This document presents research on using machine learning models to predict resource usage for web applications in cloud computing environments. It aims to develop a prediction model that can forecast future resource needs to enable timely provisioning of virtual machines. The researchers evaluate support vector regression (SVR), neural networks, and linear regression using workload data from the TPC-W benchmark. The results show that SVR achieved more accurate predictions of CPU utilization, throughput, and response time compared to the other models, with errors reductions of up to 80%. This suggests SVR may be best for predicting resource usage in non-linear systems like multi-tier web applications.

1. Proactive Prediction Models for Web
Application Resource Provisioning in the Cloud
_______________________________
Samuel A. Ajila & Bankole A. Akindele

2. Presentation Outline
 Introduction to Problem Area
 Motivation
 Goals and Scope
 Contributions
 Related work
 Machine learning algorithms
 Implementation setup
 Evaluation metrics
 Selected results
 Conclusion
2

3. Introduction
 Cloud computing refers to both the applications delivered
as services over the Internet and the hardware and
systems software in the data centers that provide those
services (SaaS, PaaS and IaaS)
 Challenges include: Data security threats, performance
unpredictability and prompt (quick) resource scaling
 Accurate Virtual machine (VM) resource provisioning:
• under and over-provisioning
 Current techniques like Control theory, Constraint
programming and Machine learning
3

4. Motivation
 VM instantiation (scaling) takes time and ranges from 5 to 12
minutes
 Challenges that can result from the instantiation duration:
• Possibility of Service Level Agreement (SLA) violations -
Cloud providers
• Poor customer’s Quality of Experience (QoE) – Cloud client
• Reputation loss – Cloud client/providers
 Presently, monitoring metrics made available to clients are
limited to CPU, Memory and Network utilization
 The motivation is to “predict resource usage so that cloud
providers can make adequate provisioning ahead of time”
 To extend the monitoring metrics by including Response time
and Throughput 4

5. Goals and Scope
 Design and develop a cloud client prediction model for cloud
resource provisioning in a Multitier web application
environment
 The model would be capable of forecasting future resource
usage to enable timely VM provisioning
 To achieve this goal, SVM, NN and LR learning techniques are
analysed using the Java implementation of TPC-W (workload)
 The scope of this work is limited to IaaS
 Prediction model is built around the web server tier. It is
possible to extend to other tiers
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6. Contributions
 Design and development of a cloud client prediction model that
uses historical data to forecast future resource usage
 The evaluation of the resource usage prediction capability of
SVM, NN and LR using three benchmark workloads from TPC-W
 The extension of the prediction model to include Throughput
and Response time, thus providing wider and better scaling
decision options for cloud clients
 The comparison of the prediction capability of SVM, NN and LR
models under random and steady traffic patterns
6

7. Related works
Table 1: Auto-scaling techniques
7
Technique Class Comments
Threshold/Rule
based
Reactive Reacts to system changes but do not
anticipate them
Control theory Reactive/Proactive Except where used with a proactive
approach it suffers reactive issues
Queuing theory Reactive As complexity of system grows, the
analytic formulas become difficult
Reinforcement
learning
Reactive/Proactive Good but converging at optimal
policy can be unfeasibly long (state
–action pairs)
Time series analysis Proactive Good and promising

8. Machine learning algorithms
Linear Regression
 The linear regression model has the form: 𝑓 𝑋 = 𝛽0 + 𝑗=1
𝑝
𝑋𝑗 𝛽𝑗 (1)
 𝑋𝑗 is the input data
 𝛽0 and 𝛽𝑗’s are the unknown parameters estimated from the training
data. Estimation is done using the least squares method.
 The coefficients 𝛽 are picked to minimize the residual (difference
between the actual and predicted value) sum of squares :
(𝑦𝑖 − 𝑓 𝑋 )2
𝑦𝑖 is the actual value
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9. Machine learning algorithms (cont’d)
Neural Network
 A two-stage regression or
classification model represented
by a network diagram
 Derived features 𝑍 𝑚 are created
from linear combinations of the
input after which the target 𝑌𝑘 is
modeled as a linear combination
of 𝑍 𝑚
 Like Linear regression, unknown
parameters called weights are
sought to make the model fit the
training data well
9
Figure 1 : Single hidden layer, feed forward neural network

10. Machine learning algorithms (cont’d)
Support Vector Regression
 The goal of SVR is to find a function 𝑓 𝑋 that has at most 𝜀 (the
precision by which the function is to be approximated) deviation from
the actual obtained target for all training data
 Mathematically, 𝑓 𝑋 = 𝑤. ∅ 𝑥 + 𝑏
 Input data 𝑥 is mapped to higher dimensional feature space via the
kernel function, then a linear regression is performed
 Goal is to find optimal weights 𝑤 and threshold 𝑏
10

