Software aging prediction – a new approach

International Journal of Electrical and Computer Engineering (IJECE)
Vol. 13, No. 2, April 2023, pp. 1773~1781
ISSN: 2088-8708, DOI: 10.11591/ijece.v13i2.pp1773-1781  1773
Journal homepage: http://ijece.iaescore.com
Software aging prediction – a new approach
Shruthi Parashivamurthy1
, Nagaraj Girish Cholli2
1
Department of Computer Science and Engineering, Global Academy of Technology, Bengaluru, India
2
Department of Information Science and Engineering, RV College of Engineering, Bengaluru, India
Article Info ABSTRACT
Article history:
Received Feb 16, 2022
Revised Sep 16, 2022
Accepted Oct 13, 2022
To meet the users’ requirements which are very diverse in recent days,
computing infrastructure has become complex. An example of one such
infrastructure is a cloud-based system. These systems suffer from resource
exhaustion in the long run which leads to performance degradation. This
phenomenon is called software aging. There is a need to predict software
aging to carry out pre-emptive rejuvenation that enhances service
availability. Software rejuvenation is the technique that refreshes the system
and brings it back to a healthy state. Hence, software aging should be
predicted in advance to trigger the rejuvenation process to improve service
availability. In this work, the k-nearest neighbor (k-NN) algorithm-based
new approach has been used to identify the virtual machine's status, and a
prediction of resource exhaustion time has been made. The proposed
prediction model uses static thresholding and adaptive thresholding methods.
The performance of the algorithms is compared, and it is found that for
classification, the k-NN performs comparatively better, i.e., k-NN showed an
accuracy of 97.6. In contrast, its counterparts performed with an accuracy of
96.0 (naïve Bayes) and 92.8 (decision tree). The comparison of the proposed
work with previous similar works has also been discussed.
Keywords:
Algorithm
Machine learning
Prediction
Rejuvenation
Software aging
This is an open access article under the CC BY-SA license.
Corresponding Author:
Shruthi Parashivamurthy
Department of Computer Science and Engineering, Global Academy of Technology
Bengaluru, India
Email: shrutip@gat.ac.in
1. INTRODUCTION
The performance degradation caused by software aging has hit various types of computing systems
including virtualized cloud systems [1], web servers [2], [3], clusters [4] and online transaction processing
systems [5]. The software aging concept has also impacted spacecraft systems [6] and military systems [7].
The impact may be loss of life in critical applications. Software aging happens because of unreleased file
handles, data corruption, memory fragmentation, memory leaks, storage space fragmentation and round-off
error accumulation. Software aging reduces the performance of cloud-based systems because of the
complexity with which they are built. The system consists of servicing components and an execution
environment. The system’s boundary separates it from its environment, but its services are towards the
surrounding environment [8]. In complex systems like the cloud, various levels like application level or
operating system are prone to software aging [9]. Operating system-level effects are non-released memory,
file handles, and sockets. Application-level effects include non-terminated threads, round-off errors, or data
file corruption. It is very important to estimate the optimal time to trigger the rejuvenation to mitigate the
software aging effects in cloud systems. Researchers have attempted to predict the time of software aging
which can be seen in the previous works. Researchers have used threshold-based, statistics-based and
machine learning approaches to estimate the software aging time. It is possible to predict the failure time of

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the system using machine learning algorithms. The various aging indicators used to estimate resource
exhaustion include memory and central processing unit (CPU) usage [10].
In this work, software aging prediction has been made using a new approach wherein virtual
machine’s current resource utilization status is fed to a machine learning model that classifies the virtual
machines as healthy, aging-prone and aged using the k-nearest neighbor (k-NN) algorithm based new
method. Static thresholding and adaptive thresholding methods have been used for aging prediction. Once the
virtual machines are classified, rejuvenation is to be initiated for aging-prone and aged virtual machines. The
rejuvenation process cleans up the system’s internal state and brings the system back to its original state by
removing the accumulated errors. The time when the rejuvenation is initiated is called rejuvenation trigger
time. The time to trigger the rejuvenation has been forecasted using the new method in this work.
