COMPARISON OF FOUR NETWORK MONITORING TOOLS. SOLARWINDS PRTG NETFLOW ANALYZER NNM9i. By KAUSHALI KUNDU Indian Institute of Technology,Kanpur

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1 COMPARISON OF FOUR NETWORK MONITORING TOOLS SOLARWINDS PRTG NETFLOW ANALYZER NNM9i By KAUSHALI KUNDU Indian Institute of Technology,Kanpur Under the Guidance of Dr.N.P.Dhavale,IDRBT 10thMay-10 th July,2013

2 CERTIFICATE OF COMPLETION This is to certify that Miss KAUSHALI KUNDU, pursuing M.Sc course at Indian Institute of Technology, Kanpur with statistics as major subject, in the Department of Mathematics and Statistics, has undertaken a project as an intern in the Institute of Development and Research in Banking Technology (IDRBT), Hyderabad from 10 th May, 2013 to 10 th July, She was assigned the project COMPARISON OF NETWORK MONITORING TOOLS which she completed successfully under the guidance of Dr. N. P. Dhavale, IDRBT. Dr. N.P. Dhavale (Project Guide) Deputy General Manager INFINET and Services IDRBT, Hyderabad Project by Kaushali Kundu Page 2

3 ACKNOWLEDGEMENT I would like to express my sincere gratitude to the Institute for Development and Research in Banking Technology (IDRBT) and particularly Dr. N. P. Dhavale(DGM of INFINET and Services) who was my guide in this project. This opportunity of learning all the network monitoring activities of a banking system of the country was a boon to me as one rarely gets such exposure. I would like to add that this short period in IDRBT has added a different facet to my life as this is a unique organization being a combination of academics, research, technology, communication services, crucial applications, etc. I am extremely grateful to Dr. N. P. Dhavale for his advice, innovative suggestions and supervision. I thank him for introducing me to an excellent network application and giving me the opportunity to perform some statistical analysis. I am thankful to the staff of INFINET department at IDRBT for helping me to get familiar with the network monitoring tools. They gave me a chance to study the application and its impact from different perspectives. I am thankful to IDRBT for providing such an amazing platform for students, like me, to work in real application oriented research. Finally, I thank one and all who made this project successful either directly or indirectly. I am very thankful to Mr.V.S.Mahesh,Ms. Anuradha, Mr. Sri Hari,Mr. Prashanth, Ms.Nagasudha and other members of INFINET department with whom I worked throughout at IDRBT and the project was possible only with their cooperation. KAUSHALI KUNDU (IIT Kanpur) Project Trainee Department of INFINET, IDRBT Hyderabad Project by Kaushali Kundu Page 3

4 ABSTRACT This project compares the data obtained from the four network monitoring tools- Solarwinds, PRTG, Netflow Analyzer, NNM9i,which are currently in working state in IDRBT. The data is tested for consistency and steadiness as well as reliability using statistical tools. A table has been prepared to bring out the important features of the above tools. Graphs and Statistical tests are used to find any discrepancy in the data gathered from these tools as well as from the routers. Surprisingly, Solarwinds and PRTG are found to be consistent with each other and with the router values, but Netflow is observed to give higher values while NNM9i gives smaller values than the original router and other tools in some cases. Application wise analysis of the data obtained from Netflow are also taken into consideration. Project by Kaushali Kundu Page 4

5 CONTENTS Introduction Objectives Details of Work Done Comparison Tables Graphs P-value of tests Regression Analysis Original Router Logs Application-wise Analysis Conclusion Limitations Software used Project by Kaushali Kundu Page 5

