Performance Optimization of a complex Virtual Private Network Architecture by Means of Adaptive Compression
Dinesh Taneja, S S Tyagi
Manav Rachna International Institute of Research and Studies, Faridabad, India [email protected], [email protected]
Abstract
Virtual Private Network (VPN) is an indispensable part of the internet world. This can be used to interconnect two networks using the cost effective shared medium of internet.
Performance of data transfer via VPN tunnel is impacted by various factors such as data format, compression algorithm, internet bandwidth etc. VPN provide security at the cost of performance; hence data specific cost benefit analysis is essential to choose the optimal architecture. Packet inspection and the correlated decision of data compression shall be a better choice before transferring the data. An algorithm of selective compressionof input data by predicting data compressibility, network bandwidth availability and compute resource utilization is being proposed which shall be self- adaptive to these factors so as to enhance the data transfer performance via secured site to site VPN tunnel.Empirical measurements show that data transfer performance improves by 60 to 80 % at higher network bandwidth availability when contrasted to standard VPN configurations.
Keywords:VPN, Tunnel, SSL, IPSEC, Compression, Information Security
1. Introduction
The development and advancement of the Internet technology has encouraged mass interest for constantly available, high performance, and overall accessibility by Internet clients.The demand for internet bandwidth is growing due to the advancement of requirements. There is need to exchange data securely across organizations. The internet medium is cost effective when contrasted to leased lines and MPLS solutions for interconnecting different offices spread across globe.Information security is major concern and can be widely categorized for data in rest, use and data in motion [1].
The data in motion has motivated the need for secure data transfer across geographically separated networks. Site-to-site VPN enables organizations to interconnect their offices spread across geographies using the shared and cost effective medium of internet.
The organizations need to exchange varied format of data across their offices and also with their partner organizations using site-to-site VPN. Different applications generate different format of data as per end user requirement of the business. The data in transaction can be Text, binary, emails, database, HTML, XML, images and video etc.
These data formats may or may not be pre compressed. Normally the compressed file formats are used to reduce the storage space in the data centre. At the same time, compression is also used in site to site VPN which is aimed to reduce data size and
optimal utilization of internet bandwidth. This is assumed that the online compression in site to site VPN may improve the efficacy of data communication.In case the data is compressible online, it may increase the performance of data transfer. However, some data cannot be further compressed and the efforts spent on compression may delay the transmission. The site to site VPN is a static connection with a predefined configuration consisting of encryption and compression algorithm. One static configuration may improve data transfer throughput in one given scenario and it may delay the data transfer in another scenario [2].
It has been measured empirically in past research works that the advantages of online compression are dependent on four critical parameters: (1) Characteristics of the data to be exchanged (whether it is in highly / moderate / low compressibility state or it may be in pre compressed format), (2) Availability of internet bandwidth at both ends of site to site VPN. (the under-utilized and over utilized internet bandwidth may impact the data transfer performance), (3) Compression algorithm used to compress data online (4) Compute resource (cpu and memory) utilization of the firewalls on both ends.
Due to the above mentioned factors, it may be stated that if the decision to use compression is manually controlled before the data is transferred andit can be enabled or disabled for each specific condition so as to achieve better performance. Unfortunately, this manual control for compression is not possible in VPN configuration because of dynamic nature of requirements and complex decision making system required before the data is transferred. This paper mentions the work carried out to evaluate existing systems and device selective compression algorithm for optimal utilization of resources in site to site VPN connectionsunder realistic working conditions.
2. Selective Compression
VPNs are configured with static parameters deciding the encryption and compression algorithm. Empirical measurements have shown that compression may not be able to improve data transfer rate always however, at certain environment conditions and scenarios, this may impact adversely the performance of data transfer via Virtual private network. Selective compression (SC) method can be usedto optimize the data transfer performance by dynamically selecting the compressibility for respective scenarios.
Selecting the best choice on the fly is a challenging goal because it is hard to predict the dynamic and interrelated factors which may impact the performance. Selective compression method attempts to optimize the VPN performance by enabling or disabling the compression on the fly.