2. RELATED WORK
Based on the type of algorithm used, machine learning approaches are categorized into two types:
classification approaches and regression approaches. In the classification method, the system status is
classified as either stable or unstable. Forecasting of system failure can be done using a regression method.
The procedure has been explained in [10]. Yan and Guo [11] developed a mechanism that forecasts software
aging using a machine learning algorithm. Data was collected from a live commercial web server and the
collected data was pre-processed. To identify a subset of the model parameters set, a feature selection
algorithm was applied. A time series model was used for the prediction of selected parameters. To predict
software aging, the model was built using machine learning algorithms. Sensitivity analysis was done to
analyze how heavily outcomes depend on input variables. IIS webserver was used to apply the method.
Experiment results were analyzed and found that the proposed method predicts software aging in the early
phase of the system development life cycle.
Alonso et al. [12] performed a comparison of various regression algorithm families like linear
regression, regression trees and hybrids. The researchers compared these algorithms in various scenarios and
various aging concepts involved. The outcome of the experimentation indicated that phenomena performed
better in the hybrid version i.e., MP5 between linear regression and decision tree. Due to the bugs in the
software like unreleased threads or memory leaks, resource exhaustion was caused leading to aging
phenomena. The model included linear piecewise models (i.e., a reasonable number of linear patches)
capturing various aging slopes and speeds. In one of the previous works, three machine learning algorithms
were used along with time series models for the prediction of software aging in web applications [13]. The
three machine learning algorithms used are decision trees, naïve Bayes classifier and neural network model.
The researchers built the models relating several system variables to aging trends like throughput and number
of connections. This was based on the observation that aging phenomena can be approximated by making use
of the piecewise linear model. The models in this work were trained using samples of data that were collected
in preliminary experiments. The model built was able to predict the time-to-exhaustion (TTE) of system
resources under different conditions.
Alonso et al. [14] had compared the large set of families like decision tree, linear discriminant
analysis/quadratic discriminant analysis (LDA/QDA), random forest, support vector machines, naïve Bayes
and k-NN for prediction of state in the context of a three-tier J2EE system. Andrzejak and Silva [15]
compared four classification methods: ZeroR, decision tree, naïve Bayes, and support vector machines. These
algorithms are compared under constant software aging injection rate by considering one aging indicator
metric i.e., memory consumption. The results indicated that all classification methods performed similarly.
Jia et al. [16] used multiple linear regression algorithms to do a detailed analysis to predict web
server parameters. In the first step, the system was pressurized using a pressure testing tool and collected data
was pre-processed. The resource consumption trend was generated using the time series model in the second
step. In the third step, the feature selection algorithm was used to select the best subset to be used as input
parameters of the algorithm. In the fourth step, analysis was done using multiple linear regression and the
aging process prediction. In the final stage, the algorithm feasibility is evaluated using evaluation metrics.
The results indicated that this algorithm could predict the aging process in the allowable error range.
Liu and Meng [17] designed a method for predicting software aging which used an artificial bee
colony algorithm. This achieves better prediction accuracy as the back propagation neural network
optimization is achieved. The experiment results showed that the software aging prediction trend is more
accurate than the traditional BP neural network. The proposed method also has a faster convergence speed
and more prediction results which are more stable.
From the previous works, it can be observed that the concept of software aging is gaining
importance. Researchers are trying to predict the software aging time to trigger the rejuvenation to avoid its
impact. Similar works related to software aging prediction have been mentioned in the literature. There is a

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Software aging prediction – a new approach (Shruthi Parashivamurthy)
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scope for improvement or alternative methods to predict software aging. Considering these points, an attempt
has been made to find a new approach to predict software aging in the proposed work. The proposed k-NN
based method performs better compared to similar previous works.