6 INTRODUCTION The term NETWORK MONITORING describes the use of a system which constantly monitors a computer network for slow or failing components and notifies the network administrator through s, SMS or other alarms. A good network monitoring software continues to send status messages and traffic to efficiently keep track of any outages on the network. Network monitoring watches hardware such as router or network servers for responses. Depending on the software, it can send response requests every minute up to every hour. Network monitoring can also give vital statistics to businesses to evaluate down time. The statistics can help network administrators analyze issues on the network to better prepare for future outages. Other statistics can give a percentage of up time versus down time to evaluate profit and loss for businesses that rely heavily on network availability. In the present study, in IDRBT, four network monitoring tools -SOLARWINDS, PRTG, Manage Engine NETFLOW ANALYZER,NNM9i, currently in working position, have been compared for their use efficiency. OBJECTIVE To compare and analyze the data/reports obtained from the four network monitoring tools- SOLARWINDS, PRTG, Manage Engine NETFLOW ANALYZER, NNM9i To identify / select the most efficient, reliable and user-friendly monitoring tools for specific situation as well as for general purpose. DETAILS OF WORK DONE STEP-I: A study was made regarding certain important features of the above mentioned monitoring tools. Two tables (Table No.1 and 2) have been prepared to describe some of their basic important properties. STEP-II: Data from each of these monitoring tools (data on traffic in/out, latency, ping time and other available) were collected. Data were also gathered after logging onto the router directly in order to compare with the four tools. STEP-III: The data obtained from these 4 tools were compared in order to check their consistency with each other. For that data on bandwidth utilization (traffic in/out) for a particular place on a specific time interval were compared using various graphs and certain statistical tests (viz Kolmogorov-Smirnov test). Project by Kaushali Kundu Page 6

7 STEP-IV: Based on Statistical tests and analysis, final conclusion was made about the consistency of the four monitoring tools. The snapshots of the four tools are given below: SOLARWINDS Simplifies detection, diagnosis and resolution of network issues. Tracks response time, availability, uptime, downtime, packet loss, important statistics and useful graphs. PRTG Bandwidth and network monitoring Packet sniffing Router(uptime/downtime) monitoring Project by Kaushali Kundu Page 7

8 NETFLOW ANALYZER Detects the netflow Provides individual application wise bandwidth utilization Gives detail graphs of important factors. NNM9i Provides data on inbound/outbound traffic Gives CRC errors Dashboard facility is available Project by Kaushali Kundu Page 8

9 Table No.1 OVERVIEW OF MONITORING TOOLS SOLARWINDS PRTG (Manage Engine) NETFLOW ANALYZER NNM9i Difficulty of installation simple but lengthy easy and simple simple but lengthy Simple user friendly License availability by interface by sensors by interface by nodes graphs Maps # # Alerts & Warnings BGP alerts # Dashboard Node/Interface details Adding/Removing Node availability of report (from dashboard) syslogs # scheduling reports to e- mail Netflow analyzer # # # :available :unavailable #:available but not enabled Project by Kaushali Kundu Page 9

10 Table No.2 INSIGHT INTO THE MONITORING TOOLS SOLARWINDS PRTG NETFLOW ANALYZER NNM9i Bandwidth (traffic in/out) CPU utilization # Memory utilization # Percentage utilization of bandwidth ping test # protocol status(up/down) Response Time Packet transfer/sec Latency # # Packet loss # # Jitter # # CRC Errors Trap Details # Network Monitoring Bandwidth utilization by # # # individual application Bandwidth utilization by individual source/destination # # # :available :unavailable #:available but not enabled/used Project by Kaushali Kundu Page 10

11 COMPARISON OF THE FOUR MONITORING TOOLS Data on traffic in/out of Khargarh_Reliance router(wan interface) on 11 th -12 th june was taken and has been represented graphically below. Fig 1. TRAFFIC_IN OF KHARGARH_RCOM(WAN) ON 11 th -12 th JUNE Time Series Plot of solar, prtg, netflow, 9i Traffic_in(kbps) Variable solar prtg netflow 9i :50am 1:30pm 5:20pm 9:10pm 1:10am 5:00am 8:50am 12:40pm 4:30pm 8:20pm Time From the graph it is observed that netflow and NNM9i are not consistent with each other. To verify whether the data obtained from these 4 tools are consistent or not, KOLMOGOROV- SMIRNOV TEST was conducted-.this K-S test(2 sample), a non-parametric test(where no distributional assumptions are made)is used to check the equality of two distributions using their empirical distribution function. Project by Kaushali Kundu Page 11