The effectiveness of the compression may be termed as compression ratio and compression gain. The compression ratio is the ratio of compressed data divided by original size of the data. Compression gain is the size of data gained due to decompression. The compression rate is the rate at which a compression method generates compressed data. Effective transmission rate (ETR) of data is the rate at which compressed data is transmitted to remote side. The ETR depends on various other factors such as nature of data, internet bandwidth between two end points,CPU and memory
usages at two end points of the firewall used for site to site VPN. Theoverhead impacting ETR of data using VPN between two sites A& B can be computed as
Ts = Ea + Ca + Eb + cb
Where Ts represents Total overhead Time of transmission, Ea and Eb represents encryption and decryption overhead at side A and B respectively, Ca and Cb represents compression and decompression overhead at side A and B respectively. The encryption is a mandatory step required for secured communication using site to site VPN. The other two factors Ca and Cb may be improved by predicting the environment, evaluation, decision making and finally compressing the data selectively so that the compression and decompression time is reduced at the two ends of site to site VPN tunnel.
The proposed method intercepts the transmission communications with socket connections. However, this method does not propose to modify the code of the program which is already being executed in site to site VPN. Selective compression method decides when to compress the data and when not to compress the data. A prediction system is introduced by means of monitoring the data along with other dynamic conditions to determine whether enabling the compression will improve the overall throughput of data transferred via VPN tunnel. In case it is found that compression may reduce the original chunk of data, then this method compresses that chunk of data before transmittingvia VPN tunnel. In this entire method, it is proposed that compression by default shall be disabled in site to site VPN configuration. The selective compression method shall be implemented on the data before it is encrypted by the VPN algorithm.
3. Architecture
The bock level diagram of the data communication using site to site VPN is shown in Figure 1. The firewall receives the data from end user on its LAN interface. The data is first received for decision making module. The total size of the data, or its characteristics whether it is plain text, binary or pre compressed format is not known in advance by the VPN connection. The monitoring module provides information to the decision making module. The decision making module can make decision based on properties of data and other environmental conditions. The decision making module needs to predict these values in advance so that it may make proper decision about compressibility of the data.
For this purpose, a model is required to be designed to predict the data characteristics and resources availability. This module employs its prediction system whether compression is beneficial for data transmission and it identifies a potential opportunity for compression.
If compression is found to be beneficial, it forwards data to compression module for necessary downsizing the data else uncompressed data directly transferred to the VPN tunnel.
Figure 1 Architecture of selective compression in VPN
The proposed selective compression method in site to site VPN shall have an architecture consisting of following three parts
Monitoring Module: This monitoring module is used for collecting information about data compressibility and environmental properties. The monitoring can be done by means of reading some values or by means of complex prediction algorithm. These monitored values are unified by a predefined mechanism. The decision making module takes decision using these monitored values as input parameters.
The monitoring module shall be used to identify the real time information about network bandwidth availability, CPU and memory utilization status. The job of this module is referred as termed as collector. To predict the environmental conditions, there needs to be a procedure to collect real time statistics repeatedly after a defined sampling period This module continuously monitors four parameters i.e. LAN, WANinterface i/o statistics, CPU utilization and memory utilization. The average value of these parameters over a sampling period is analysed by decision making module.