3. MOTIVATION
The motivation to conduct this research originated since software aging is an emerging research area
and machine learning is an emerging technology trend. Contributing to the area of software aging and
research is satisfying work as this area is gaining momentum in recent years. The power of machine learning
algorithms can be applied to achieve the objective. As the usage of cloud-based applications is increasing, it
is the responsibility of the service provider to provide uninterrupted services to satisfy users. The inclusion of
a module that avoids the impacts of software aging on a platform on which the application is hosted will make
the service provider trustworthy. In this regard, the intended research helps cloud service providers also.
4. THE PROPOSED MODEL
Most of the services hosted on the cloud run in a virtualized environment. Virtualized environment
includes various layers such as physical hardware, virtual machine, virtual machine monitor and applications
running on a virtual machine. Figure 1 shows the typical cloud platform.
Figure 1. Typical cloud platform
The long-running applications on virtual machines suffer from software aging and hence there are
chances of affecting service availability. The resource consumption metrics are collected from various layers
of the virtualized environments on which cloud services are hosted to predict software aging. The metrics
chosen to collect are aging indicator metrics. Figure 2 provides information regarding aging indicators. The
metrics and justification for selecting these metrics are given.
Figure 2. Aging indicator metrics
As the response time of any application indicates the performance of the system, application
response time is one of the aging indicators usually considered in studies related to software aging. The most
important operating system resources are CPU and memory. The metrics indicating the usage percentage of

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these resources can also be considered as aging indicators. In this work, CPU consumption and memory
consumption metrics are used to build the prediction model. The prediction model has been built using the
following strategy. In this work, for the prototype, metrics collected from a virtual machine (VM) are used
and the same technique can also be applied to a virtual machine monitor.
a. The status of VM identification using the three methods
 Static threshold: In the live environment, previous data related to resource usage was captured to know
when the system was affected by software aging. CPU and memory usage metrics when the system failed
were considered as static threshold values. At a certain point in time, various VMs status and resource
utilization are captured to build the data set. The scatter graph is plotted using these values. The status of
VM is identified by finding the nearest neighbors.
 Adaptive threshold of CPU usage: The CPU usage history of k-NN is captured. Inter quartile range (IQR)
statistical method is applied to find the adaptive threshold. The labeling of nearest neighbors is done
based on the adaptive threshold value. The statuses are healthy, aging-prone, and aged.
 Adaptive threshold of memory usage: The memory usage history of k-NN is captured and IQR is applied
to find an adaptive threshold.
b. Prediction of software aging
 Once the aging-prone VMs are identified, the nearest aged neighbors are to be found.
 Resource utilization trend of aged VMs is found and based on this, prediction of time required for aging-
prone VMs to reach aged state is made.
Table 1 shows the steps followed for software aging prediction using k-NN based software aging prediction.
Table 1. Steps for software aging prediction using k-NN based method
No Step
1 Load the dataset which consists of CPU usage and Memory usage percentage.
2 Determine the value of K, which indicates chosen number of neighbors.
3 Calculate the Euclidian distance between the query example and the current point for each point in the dataset. Add this attribute
to the dataset.
4 Sort the dataset in ascending order of Euclidian distance (smallest to largest).
5 Pick the k number of rows from the sorted dataset.
6 Get the labels from selected k entries.
7 Return the mode of k labels.
8 Sort the CPU and memory utilization history of k points in the ascending order
9 Find the Median for CPU entries.
10 Identify Quartiles.
Before median it is Q1 and after median it is Q3.
11 Find Q1 and Q3
12 Subtract Q1 from Q3 to obtain the Interquartile range
IQR = Q3-Q1
13 Calculate MaxCPUThreshold = IQR3+s.IQR (s=1.5)
14 Calculate
CPU Utilization >=MaxCPUThreshold (status is aged)
CPU Utilization <=maxCPUThreshold and >=maxCPUThrshold - 10% (status is aging-prone)
CPU Utilization <=MaxCPUThreshold -10% (status is healthy)
15 Classify VMs as per status calculated in step 14 comparing with current CPU utilization
16 Calculate MaxMemThreshold = IQR3+s.IQR (s=1.5)
17 Classify VMs as per status calculated in step 14 comparing with current Memory utilization
18 For VMs with status = Aging-prone
Find nearest k ‘aged’ VMs
End for
19 For each aged VM
Identify the resource utilization trend.
