In the study of software reliability, the phenomenon of software aging has
been reported in the long-running software system, in which system performance
gradually deteriorates over time and even sudden failures occur (Avritzer
and Weyuker, 1997). Software aging phenomenon is closely related to system
resource consumptions. It has been observed in transaction processing system,
web server and Java virtual machine and so on (Garg
et al., 1998; Grottke et al., 2006;
Kourai and Chiba, 2011). In order to attack the phenomenon
of software aging, software rejuvenation technique as a preventive maintenance
policy was first proposed by Huang et al. (1995)
to ensure system reliability, to reduce high maintenance cost and breakdown
cost and to improve system availability.
Web services are emerging as a standards-based platform for application integration
across wide area networks and enterprises. Web services can integrate and collaborate
with various applications in a loosely coupled way to achieve business goals.
It also decreases the complexity of application connection in order to reduce
the cost of maintenance and updating. So it is one of the most promising solutions
in web application environment, (Tsalgatidou and Pilioura,
2002). Since deployment of web services based architectures has grown over
recent years, it is necessary to provide reliable web services (Zhang
et al., 2012).
The application server is a kind of independent software system or service
program that provides management of computing resources and network communications.
It shields the complexity and heterogeneity between the underlying network and
the operating system, so that the whole system is transparent to client access.
In the area of network computing, application server is the supporting runtime
platform of web services and the dynamic characters of web services affect systematic
behavior and runtime state of application server. We study a service-oriented
J2EE application server to see whether it suffers from software aging. Our final
objective is to monitor system resources and analyze the cause of software aging
which plays an important role in improving the availability and providing more
reliable web services of application server.
In this study, we demonstrate the simple methodology for detecting aging in
the service-oriented J2EE application server. Since the application server is
running on JVM, memory leaks in the JVM is firstly analyzed. Then a memory analyzer
tool referred as Profiler is designed and implemented to collect performance
resource usage and system activity data from application server at regular intervals.
Finally, the evaluation metric of slope estimation technique in the linear regression
method is adopted to estimate the presence of software aging in application
server and the cause of software aging is given.
Numerous and valuable studies have been devoted to detection and analysis of
software aging. Garg et al. (1998) first proposed
a measurement-based method to estimate software aging, the operating system
resource usage and system activity data such as memory usage were collected
via a SNMP-based distributed monitoring tool and then software aging in UNIX
workstation is estimated and verified based on a statistical model. Cassidy
et al. (2002) explored the feasibility and practicability of exploiting
advanced statistical pattern recognition for detecting software aging in large
OLTP DBMS servers. To estimate aging trends in a Apache web server, the non-parametric
statistical methods and parametric time series models were applied by Grottke
et al. (2006) which demonstrating a numerical validation based on
collecting the data of resource usage and activity parameters such as used swap
space, response time, free physical memory. By adopting the aspect-oriented
programming technology, Alonso et al. (2010)
injected the monitoring solution in runtime J2EE applications to determine the
component root cause of software aging. Kourai and Chiba
(2011) collected and analyzed data of throughput loss and memory depletion
and then adopted both parametric and non-parametric statistical techniques to
reveal the presence of software aging phenomenon in the Sun Hotspot Java virtual
machine. The virtual and resident memory utilization was investigated by Araujo
et al. (2011) to indicate the presence of software aging in the cloud
computing infrastructure. Matos et al. (2012)
monitored and collected the data of RAM memory exhaustion, swap memory usage
and CPU utilization, to analyze the software aging effects on the elastic block
storage management of eucalyptus framework.
The statistical analysis method and the resource usage parameters including
memory usage monitored in this study are similar to the above studies. In contrast
to these studies, various system workloads are considered in this study for
detecting software aging. In addition, our investigation focuses on the J2EE-based
applications and takes into account memory leaks in JVM to analyze the root
cause of software aging.
MEMORY LEAKS IN JVM AND THE MEMORY ANALYZER TOOL
Memory leaks are known to be a major cause of reliability and performance issues
in software system and they are often a contributing factor to software aging.