Monitoring the data on the fly: The characteristics of the input data are not estimated based on the context of the files or by some compression algorithm.As a standard algorithm, the data transmitted is first divided into chunks of data. This is proposed that byte pattern may be observed in the input data chunks destined for transmission via site to site VPN. The byte pattern may be observed in a threshold value of data bytes. The unique byte pattern is identified in this threshold value. The number of unique bytes are counted in the threshold value data blocks of the input data. The unique byte pattern count (UBPC) is the number of bytes in the input data which appears repeatedly in the given chunk. This can also be stated that UBPC is the count of unique bytes pattern appearing in the input data. The UBPC value as 1 in 1024 bytes represents all the data under observation is made of same byte pattern or it can also be stated that the same byte pattern is repeating in this entire 1024 bytes under observation which means that the data is highly compressible. In case UBPC is 512, there is a probability of highly incompressible data block. Therefore, this method is useful for identifying the compressibility nature of the data. Data blocks of 64, 128, 256, 512, 1024 bytes has been considered as threshold value in this algorithm for identifying UBPC. Initially this model may observe 64 bytes and if the UBPC value remains close to previous five continuous observations, this may increase the observation byte count to 128 and subsequently up to 1024 bytes or vice versa. In this method of sliding window, the process of unique byte pattern count can
efficiently predict the compressibility nature of the input data without much impact the compute resource utilization of the firewall.
Identify CPU and Memory Availability: Compute resource utilization parameters are taken live from /proc in Linux based systems.
Identifying the network bandwidth availability (NBA): This is calculated using nload command in Linux based systems. One firewall may have different WAN and LAN port statistics and the other firewall may have different values at same point of time. The data transfer may be impacted due to bottlenecks at any end of the two firewalls. The input traffic rate at LAN interface of local firewall (Ll), output rate at WAN interface of local firewall (Wl), input rate at WAN interface of remote firewall (Wr) and output rate LAN interface (Lr) was considered. The bandwidth availability at WAN interface of first firewall can be calculated by subtracting I/O rate (Wl ) from the interface speed configured at that interface. Similarly the bandwidth availability can be calculated at WAN interface of remote firewall. Minimum of the network availability evaluated at the WAN interfaces of two firewalls is considered as bandwidth available (NBA).
The network bandwidth availability (NBA) is an important requirement for SC. As NBA increases, benefits of SC become stronger. To predict the NBA, the Multi Router Traffic Grapher (MRTG) reports of different organizations having international and domestic site to site VPN connections were evaluated. This was observed that the network bandwidth utilization remains continuously at same level and the variation of network utilization does not happen frequently. This study helped to analyse the sampling period to detect the network bandwidth availability. The sampling period for identifying Wl and Wr can be fixed at 5 minutes and the period may slide in both directions depending upon variation between current and past readings.
Decision Making Module: There shall be a decision making module for selective compression.The characteristics of the dataaffect EOR, Compression ratio, and compression gain as compressibility varies by original content. The environment properties such as network bandwidth availability, CPU utilization etc are also important factors for decision making module. The inputs given by monitoring module are crucial to improves CR and OR.The parameters provided by the above mentioned points shall be used to decide whether compression is beneficial for data transfer or not. This method shall be adopted to make final decision whether data needs to be compressed or the data can be transmitted uncompressed via site to site VPN tunnel. The selective compression decision making algorithm is mentioned in Figure 1. The information as collected from the monitoring module about CPU, memory utilization and WAN interface statistics is analysed by this module. In case, compression decision module identifies that that the compression is beneficial, it shall forward the data to compression module which may compress data and then transfer to VPN tunnel for communication. However, if the decision making module identifies that data is incompressible, it shall forward the data directly to VPN tunnel.
This was found in empirical measurements done in the simulation lab that bandwidth availability between two firewalls and compute resource utilization availability at firewall end are the right parameters for making decision for selective compression. The decision making module shall take decision whether data shall be compressed or shall be forwarded uncompressed to VPN tunnel for further transmission to remote side.
Compression Module: In standard site to site VPN configuration, the compression and decompression is part of the VPN algorithm. In this algorithm, it is being proposed that compression of data, if required, shall be executed before it enters into tunnel based on the recommendation of compression decision making module. This module provides functionality for data compression using pre-existing algorithm which is already used in the industry. This method does not propose any changes in the pre-existing compression algorithms. Since the data is being compressed selectively, there is a need to inform theremote side whetherdata is compressed or not. For this purpose, this is proposed to add header to each block which indicates whether the data iscompressed at sender side so that it is decompressed at remote side. At the remote side, the decompression algorithm reads the additional information in the block and decompresses the data. This method proposes to use one compression / decompression algorithm.