Find out the time taken for aged VM to reach the current status from aging-prone status.
End for
20 Find out the average time taken by k aged VMs to reach aged status from aging-prone status.
21 On the basis of obtained average time taken, forecast the status of aging-prone
In step 13, the value of s is taken as 1.5 for the following reason. When John Tukey was inventing
the box-and-whisker plot in 1977 to display the values, he picked 1.5×IQR as the demarcation line for
outliers [18]. This has worked well, so researchers have continued using that value ever since.
The concept has been implemented using Python scripting language. Python is being used by
researchers nowadays because of the various libraries it has that can support any type of research. Python
includes libraries and frameworks related to machine learning. It is platform-independent and has a wide user
community which makes it the first choice of research.

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4.1. k-nearest neighbor algorithm
The k-NN algorithm is a supervised machine learning algorithm. It is applied to solve classification
problems. The usefulness of the k-NN algorithm has been proved by the number of applications built based
on this machine learning algorithm. In this research work, the k-NN algorithm has been used to classify the
entity of virtualized environment as aged, aging-prone, or healthy.
4.2. Cluster creation
Figure 3 shows the sample dataset used for plotting the scatter graph. The scatter graph is plotted
using the dataset to form the cluster. The rows used also included outliers. Outliers, in this work, are the
aging indicator metrics that are usually not in the range of other points. It happens because of an unexpected
spike in resource usage which is actually not a result of software aging. Outliers have been handled. Missing
values are filled with suitable values. Clusters are formed based on the x and y values, in this case, CPU and
memory consumption status. If the resource consumption reaches 80%, it is considered aged because service
delivery will be hit. If one of the CPU or memory values reaches 70%, it is considered aging prone. The
static threshold defined is based on our observation. This is also found in previous works [19].
The scatter graph has been plotted to visualize the clustered formed. Each of the clusters indicates a
group of VMs with similar status. Figure 4 shows the scatter graph. The scatter graph has been plotted to
visualize the clustered formed. Each of the clusters indicates a group of VMs with similar status. The status
can be healthy, aging-prone, or aged. The different clusters in the scatter graph shown here indicate groups of
entities belonging to various statuses: aged, aging-prone, and healthy.
Figure 3. Sample dataset 1 Figure 4. Scatter graph-clusters
4.3. Query point
Once the model is built, the status of any VM can be obtained by providing CPU and memory
utilization percentages. This input is called query point. The nearest neighbors are found by calculating
Euclidian distance. The formula for calculating the Euclidian distance is given in (1):
d(p, q) = √(q1 – p1)
2
+ (q2 – p2)
2
(1)
where p1 and p2 are cartesian coordinates of the point p and q1 and q2 are the Cartesian coordinates of the
point q, d is the distance between p and q. (p and q are points for which the Euclidian distance is calculated).
After calculating the Euclidian distance, the nearest neighbors are found. The status of the majority of the
neighbors indicates the status of the requested VM. The model which is built based on the k-NN algorithm
returns the status as one of the three options: healthy, aging-prone, or aged. The value of k is to be provided
which means the number of neighbors to be considered.
4.4. Adaptive threshold method
In this method, the resource usage history of k-nearest neighbors is captured. An IQR statistical
method is applied to find the adaptive threshold. The labeling of nearest neighbors is done based on the
adaptive threshold value. The statuses are healthy, aging-prone, and aged. Figure 5 shows the sample dataset.
For example, there are seven data points. These points are the resource consumption percentages of previous
days of a virtual machine (represented as T1 to T7 in the program). Table 2 shows the values.