Despite the built-in garbage collector in JVM, memory leaks can exhaust available
system memory as an application server runs on JVM. Therefore, the JVM memory
management mechanism is firstly introduced to analyze the problem of memory
Memory leaks in JVM: JVM memory region, also called runtime data area,
can be divided into the area of method, heap, stack, register and native code
stack. While a program is running in JVM, JVM memory region can store data such
as byte codes, objects, parameters, return values, local variables and intermediate
results and so on. Where, the heap memory is used to store instances of classes
or array of a runtime Java program. JVM provides the instruction of allocating
a new object, but doesnt support
memory release which is implemented by garbage collector. However, garbage collector
only reclaims useless and unreferenced object as shown in Fig.
1, so that the useless and reachable objects cannot be released from memory
area by garbage collector. Therefore, such potential defects of garbage collection
mechanism probably cause memory leaks in JVM.
If the problem of memory leaks can not be resolved, JVM memory usage will continue
to increase over time and reach to the maximum memory usage for JVM. At the
moment, the garbage collection mechanism automatically starts up to gradually
release JVM memory for a period time and JVM will occupy a large amount of CPU
time and system resources for garbage collection. Nevertheless, the resource
is finite, thus it will eventually result in system crash or software aging.
The memory analyzer tool: In view of memory leaks in JVM, a special
tool is required to monitor and detect JVM memory, so that developers can easily
estimate whether software aging exists in application server.
Application server provides the bidirectional interface JVMPI (Java Virtual
Machine Profiler Interface) between JVM and external programs, as shown in Fig.
2. Agent is loaded when JVM is starting up. Agent communicates with JVM
via JVMPI and it can receive various events from JVM and send control information
to JVM. The Agent module is realized by local language C. Based on Javas
platform independence, JVM can call the Agent module.
|| Memory leaks in JVM
|| JVMPI working principle
|| Event triggering mechanism of the agent
In the Agent module, the pointer to an instance of JVM is obtained by using
the JNI (Java Native Interface).
The event trigger mechanism is shown in Fig. 3. As the event
receiver, Agent registers its interesting events (such as the memory allocation)
to JVM which is the event trigger, so that JVM can notify the Agent when the
event is triggered.
Based on JVMPI, the memory analyzer tool called Profiler is designed and developed.
The main functions of the Profiler include: monitoring memory usage through
the visualization window in real-time, selecting sampling frequency of memory
data according to the need of users and filtering the output information. The
Profiler can collect the data from four modules: JVM runtime module, system
data module, web thread module and transaction manager module. JVM runtime module
is responsible for extraction of the size of JVM memory and used memory. System
data module extracts the CPU usage and free memory usage. Web thread module
is in charge of extracting the count of created threads, destroyed threads,
concurrent threads. Transaction manager module extracts information of global
transactions. Among the four modules, JVM runtime module and system data module
are the two most important modules.
AGING DETECTION AND ANALYSIS OF APPLICATION SERVER
The experimental platform simulates a monitoring and recording system for a
service-oriented J2EE application server.
Experimental environment: As shown in Fig. 4, the
experimental environment consists of a J2EE application server, multi-clients
and a database server. In the clients, the load generator is used to generate
web service requests to the application server through standards-based HTTP
or SOAP protocols. The application server receives the requests, connects and
queries the database server and then returns results to the clients. The memory
analyzer tool referred as profiler is used to monitor and collect the data of
JVM heap memory from the application server.
All the servers involved are 3.0 GHz Pentium IV system running Windows XP,
with 1.0 GB of memory. The application server is Websphere 5.1 with maximum
JVM heap memory 256 MB and the use case deployed on the application server is
Petstore 1.3.1-02. The database server is MySQL. The machines are connected
on a same local area network with 100 Mbps Ethernet.
The dynamic parameters in the clients and the application server are periodically
monitored and recorded in a certain format separately. The sampling interval
is ten minutes. The online access behavior of users obeyed the Poisson distribution
in the form of a week period. The load density is different between business
days and rest days.
Note that the Profiler runs on the application server and it also occupies
part of system resources. Hence, the resources data obtained by this method
are in fact the accumulation data of the Profiler and the application server.
Nevertheless, the memory analyzer tool Profiler only occupies fewer system resources,
so its effect on the aging detection results can be ignored. Thus, the aging
detection results can be regarded as the ones of the application server itself.