4. Methodology
Tomeasure the factors impacting the data transfer via site to site VPN, a simulation environment was created using 4 desktops with core i7 and 8 gb RAM specifications.
Centos version 7.5 were used on all the desktops. The experimental environment was created as shown in the block diagram represented in Figure 2. The two inner desktops were installed with libreswan(ver. 3.2) software to create site to site VPN using IPSEC.
This isolated environment was created dedicatedly for measuring the results so as to rule out any external impact due to internet line fluctuations, cloud service providerissues etc.
One mp4 format video file of size 715MB and one text file of 710 MB was used for data exchange. In this simulation environment, Video file in mp4 format is a pre compressed file whereas plain text is uncompressed data. VSFTP (ver 3.2) was used one side of the desktop and ftp was used on other side desktop for file transfer between two sides. Load generator was used to increase the utilization of CPU and memory. The bandwidth between two firewalls were restricted between 1000 mbps and 100 mbps to simulate the environment of under- utilization of bandwidth (700mb data sent from 1000mb link) and over utilization of bandwidth (700 mb data transferred from 100mb link) respectively.
The results were recorded in different scenarios of bandwidth, CPU and memory utilization.
Figure 2 Block Diagram of firewall connectivity.
Different compression algorithms have their respective characteristics, concepts and other factors affecting their performance [3]. The compression effectiveness can be termed as compression gain. There is a trade-off between compression gain and resource utilization while using different compression algorithms. The empirical measurement
show that the LZO is an algorithm which sacrifices the compression gain in lieu of speed;
however, this consumes higher memory usage while compression and decompression.
Bzip2 gives higher compression gain but at the cost of speed; however, memory usage is found to be comparatively less for compression and decompression processes. Gzipwas found to be well balanced compression algorithm [4]. In the simulation test lab environment, the testing was performed using gzip compression.
The environmental conditions are calculated at both end firewall systems. CPU and Memory utilization details are taken live from /proc in Linux based systems. The max value of the CPU utilization at both firewalls was considered by the decision making module. The same was repeated for memory utilization. The values were exchanged between two sides at regular intervals. The NBA value is dependent on four statistical parameters (Input / Output rate) collected from LAN and WAN interfaces of both firewalls. The network bandwidth availability is calculated using nload command in Linux based systems. However,WAN bandwidth availability (WBA)at WAN ports of both the firewalls was calculated by subtracting the utilization of the WAN interface as captured by nload from the speed configured at that interface. The minimum of the two values is recorded for further calculations.If the WAN interface speed is 100mbps and nload reports utilization of 55 Mbitsand 60 Mbitsper second at two WAN interfaces.
Then, min value i.e. 40Mbps is the WBA value between two WAN ports. The max of input rate at LAN interface of local firewall and output rate at remote firewall (MIO) is compared with the bandwidth available between WAN ports. Finally, the Network Bandwidth Availability (NBA) is calculated by subtracting MIO from WBA. This computed NBA valuewas considered by the decision making module for selecting the bandwidth available between two sides.
The decision making module needs to make decisions based on certain conditions of cpu, memory and network bandwidth availability. The permutation and combination of CPU, memory utilization and NBA are very large. Such decisions can be made at some discrete value combination. Unfortunately, determining the discrete value combination of three parameters for taking decisions at any point of time, shall be unrealistic and trivial task.Therefore, it was decided to define certain combinations of these parameters which are known to be highly significant in practical working conditions. The quantification of the matrix is depicted in Table 2. UBPC percentage was calculated as the number of Unique pattern bytes found with respect to the number of bytes observed (128 / 256 / 512 / 1024).