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𝐼𝑄𝑅 = 𝑄3 − 𝑄1 = 6.8 – 6.0 = 0.8 (2)
𝑈𝑝𝑝𝑒𝑟 𝑇ℎ𝑟𝑒𝑠ℎ𝑜𝑙𝑑 = 𝑄3 + 𝑠. 𝐼𝑄𝑅 (3)
𝑈𝑝𝑝𝑒𝑟 𝑇ℎ𝑟𝑒𝑠ℎ𝑜𝑙𝑑 = 6.8 + (1.5 𝑋 0.8) (4)
𝑈𝑝𝑝𝑒𝑟 𝑇ℎ𝑟𝑒𝑠ℎ𝑜𝑙𝑑 = 6.8 + 1.2 = 8` (5)
The obtained status is tabulated, and the query point is labeled accordingly as shown in Table 3. The status of
VM is calculated using three methods: static threshold, adaptive threshold using CPU metric, and adaptive
threshold using memory usage metric.
Depending on the mode of k points in three evaluations, query point label is done. If three statuses
are different, then static threshold status is considered. The screenshot of the program execution has been
given in Figure 6.
Figure 5. Sample dataset 2
Table 2. Resource consumption values
Data Points 5.2, 6.0, 6.2, 6.4, 6.7, 6.8, 7
Q2 6.4
Q1 6.0
Q3 6.8
Range: 0 (min)-10(max) indicate percentage of consumption
Table 3. Status of query point from three methods
Nearest
neighbors
Status as per static
threshold
Status as per current CPU utilization
(adaptive threshold)
Status as per current memory utilization
(adaptive threshold)
K1 Aged Aged Aged
K2 Aging prone Healthy Aging prone
K3 Aged Aging prone Aged
K4 Aging prone Aged Aging prone
K5 Aging prone Aged Aging prone
Figure 6. Output indicating the status
4.5. Prediction of software aging
As mentioned in the algorithm, the nearest k-aged VMs are identified. Here the procedure for one
aged VM is given. Resource usage of aged VM is found which is previous days’ data before it gets aged.
Resource usage data is tabulated in Table 4.

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Table 4. Resource usage of one virtual machine
Time CPU Usage Average Status
Day 1 6.2 5.7 Healthy
Day 3 5.2
Day 7 6.7
Day 9 7 6.9 Healthy
Day 11 6.8
Day 15 6.6
Day 17 7.0 7.1 Aging-prone
Day 19 7.2
Day 23 8
Day 27 7.9
Day 29 8.0 8.15 Aged
Day 31 8.3
Range: 0 (min)-10(max) indicate percentage of consumption
Hence, current aging-prone VMs take 6 days to become aged VM. Figure 7 shows the screenshot of
the program execution. As per the trend observed in the nearest 3 aged VMs, identify the time required for
aged VM to become aged from aging-prone. Table 5 shows the resource usage of 3 virtual machines.
Figure 7. Prediction part of random execution
Table 5. Resource usage of 3 virtual machines
Aged VM No of days taken Average days
VM-1 5
6
VM-2 7
VM-3 6
4.6. Rejuvenation
Software rejuvenation is the technique that refreshes the system and brings it back to a healthy state.
The rejuvenation process is triggered for aging-prone VMs to improve service availability. Actions are
triggered based on classification as depicted in Table 6.
Table 6. Aging status and actions
VM Status Action taken Remarks
Healthy Rejuvenation not required Observation continues.
Aging-prone Forecasting is done to identify resource exhaustion time. Rejuvenation is triggered before resource exhaustion
happens.
Aged Rejuvenation is triggered immediately. The system returns to a healthy state after rejuvenation.
4.7. Evaluation of the proposed method
As the work is k-NN based new approach, the performance of the k-NN classifier has been
compared with similar classifiers decision tree and naïve Bayes for the same data set. The result indicates that
the k-NN algorithm performs better than the decision tree. The execution result is tabulated in Table 7. Details
of previous research works for software aging prediction have been tabulated in Table 8. It can be observed that the
proposed model of software aging prediction addresses the drawbacks in the previous works.

