The network has 20 network inputs and one network output. We input the number of players from time step 1 to time step 20 and predict the number of players at time step 21. The neural network has two hidden layers. Hidden layer A and hidden layer B have 20 neurons Figure 5: The total numbers of players and AFK players in a city, Orgrimmar, of a
WoW realm in Taiwan.
11:05 04:19 21:34 14:49 08:03 01:18
Number of players
Time (03/10 11:05~03/14 07:12) Total players AFK palyers
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and 10 neurons, respectively. Each layer’s neuron performs calculations based on the following equations: neural network-based predictor to predict the total numbers players and AFK players.
Then, we predict the number of VMs required by the following equation:
( )
Figure 6: A neural networks-based predictor for numbers of total players and AFK players.
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Chapter 4
Evaluation Results
4.1 Experimental setup
For evaluation, game data including the total numbers of players and AFK players were collected by using our game data collection method, as described in Chapter 4. We used MATLAB [22] as a prediction tool to predict the total numbers of players and AFK players in the near future. The evaluation period was from 2012/03/10 to 2012/5/20. To evaluate the number of VMs used, we assumed one VM can support 31 avatars [23] and VM startup time is 10 minutes [24]. If players are AFK, we reduced their frame rates to 8 FPS, which is the lowest rate supported in WoW. Experimental parameters are summarized in Table II.
Table II: Experimental parameters.
Game data From WoW
Prediction technique Neural network
Prediction tool MATLAB API (nntool)
Evaluation period 2012/03/10 11:05 ~ 2012/5/20 18:30
VM capacity 31 avatars [23]
VM startup time 10 minutes [24]
AFK player’s frame rate 8 FPS (lowest rate supported in WoW)
4.2 Comparison of three prediction methods
We compared the neural network-based prediction method with the last value and moving average prediction methods. We aimed to minimize the prediction error (PE) which was calculated by the following equation [20]:
∑ | |
∑
where is the actual number of players at time step and is the predicted number of players at time step . A prediction method with the lower PE represents the better prediction accuracy.
The PEs of the above three prediction methods are showed in Figure 7. Since the neural network-based predictor has high prediction accuracy, the PE of the neural
Figure 7: Prediction errors (PEs) of the three prediction methods.
6.41
Neural network Last value Moving average
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network-based prediction method is much lower than that of moving average prediction and that of last value prediction.
4.3 The percentage of extra players supported considering AFK players
We evaluated the percentage of extra players supported considering AFK players.
Experimental results show that the average percentage of extra players supported for RP-AFK is 11.04%, and it is almost equal to the average percentage of extra players supported based on the actual game data,, as shown in Figure 8.
Figure 8: The number of extra players supported considering AFK players.
27.83
Percentage of extra players supported (%)
RP-AFK (prediction + considering AFK players) Actual game data (considering AFK players only)
4.4 The average usage of VMs in one zone of a WoW realm
We also evaluated the usage of VMs in one zone of a WoW realm. Figure 9 shows RP-AFK used one less VM for 46.05% of the observed time period and used two less VMs for 1.75% of the observed time period. It supports that our scheme can save cloud resources by decreasing the provision of resources to AFK players.
4.5 Under-allocation and over-allocation
Finally, we evaluate the under-allocation and over-allocation of RP-AFK.
Under-allocation means that the assignment of the number of players is more than the number of players that VMs can handle [20] and over-allocation means that the number of VMs allocated is higher than the number of VMs needed [25]. Figure 10 shows the under-allocation of RP-AFK is slightly higher than the under-allocation of resource allocation based on actual game data. However, the over-allocation of
Figure 9: The average usage of VMs in one zone of a WoW realm.
46.05
One less VM Two less VMs One more VM
Percentage of observed time (%)
RP-AFK (prediction + considering AFK players) Prediction only
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RP-AFK is smaller than the over-allocation of resource allocation based on actual game data.
Figure 10: Under-allocation and over-allocation.
2.66
Percentage of observed time (%)
RP-AFK (prediction + considering AFK players) Actual game data (considering AFK players only)
Chapter 5 Conclusions
5.1 Concluding remarks
We have presented an efficient resource provisioning scheme considering AFK players (RP-AFK) for MMOG clouds. We leverage the characteristic of AFK players, which only need low QoS for gaming, to decrease the provision of resources to them and to support more online players. We used WoW as a case study to illustrate the proposed resource provisioning scheme that is valuable to MMOG cloud providers.
The proposed RP-AFK scheme can support 11.04% extra players and use one less VM for 46.05% of the observed time in a game zone of a WoW realm. Therefore, our scheme can reduce the waste of cloud resources and increase resource utilization by supporting more online players.
5.2 Future work
In our current resource provisioning scheme, we focused only on number of players. In the future, we will consider players’ interaction in our resource provisioning scheme. In addition, we will implement our proposed resource provisioning scheme considering AFK players in an experimental MMOG cloud.
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