Comparing NDN and CDN Performance for Content Distribution Service in Community Wireless Mesh Network Adisorn Lertsinsrubtavee
Preechai Mekbungwan
Internet Education and Research Laboratory Asian Institute of Technology Thailand
Internet Education and Research Laboratory Asian Institute of Technology Thailand
[email protected]
[email protected]
Nunthaphat Weshsuwannarugs Internet Education and Research Laboratory Asian Institute of Technology Thailand
[email protected] ABSTRACT
General Terms
Content distribution has recently become a predominant service on the current Internet while the early Internet architecture was not designed for scalable content delivery. In this paper, we address the issue of content delivery in community wireless mesh networks (CWMN) with narrow and unstable wireless links providing services to communities, where each community represents a small number of users as compared to the normal Internet. To provide high quality content such as high definition video content to these communities, we propose to attach cache storage to each router on the network and use these distributed storage to enhance the delivery of the content. Two different content delivery methods, designed to accommodate scalable content delivery on the Internet, are compared on CWMN by this study. One is a pull based distributed cache or Named Data Networking (NDN) and another is the push based, Content Distribution Network (CDN). In the former approach data chunks get cached along the path to the requesting user while for the latter approach contents are pushed as close to the users as possible a priori. In this paper, we present an experimental performance evaluation in a laboratory environment where the distribution of content and request patterns were based on the log files collected from real village CWMN in the rural area north of Thailand.
Experimentation, Measurement
Keywords Community Wireless Mesh Network, Content Distribution Network, Named Data Network
1.
INTRODUCTION
The emergence of content delivery services has significant impacts on the current Internet architecture. This is posing new challenges related to the new design of future Internet architecture. As a consequence, several caching technologies are proposed to support scalable content delivery aiming at reducing the redundancy traffic in the Internet. Web proxy is the early adopted solution for caching [14]. The popular or attractive contents such as websites can be cached in web proxy server. However, the problem is then lacking of transparency, users may need to specify manually the address of proxy. Furthermore, multiple connections may simultaneously connect to the server. This leads to the presence of well known flash crowd problem generating undesired consequence to the system such as crashed or unusual high response time [10]. Content Delivery Network (CDN) is proposed to alleviate the problems of web proxy while aiming to push the contents as close to the users as possible a priori. The dynamic contents like video on demand and live streaming are replicated at the multiple replica servers in different location. CDN seems to be a good solution to reduce the redundancy traffic while allowing users to retrieve the desired contents from the nearest replica server. Nevertheless, there are some limitations that hinder CDN to support the dynamic content distribution. First, CDN has no fine-grained control where to place the replica server aiming to quickly response to users’ request. Second, caching in CDN is done statically where the contents must be stored in the replica servers beforehand. This leads to high possibility to
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[email protected]. AINTEC’14, November 26–28, 2014, Chiang Mai, Thailand. Copyright 2014 ACM 978-1-4503-3251-4/14/11 ...$15.00. http://dx.doi.org/10.1145/2684793.2684800 .
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increase the cache miss, if users’ interest is highly dynamic. Named Data Networking (NDN) is recently introduced in research community that aims to overcome the limitations of content delivery services [8]. NDN captures those problems and provides optimal solution through the name resolution and dynamic network caching which allow contents to be stored and retrieved from anywhere in the network. The NDN approach shifts away from the current IP-based to the new naming address scheme. Instead of specifying the host address of the source and destination, NDN assigns a unique name for each data chunk, which represents the identifier of the content. Thus, users are only interested in what the content is rather than where the content is located. The content delivery in NDN is driven by two message types: Interest and Data messages. The former indicates the name of desired content that user is willing to retrieve. The latter contains the content payload satisfied the content name request. The Interest message is forwarded to the next hop until reaching either the original content producer or a router that has the matching content. As a consequence the Data message is returned back to the requesting user with the same path that the Interest message came from. In addition to the content distribution service, NDN enables the dynamic caching and reuse of content in the network [19]. Specifically, each router on the network is equipped with a cache storage while dynamically caching the content that flows though it from the path between content source and the requesting user. As a result, the routers along the path can dynamically store the data chunks to their cache which can subsequently serve the future requests. Following this approach, the content requests are not necessarily delivered from some central servers or the original content producer, but they are instead served by the closet router owning the matching content. Apparently, NDN approach truly benefits from this dynamic caching to keep the content closest to the interests of the users in its proximity as locations of NDN objects are closely related to the requests from the users. In this paper, we aim to evaluate the performance of NDN and CDN as content distribution services over the Community Wireless Mesh Network (CWMN). We have implemented the real testbed in our laboratory supporting both NDN and CDN technologies. Our objective is to provide high quality contents such as high definition video to the CWMN where the users could view the content from the nearest cache. Furthermore, log files collected from real village CWMN are applied to emulate the distribution of content and request patterns for our experiments. Our results provide useful insights on how the content services can benefit from the NDN architectural advantages. In particular, we show some limitations of NDN that hinder to achieve the better
service in terms of the round trip time delay. However, we found that NDN can reduce the number of cache miss and also the amount of bandwidth usage in overall network. Finally, we learnt that when the total cache miss is the same, NDN can be more effective than CDN in terms of the total amount of cache usage.