Table 1The quantification of matrix to select compression CPU Load
(%)
Memory Utilization (%)
NBA UBPC / Bytes
observed (%)
Compression (Yes / No)
1 -30 <80 >1 40 - 50 Yes
31-60 <80 >1 20 - 50 Yes
60 - 90 <80 >1 1 - 50 Yes
In case the CPU utilization is above 90% or memory utilization is above 80% or NBA- min is less than 20% or the UBPC %, the compression shall not be recommended.
However, in case the compression is successful at any of the peak range value, that peak range value in the matrix is improved by 5% for future communication or vice versa. This helps in moderating the table matrix on the fly for better results. This type of quantification in form of a matrix, assists in maintaining a relationship between the complex variables and the expected outcome. After continuous usage of this methodology and learning of the data, the table matrix shall be matured for better results.
While testing in simulation environment, hyper threading was disabled in the bios of all the desktops so that all four cores of i7 processor gives better performance on a single threaded operation.
5. Results and Analysis
The empirical measurements for Text file transmission under different internet bandwidth conditions (varying from 100mbps to 1000 mbps) were recorded with No VPN, VPN with disabled compression and VPN with enabled compression. Similarly, the measurements of video file for different conditions were also recorded. Then same experiments were repeated with selective compression algorithm. All the tests were repeated ten times and it was observed that there is less than 1% variation for repeated tests. The average value of ten repeated tests was taken into consideration for analysis. A comparative analysis of the time taken by Text file sent with VPN (Compression ON) and VPN with selective compression has been depicted in Figure 3 whereas the time taken by video file is mentioned in Figure 4.
Figure 3 Impact on Text File Transmission in varying Bandwidth Condition 0
20 40 60 80 100 120
100 200 300 400 500 600 700 800 900 1000
Time to transfer Text FIle (seconds)
WAN Interface Speed (Mbps)
VPN VPN with SC
Figure 4 Impact on Video File Transmission in varying Bandwidth Condition
The internet Bandwidth between two firewalls was restricted between 100 mbps to 1000 mbps as shown in horizontal axis of figure 3 and Figure 4. The vertical axis shows the time taken for file transmission. This is observed that the additional time taken by selective compression for both video and Text file varied from 0.7 sec to 1 second approximately. The impact of compression decreases due to increase in available bandwidth because all the compute resources (CPU and memory) is consumed by compression algorithm. This effect is more distinguishable for the incompressible text data. However, this adverse impact occurs even for pre-compressed Video format data, because the CPU and memory may become the bottleneck due to the computation required for compression, for higher internet bandwidth conditions.
This is visible in figure 3 and figure 4 that the file transmission under lower internet bandwidth conditions is almost similar in VPN (compression ON) and VPN (SC).
However, the VPN (SC) has performed better along with increase in bandwidth speed.
The improvement due to selective compression is depicted in Figure 5. An approximate time of improvement due to selective compression starts when available bandwidth is > 1;
for example 700 mbps file size transmission improves as shown soon as the bandwidth consumption crosses 700mbps speed. The similar experiments were repeated with smaller size (312mb Text file and 329mb Video file) transmission together at same instance of time. This was observed that the performance improvement analysis due to selective compression remains close to the one shown in figure 5.
0 50 100 150 200 250
Time to transfer Video FIle (seconds)
WAN Interface Speed (Mbps)
VPN VPN with SC
Figure 5 Improvement of data transfer due to selective compression
6. Related Work:
KrintzChandra and SucuSezgin have proposed adaptive compression environment (ACE) [5] which has used pre-computed data to choose between multiple methods of compression. For network bandwidth availability prediction, they have used Network Weather Service [6]. The pre-computed comparison model assumes a linear relationship between the average compression ratio and real time environment. However, some compression algorithms are non-linear for specific type of data. The authors of ACE have considered the last block assumption (LBA) which implies that previous and current data block inputs are similar and this may not be the real time scenario. The method of remote compression proxies may also improve the throughput performance of end users by the way of compression in the network [7] [8]. H Ning has proposed a method in which compression ratio and output ratio is computed from sample input data on the fly [9].