11. Architecture
11Figure 2 Implementation architecture

12. Implementation setup (cont’d)
Time (minutes) 1-7 56-63 154-161 350-357 490-497 504-511
Shopping mix
users
84 168 16 180 248 160
Browsing mix
users
52 112 36 320 192 160
Ordering mix
users
52 108 28 224 268 160
Total user
Requests
188 388 80 724 708 480
12
 Total length of experiment was about 10 hours
 Selected experimental workload mix

13. Evaluation metrics
13
 Evaluation is done on the 60% training and 40% held out test
dataset
 The held out dataset is used to forecast to a maximum interval
of 12 minutes (VM instantiation time reported by other
authors)
Performance metrics and their calculations
Metric Calculation
MAPE (Mean Absolute
Percentage Error)
1
𝑛
| 𝑎𝑖−𝑝𝑖|
𝑎𝑖
𝑛
𝑖=1 where 𝑎𝑖 and 𝑝𝑖 are the actual and predicted values
respectively
RMSE (Root Mean Square
Error)
√
( 𝑎𝑖−𝑝𝑖) 2𝑛
𝑖=1
𝑛
MAE (Mean Absolute Error) 1
𝑛
|𝑝𝑖 − 𝑎𝑖|𝑛
𝑖=1
PRED 25 No. of observations with relative error ≤ 25% / No. of observation

14. CPU Training & Testing Performance
14
CPUUtilizationTrainingandTestingPerformanceMetricforLR,NN,andSVR
LR(LinearRegression)Model MAPE RMSE MAE PRED(25)
LRTraining 113.31 14.70 11.11 0.51
LRTesting 36.19 22.13 15.98 0.36
NN(NeuralNetwork)Model MAPE RMSE MAE PRED(25)
NNTraining 105.63 14.08 9.48 0.59
NNTesting 50.46 31.08 19.82 0.34
SVR(SupportVectorRegression)Model MAPE RMSE MAE PRED(25)
SVRTraining 107.80 15.48 10.09 0.64
SVRTesting 22.84 11.84 8.74 0.64

15. Selected results (cont’d)
15
CPU utilization test performance metric
Model MAPE RMSE MAE PRED(25)
LR 36.19 22.13 15.98 0.36
NN 50.46 31.08 19.82 0.34
SVR 22.84 11.84 8.74 0.64
Figure 5 CPU Utilization Actual and Predicted test model results

16. Selected results (cont’d)
16
Throughput test performance metric
Model MAPE RMSE MAE PRED(25)
LR 24.62 3.72 2.87 0.63
NN 38.90 6.12 4.46 0.47
SVR 22.07 3.22 2.41 0.67
Figure 6 Throughput Actual and Predicted test model results

17. Selected results (cont’d)
17
Response time test performance metric
Model MAPE RMSE MAE PRED(25)
LR 12.35 1.39 1.11 0.91
NN 17.84 2.02 1.64 0.75
SVR 9.92 1.21 0.87 0.93
Figure 7 Response time Actual and Predicted test model results

18. CPU - Comparison of Prediction Models
18

19. Throughput – Comparison of Prediction
Models
19

20. Sensitivity Measurement Using Little’s Law
20
Data Consistency Measurement Using Little’sLaw
Time (minute) 1-7 56-63 154-
161
350-
357
490-
497
498-
503
504-
511
Average total user
requests
27
(188/7)
55
(388/7)
11
(80/7)
103
(724/7)
101
(708/7)
95
(664/7)
69
(480/7)
Average Throughput
(Requests/second)
5.29 10.52 2.47 15.92 10.00 12.23 13.27
Average time spent
(seconds)
5.10 5.23 4.45 6.47 10.01 7.77 5.2
Measured time spent 4.66 4.92 9.92 7.53 9.06 9.01 9.45
Time variance (%) 9.44 6.31 55.14 14.08 10.49 13.76 44.97

21. Conclusion
 SVR displayed superior prediction accuracy over both LR and
NN in a typically nonlinear, not defined a-priori workload by:
 In the CPU utilization prediction model, SVR
outperformed LR and NN by 58% and 120% respectively
 For the Throughput prediction model, SVR again
outperformed LR and NN by 12% and 76% respectively;
and finally,
 The Response time prediction model saw SVR
outperforming LR and NN by 26% and 80% respectively.
 Based on this experimental results SVR may be accepted as
the best prediction model in a nonlinear, not defined a-priori
system
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22. Future works
 SVR and other machine learning algorithms are good for
forecasting, however
• training and retraining is a challenge
• Parameter selection is still empirical
 Combination of SVR and other predicting techniques may
mitigate this challenge
 Other future direction include
• Inclusion of database tier for a more robust scaling
decision
• Resource prediction on other non-web application
workload
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23. Questions
23
Thank You