12 Hypothesis tested : the samples are coming from the same distribution(taken 2 at a time) : the samples are coming from different distributions The p-value of the test Tools P-value Decision Solarwinds & Netflow PRTG & Netflow Netflow & NNM9i Solarwinds & PRTG Solarwinds & NNM9i PRTG & NNM9i Reject Reject Reject Accept Accept Accept From the p-values it is clear that netflow is giving significantly different data under 5% level of significance(since p-values<0.05). while the other data sets are consistent with each other(on the basis of the sample data taken) Project by Kaushali Kundu Page 12

13 Traffic_out values of Khargarh_Reliance(Wan interface) Fig 2 TRAFFIC_OUT OF KHARGHAR_RCOM(WAN) ON 11 th -12 th JUNE Time Series Plot of solar, prtg, netflow, 9i Traffic_out(Kbps) Variable solar prtg netflow 9i :35am 1:15pm 5:05pm 8:55pm 12:56am 4:46am Time 8:36am 12:26pm 4:16pm 8:06pm From the graph it is clear that netflow is giving higher values than the other tools,while NNm9i values are little lower than the rest To verify this K-S test was performed again. The p-value of the test Tools P-value Decision Solarwinds & PRTG Solarwinds & NNM9i Solarwinds & Netflow PRTG & Netflow Netflow & NNM9i PRTG & NNM9i Accept Accept 8.23E-11 Reject 8.23E-11 Reject 5.79E-12 Reject Accept From the p-values it is very clear that netflow is giving significantly different values from the other tools under 5% level of significance(p-values <0.05)while others are consistent(on basis of the sample) Project by Kaushali Kundu Page 13

14 Relationship of the data obtained from Netflow with the other tools Another attempt was made to find a relationship of the data obtained from Netflow with the other tools. A regression line (with Netflow values as dependent variable) is fitted as follows: Netflow values : y (dependant variable) Solarwinds values : (independent variable) Prtg values NNM9i values : (independent variable) : (independent variable) In case of TRAFFIC_IN, the REGRESSION LINES are as follows: y= (R-sq=88%) y = (R-sq=81.5%) y= (R-sq=52.5%) As we can see that these Regression equations, the regression coefficients are more or less equal to 1 and R-square values are high.this shows that data obtained from Netflow is consistent with the other tools. In case of TRAFFIC_OUT, the REGRESSION LINES are as follows: y= (R-sq=87%) y = (R-sq=93.6%) y= (R-sq=84.7%) The result obtained from this analysis is very interesting. Here the regression coefficients are more or less equal to 1.7 kbps while the R-square values are also significantly large. Thus, it can be concluded that there might be some kind of magnification in the data as obtained from Netflow. The reason behind this needs further investigation. Project by Kaushali Kundu Page 14

15 Next, data on Traffic_in/out was collected from 4 of these tools. But this time it was for NAGPUR_AOB_SIFY_WAN INTERFACE The following graph shows Traffic in values on 13 th June Fig.3 TRAFFIC_IN OF NAGPUR_AOB_SIFY (WAN) ON 13 th JUNE Traffic_in(Kbps) Time Series Plot of solar_in, netflow, 9i_in, prtg_in Variable solar_in netflow 9i_in prtg_in :05am 2:15am 4:35am 6:55am 9:15am 11:53am 1:55pm 4:15pm 6:35pm 8:55pm Time NNM9i values are much below the values given by the other tools.the K-S test was conducted on these values. The P-value of the tests: Tools Solarwinds & PRTG Solarwinds & Netflow Netflow & PRTG Solarwinds & NNM9i PRTG & NNM9i Netflow & NNM9i P-valuevalue Decision Accept Accept Accept 8.78E-06 Reject 1.57E-05 Reject 2.76E-05 Reject Project by Kaushali Kundu Page 15

16 From the p-values it can safely be concluded that values of NNM9i are significantly different from the other tools under 5% level of significance( since p-values <0.05). Similarly, data taken on TRAFFIC_OUT is also plotted. Fig.4 TRAFFIC_OUT OF NAGPUR_AOB_SIFY (WAN INTERFACE) ON 13 th JUNE Traffic_out(Kbps) Time Series Plot of solar_out, net_out, 9i_out, prtg_out Variable solar_out net_out 9i_out prtg_out :00am 2:10am 4:30am 6:50am 9:10am 11:30am 1:50pm 4:10pm 6:30pm 8:50pm 11:10pm Time It is visible from the graph that NNM9i values once again are consistently lower than the other tools while the Netflow values seem to be little higher than the rest. To verify this K-S test was conducted. The p-value of the test: Tools P-valuealue Decision Solarwinds & PRTG Solarwinds & Netflow PRTG & Netflow Accept Accept Accept Solarwinds & NNM9i PRTG & NNM9i Netflow & NNM9i Reject Reject 2.01E-07 Reject Project by Kaushali Kundu Page 16