This may not prove to be productive in case the compressed output of sample is inaccurate and dissimilar. The compression ratio and compression rate are calculated based on the sample data known prior to data transmission which may not be the real time scenario. The author has used network bandwidth prediction system using Remos and NWS. Remos method provides prediction such as confidence level, error rate etc of data to its applications. However, it has been recommended in this paper to use real time network data statistics. The sample collection window timing may slide based on the real time network bandwidth usage which shall help in providing real time statistics without impacting the resource utilization of firewalls at both ends.
This is difficult to predict accurate estimates about of the file types. There is wide range of compression characteristics for binary files. Therefore its compressibility may
-20.00 -10.00 0.00 10.00 20.00 30.00 40.00 50.00 60.00 70.00 80.00 90.00
0 0 0 0 0 1 14 29 43
Improvement due to Selective COmpression (%)
Bandwidth Availablity at WAN interface (%)
Improvement for Video File
Improvement for Text File
not be predicted based on certain samples. To address the different characteristics of wide formats of the data, it has been recommended in this paper to check the unique byte pattern count in the input data blocks. This has also been recommended to use slider window for counting unique byte pattern amongst different block size of data ranging from 128 bits to 1024 bits so that better results may be obtained.The methods of calculating compression and decompression time and prediction of network bandwidth availability may be optimal to be used in dedicated appliance which are meant for performance optimization such as WAN accelerators. Any complex calculations done at the instance of selecting the right compression method may raise resource utilization values. Generally small and mid-size organizations use single gateway appliance (firewall) for multiple functions such as perimeter security, remote VPN, site to site VPN etc. SO the proposed algorithm of this work may be beneficial for such firewalls.
7. Conclusion and Future Work
The selective compression improves throughput of network communication done via secured method Virtual Private Network (VPN) by dynamically selecting the compressibility of data on the fly transparently.SC makes its decision by monitoring the data for compressibility. The performance due to selective compression strategy is similar to VPN with default settings of enabled compression at lower available bandwidth conditions. However, the solution shows very good improvement at higher internet bandwidth conditions. SC adapts itself to the changing environmental conditions at both the firewalls of site to site VPN. SC was evaluated for wide range of changes in the CPU, memory, network bandwidth etc.Finally, the proposed algorithm may perform well across wider range of realistic data and resource utilization.
The proposed selective compression method is foundation model for future enhancement. Dynamic methods for quantification levels or using multiple levels for data characterization may improve the performance further. There may be some overhead due to increased monitoring and modelling of resource utilization techniques. There needs to be trade-off between monitoring, modelling, prediction and performance improvement.
While concluding, it can be stated that in comparison to any static configuration of VPN, selective compression algorithm improves efficiency and performance in a wide range of environment with varied data types. It has significant potential for enhancement of secured data transfer in real world.
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Authors
Dinesh Taneja is research associate at ManavRachna International Institute of Research and Studies, Faridabad. He has keen interest in Information Security and performance analysis for data in motion with respect to the data security. He has designed campus IT Infrastructure and data centers consisting of heterogeneous equipment. His main areas of interest are cloud computing, low cost high available clusters and information security. He has vast experience of more than 20 years in the domain of enterprise solution architecture. He did his Master of Engineering in computer technology and applications from Delhi College of Engineering in 1999.
Prof. S. S. Tyagi is presently working as a Professor, Computer Engg.
and Head of the Department of FCA, ManavRachna International Institute of Research and Studies (MRIIRS), Faridabad. He is former HOD Department of Computer Sc. &Engg. and also of Deptt. of Information Technology. He completed his Ph.D in Computer Science and Engineering from Kurukshetra University, Kurukshetra in the year 2010. He did his M.E from BITS Pilani in the year 2002 and B.Tech in Computer Technology from Nagpur University, Nagpur in 1992. He is having an experience of more than 25 years including 4 years of industrial and 21 years of academic/teaching experience. He has been holding various academic and administrative positions during his career.
He is having a vast experience of teaching for the students of B.Tech, M.Tech, MCA and Ph.D.