Peak load test: The peak load of application server is the maximum amount
of concurrent client requests that an application server could respond within
a certain time period.
||Software aging detection model for the service-oriented J2EE
In order to simulate the peak load, load requests with the equivalent intensity
are sent to an application server in a short time. If all these requests are
successfully responded by the application server, then the load intensity is
increased until there is no response from the application server, when the load
intensity is regarded as peak load.
According to the value of peak load, different scenarios with various load
intensities are designed to detect and analyze software aging in the application
server. Due to the request number per unit time and the mean service time are
the main factors influence the load intensity, they are selected as the preconditions
in the following aging tests.
Aging test under heavy load: In the heavy load test, the average load
intensity is designed to 30% of peak load and the load intensity in the peak
period is determined as 50% of peak load. The default initial size of JVM heap
memory usage is 128MB and the maximum is 256MB. The application server is unable
to respond to client requests after running 43 h, when JVM heap memory usage
reached about 250MB.
The relationship diagram between JVM heap memory and running time of application
server is shown in Fig. 5. Our practical task is to verify
whether software aging exists in the application server, namely, whether the
system performance gradually degrades over time. A reasonable approach is to
analyze the relationship between JVM heap memory and running time of application
server via linear regression method. And the estimated times to JVM heap memory
exhaustion was computed using the linear regression equation Y = m*X+c, where
m is the slope, c is the intercept or the initial value and Y is the final value
(Grottke et al., 2006). In this heavy load test,
the linear regression equation is fitted as follows:
where, X is the running time of application server and Y is the JVM heap memory
||JVM heap memory usage of application server under a heavy
||JVM heap memory usage of application server under a light
It can be seen that the slope of regression line in the Eq. 1
is positive which indicates that the JVM heap memory usage increases with the
running time of application server. Thereby, it is evident that software aging
exists in the application server.
Aging test under light load: In the light load test, the request number
in unit time is decreased. The average load intensity and the load intensity
in the peak period are being reduced by 20% of the heavy load test. In this
test, the application server is unable to respond to client requests after running
140 h. The JVM heap memory of application server is shown in Fig.
||JVM heap memory usage under prolongation of the mean service
Similarly, when JVM heap memory usage reached about 250MB, the application
server crashed. The linear regression equation which describes the relationship
between JVM heap memory and running time of application server is as follows:
Note that the slope of regression linear Eq. 2 is positive
and it is smaller compared with that in the heavy load test. It verifies that
software aging exists in the application server and the depletion of JVM heap
memory over time is the major cause of software aging. In addition, compared
with heavy load test, the load intensity in the light load test is being reduced
by 20% yet the running time increases three times. Therefore, it can be concluded
that the load intensity is a major factor that influences software aging.
Aging test under prolongation of the mean service time: Next, the mean
service time is prolonged six times and the load intensity is equivalent to
that of light load test.
The application server crashed after running 43 hours. The JVM heap memory
is shown in Fig. 7 and the linear regression equation is as
Likewise, the slope of regression linear Eq. 3 is positive
and it is greater than the ones of the above two kinds of test. This is because
of the prolongation of the mean service time, namely, increasing the residence
time of users in application server which requires more available system resources.
Thus it will greatly accelerate resource consumption and aging process in the
CONCLUSION AND FUTURE WORK
Although the garbage collection mechanism can be regarded as the special Java
function to release objects automatically by the garbage collector, the problem
of memory leaks still exists in Java applications. And memory leaks are a major
cause of software aging. In this study, the aging detection method for the service-oriented
J2EE application server is presented and the design and implementation of aging
detection are described. Through monitoring the performance parameters of runtime
application server, the JVM data is extracted for aging analysis and verification.
Finally, through the experimental and statistical analysis of JVM usage, the
gradual increase in JVM heap memory usage is visible and the existence of aging
is evident. It can be concluded that memory exhaustion is the main cause of
software aging in application server and the system workload has great influence
on the memory usage. The aging symptoms detected in this study may also occur
in other systems based on J2EE architecture.
Future research mainly includes monitoring and analysis of other system parameters
of resource consumption. And the study on software aging mechanism is another
avenue for new development.
The author would like to thank the sponsors of the National Natural Science
Foundation of China under Grant No. 61100173, Scientific Research Plan Project
of Shaanxi Education Department of China under Grant No. 09JK642, Doctoral Fund
No. 116-210912 and Scientific Research Plan Project of Xian
Technology University under Grant No. 116-210907.