17 From the p-values,it can be concluded that NNM9i values are significantly different from the other tools under 5% level of significance(on the basis of the collected sample)while rest are consistent with one another. Finally, data was gathered after logging into the router directly using the command prompt. The graph below shows the data from the router, Solarwinds, Prtg, Netflow and NNM9i for a particular period of 1 hour (for Nagpur_AOB) Fig.5 TRAFFIC _IN OF NAGPUR_AOB (SIFY_WAN INTERFACE) ON 17 th JUNE Time Series Plot of original_in, netflow, solar, prtg, 9i Variable original_in netflow solar prtg 9i Traffic_in(bps) :00pm 3:10pm 3:20pm 3:30pm Time 3:40pm 3:50pm From the graph it is visible that except NNM9i, all the other tools are giving more or less similar values as obtained directly from the router (particularly for that time interval)the values of NNm9i are much lower than the others Project by Kaushali Kundu Page 17

18 Similarly, another graph on Traffic_out is also plotted. Fig.6 TRAFFIC_OUT OF NAGPUR_AOB(SIFY_WAN INTERFACE) ON 17 th JUNE Traffic_out(bps) Time Series Plot of original_out, solar, netflow, prtg, 9i Variable original_out solar netflow prtg 9i :00pm 3:10pm 3:20pm 3:30pm Time 3:40pm 3:50pm However from this graph, it is seen that NETFLOW values are significantly different from the other tools as well as the data from the router (particular for this 1 hour time period).netflow values are much higher than the rest. CONCLUSION From the above graphs and Statistical tests performed it can be concluded that 1. Solarwinds and PRTG monitoring tool- values are consistent with each other as well as with real time router values when logged in and checked. 2. Deviations observed in the data obtained from Netflow and NNM9i monitoring tools. 3. Netflow is giving higher values in Traffic_out than other tools. 4. In some cases,nnm9i is showing smaller values than real time router values. 5. The above observations and reason(s) may be used as pointers in further investigating and traffic analysis. Project by Kaushali Kundu Page 18

19 APPLICATION-WISE ANALYSIS OF NETFLOW ANALYZER Next,an attempt is made to analyse the individual applications running in the four data-centres: 1.Khargarh 2. Nagpur_AOB 3.Belapur 4.IDRBT Some of the important significant applications running are: IPENCAP Ncube-lm Ms-wbt Windb http DIAGRAMATIC REPRESENTATION OF INDIVIDUAL APPLICATION (RCOM LINK) IN Traffic OUT Traffic MS-WBT NCUBE-LM KHARGARH IPENCAP IPENCAP WINDB IPENCAP NCUBE-LM WINDB NAGPUR_AOB HTTP HTTP Project by Kaushali Kundu Page 19

20 NCUBE-LM BELAPUR MS-WBT NCUBE-LM IDRBT NCUBE-LM After further analysis,it is observed that Data Replication Traffic: IPENCAP Application is significant in Khargarh+Nagpur_AOB Ncube-lm is the main IN_TRAFFIC for Belapur+IDRBT and OUT_TRAFFIC for IDRBT+Khargar IN_TRAFFIC for Khargarh+Belapur consists of Ms-wbt server while it is the OUT-TRAFFIC for Belapur Windb is the IN_TRAFFIC for Nagpur and OUT_TRAFFIC for Khargarh Application: Http is the major application of Nagpur OUT_TRAFFIC (This analysis is done on the basis of the 5mins interval data obtained from Netflow ) LIMITATIONS OF THE STUDY Conclusions are made on the basis of the data for specific router and for specific time interval. Here, only two of the router values are analyzed for traffic_in/out only. Other parameters are not considered. SOFTWARES USED Minitab R-code Project by Kaushali Kundu Page 20

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