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Table 7. Performance comparison
Algorithm Data set size Accuracy
k-nearest neighbor 115 97.6
Naïve Bayes 115 96.0
Decision tree 115 92.8
Table 8. Comparison of similar research works
Researchers The highlight of the work The proposed model
Fang et al.
[20]
Instead of fixed thresholds, the method used in this work
regularly regulates the thresholds by taking feedback
information in the running process into account.
Recommended adaptive threshold for aging detection
Adaptive thresholding is a part of the overall software
aging prediction strategy in this research work.
Ahamad [21] Found reasons for aging, effects of software aging.
Concluded that it is impossible to stop software aging,
but it is possible to reduce its speed and progress.
The software aging prediction method employed in this
work enables rejuvenation to reduce the speed and
progress of aging accumulation.
Liu et al.
[22]
A monitoring agent in every VM collects metrics; CPU
usage and free memory available to detect the aging
severity.
It is an intrusive method.
The tool used for collecting the metrics related to software
aging is non-intrusive in this work. NMS tool captures
metrics without adding overhead.
Yan [23] Operating system parameters and database parameters in
the running phase are collected using a built-in windows
counter without disturbing the running system.
Used IIS Webserver which is platform specific.
The model used in this work can be deployed on any
platform like Windows or Linux. It is not platform-
specific.
Cui et al.
[24]
The rate of aging is more in virtual machines. The proposed model is built for a cloud platform which is
a virtualized environment. It justifies the chosen platform.
Umesh et al.
[25]
Software aging forecasting using time series model.
Not recommended method if there is a spike in resource
usage.
The proposed model uses static and adaptive techniques
which eliminates this concern.
Umesh and
Seinivasan
[26]
Used different methods for forecasting.
Weightage given to different techniques is not acceptable
in all scenarios.
The model proposed in this work improves the prediction
accuracy.
5. CONCLUSION
In this work, an attempt has been made to forecast software aging. During the testing phase of
software development, the application can be tested for all types of probable issues, but a problem like
software aging must be dealt with during runtime only. It cannot be avoided; it can only be managed. As the
accumulation of errors, lock contention, and data corruption, lead to this problem, the impact can be seen as
the owner’s loss as the service provider will lose the customers. Also, reduced performance and decreased
reliability are other negative impacts of software aging. Even if all proactive measures are taken, the problem of
software aging cannot be prevented, but it can only be managed. The only available solution is to predict the future
status and pre-emptively rejuvenate the system. The aging forecasting is done using the new method. This
research work can be one of the considerable contributions to the area of software aging and rejuvenation
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BIOGRAPHIES OF AUTHORS
Shruthi Parashivamurthy is an Information Science and Engineering graduate.
She also holds the M.Tech. degree in Computer Science and Engineering from VTU. She is
pursuing a Ph.D. under VTU in the area of software aging and rejuvenation. She presently
works as an assistant professor in the Department of Computer Science and Engineering,
Global Academy of Technology, Bengaluru, Karnataka. She has a total of 7 years of
experience in teaching and industry. She has published several papers in international journals.
She is active in research and a life member of the CSI society. She can be contacted at
shrutip@gat.ac.in.
Nagaraj Girish Cholli is a Computer Science and Engineering graduate. He also
holds the M.Tech degree in Computer Science & Engineering from IIT-Roorkee. He obtained
Ph.D. from VTU in the year 2016 in the area of software aging and rejuvenation. He presently
works as associate professor at the Department of Information Science and Engineering, R.V
College of Engineering, Bengaluru, Karnataka, India. He has a total of 14 years of experience
in teaching, research, and industry. He has published several research articles in international
journals and presented papers at conferences. He is active in research, has filed patents and
guiding several Ph.D. scholars. He is also a life member of ISTE and CSI society. He can be
contacted at nagaraj.cholli@rvce.edu.in.

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