2.
RELATED WORKS
During the last decade, there are many approaches targeting for the architecture of the future Internet. Those approaches including NDN can be categorized as the Information Centric Network (ICN) or Content Centric Network (CCN) research area [4]. However, the first ideas mentioned about this concept should be credited to the seminal paper of Gritter and Cheriton [7]. An extensive survey on research in ICN is presented in [18]. In this survey, the authors classify the key functions of various ICN approaches and highlight their similarities and differences. The alternate approach like Content Distribution Network (CDN) is also widely studied and deployed in the current Internet services (e.g., Akamai, Limelight) [13, 16]. However, due to lacking of collaboration of multiple CDN service providers, CDN approach has limitation on how to place their services and contents to support users [5]. In [12], the linear integer optimization model is formulated in order to evaluate and compare the caching performance of CCN and CDN. The results show that CDN achieves slightly lower bandwidth usage than CCN when the amount of caching storage deployed in the topology is the same. The performance evaluation of CCN on the real testbed using the Open Network Laboratory (ONL) is presented in [19]. This work compares the performance of large file transfer between the classic HTTP proxy and CCN technology. As a result, the average download times for CCN is longer than the HTTP proxy. However, in the lossy network condition, CCN can benefit from distributed cache on each router to achieve lower average download time compared to the HTTP proxy. To identify the behavior of content distribution, the traffic measurement from the real platform is also important. In [9], the authors measure the traffic usage from a rural wireless mesh network in Zambia. The analysis shows the amount of bandwidth usage and also the classification of popular websites being accessed by the villagers. Similar work is also found in [11], but the observing wireless network is in the university campus.
3.
CONTENT DISTRIBUTION IN TAKNET CWMN
Community Wireless Mesh Network, or CWMN, is a form of self-configuring, self-healing wireless network that is easy to deploy and maintain. CWMN can be easily adapted to provide intranet services in isolated rural 44
communities, at a significantly lower cost when compared to individually and directly connected Internet services. Our CWMN deployment, known as TakNET, is located in Mae-Sot district of Tak province, a rural area north of Thailand [15]. TakNET is a network of mobile routers forming mobile ad hoc network, using OLSR protocol, with small cache storage attached (16GB USB flash drive). There are 10 mobile routers deployed in TakNET and per-node 16GB storage allows the CWMNs of TakNET have the CWMN-wide total storage of approximately 160GB. Such a vast amount of storage is also distributed almost evenly within each CWMN’s deployed area. Figure 1 shows the geo-location of the mobile routers deployed in TakNET CWMN. To cope with high VoD demand bandwidth, we experiment with content push and replication during off-peak hours at night. The Internet gateway can be used to push or pass new video contents into our CWMN just once to the Master Content storage (i.e. the Raspberry Pi ) by remote copying of new files to specified folders on the Master Content storage.
Figure 3: Fraction of data usage by mobile router that there were many active users using our service in the MR2’s area. On the other hand, MR7 has the lowest data usage compared to the others, since only few user accessed to our network.
4.
EXPERIMENTAL SETUP
In this section, we first introduce the hardware platform and software tools deployed in our NDN and CDN testbed. As a consequence, we explain how we extract the content distribution from the logs and conduct the experiments through the testbed.
4.1
Testbed
To validate the performance of NDN and CDN approaches, we conduct the experiment through real equipments and softwares. Initially, we deployed 10 mobile routers in TakNET CWMN. However during the period that we collected the traffic logs, some mobile routers were not active. This is because some villagers in the TakNET turned off our mobile routers to economize their electricity bills. Finally, there were only 7 active mobile routers remaining in our log files.
Figure 1: Geographical view of TaKNET CWMN To better understand the behaviour of Internet usage in TakNET CWMN, we analyze a set of Internet gateway trace logs and mesh network logs from each mobile router during 22 - 24 August 2014. Figure 2(a) shows the total amount of data usage as well as the total megabytes sent and received at the gateway. Besides, we also collected traffic activity in TakNET by running tcpdump at the gateway and filtered the http get-request messages from the logs. As a result, the top 24 site domains visited by villagers in TakNET is illustrated in Figure 2(b). The most top visited domain is referred to the Facebook which has hit accesses up to 27.61% of the total requests. By measuring the megabytes sent and received, we are able to capture the amount of traffic generated from each mobile router. Figure 3 classifies the fraction of data usage of mobile routers in TakNET CWMN. This helps us to understand how the contents from the Internet are distributed through our network. For instance, MR2 is indicative of the major traffic that appears in our measurement (59.4% of the total). Due to the fact
MR6 MR2
MR5
MR7
MR1
MR3
MR4
Content Server
Figure 4: Topology setting in laboratory For the experimentation, we deploy one master content server and seven mobile routers in our laboratory testbed, while setting the topology as close as the real topology in TakNET (see Figure 4). These mobile routers are installed the OpenWRT Linux firmware, Attitude 45
(a) Total amount of data usage at the gateway
(b) Fraction of requests by web accessed
Figure 2: Traffic analysis of TakNet CWMN Adjustment 12.04 [2] running on Atheros AR71xx WiFi chipset with two virtual wireless interfaces (IEEE 802.11 /b/g/n). The first interface is configured in access point (AP) mode providing WiFi connectivity to clients. The second interface is configured in ad hoc mode for communication among mobile routers while running OLSR routing protocol [6]. Besides, each mobile router equips with 16 GB flash memory which is an extension storage for softwares and content caching. As for the master content storage, it operates the Linux Ubuntu 14.04 LTS running on AMD E-450 Processor 1.6 GHz. MR1 connects to the content server via Ethernet interface and performs as a gateway for other MRs. We note that in CWMN, residential Internet access is indicatively costly compared to the income levels of rural people. This challenge leads us to the cost-effective solution which could support our content delivery service in CWMN. Content Distribution Network (CDN) approach seems to be a good solution for content delivery service in CWMN . However, some complex caching mechanisms of modern CDN require higher cost in terms of hardware resources such as powerful server or high performance router. Therefore, in our first deployment of CDN-CWMN, we apply only a simple caching function where some selected contents are cached statically inside the mobile router. On the other hand, Named Data Networking (NDN) approach is already integrated the pull based distributed cache where content chunks get cached along the path to the requesting users. This feature can be deployed easily to the small low costcost mobile routers by adding an external memory for caching space.
4.2
and LRU respectively. In order to run the NFD software on our mobile routers, some modifications in nfd configuration are mandatory. One of the key modifications is referred to the setting of maximum size of content store. By default, the size of content store is set as 65536 packets that requires RAM memory about 500 MB. However, our mobile router has total RAM memory of approximately 32 MB. To avoid the system crashes on mobile router, the size of content store is reduced to 100 packets which is about 1 MB. The ndn-traffic-generator tool is used for generating Interest and Data messages in the NDN network [1]. The client and server tool apply the configuration in ndn-traffic.conf to generate the pattern of NDN traffic. In our scenario, we run the ndn-traffic-client inside the mobile router to generate the Interest messages and run the ndn-traffic-server inside the content server to response the Interest message with matching Data message.
4.3
CDN Implementation
For the performance evaluation of CDN system, we deploy the lightweight HTTP server lighthttpd-1.4.30 to cache contents inside the mobile routers and the content server. The wget program is used for requesting and downloading contents during the experiment. Inside the mobile router, we create a script consisting of a sequence of wget commands specified the request pattern of clients.
4.4
Configuration
In this section, we explain how the emulation of NDN and CDN content distributions are configured via the log files from TakNET CWMN.
NDN Implementation
To perform content distribution in NDN environment, we install the NFD-0.2.0 distribution [3] in all mobile routers and also the content server. In our experiments, the forwarding strategy and cache replacement policy are configured as default setting which are Best route
4.4.1
Traffic Pattern
We emulate traffic pattern for our laboratory setting by considering a HTTP request in TakNet CWMN as a content request. The domain name of each website men46
tioned in Figure 2(b) is identified by simple unique name in our setting. For instance, ∗.f b.me, ∗.googlevideo.com and ∗.youtube.com are named as ContentA.txt, ContentB.txt and ContentC.txt respectively. Due to memory constraints on our mobile router, we first consider the small file size in our experiments and let the large file size content distribution with proper storage management for the future work. Thus, in our scenario, for the purpose of comparing relative performances, a size of each content is fixed as 8KB. Notice that our main objective is to study the benefit of distributed caching. Hence, the size of content may not have significant impacts on this scenario. However the distribution of content over the network is more important for evaluating the performance of NDN and CDN. Without loss of generality, we assume that client’s traffic is directly generated from mobile router. Moreover, we also consider that all mobile routers have the same pattern of content distribution. In other word, all mobile routers apply the fraction of requests as presented in Figure 2(b). For example, the percentage of requesting ContentA.txt is configured as 27.61%. Nevertheless, each mobile router generates different number of request content regarding the proportion amount of workload as illustrated in Figure 3. The calculation of the total number of content request will be discussed in the next subsection.
4.4.2
Table 1: Traffic configuration on each mobile router MR 1 2 3 4 5 6 7
4.4.3
Even all mobile routers apply the same request pattern, the amount of content request from each mobile router must be different regarding their workload. Therefore, we compute the number of content request (Ri ) for each mobile router individually. This calculation is based on bandwidth usage (BW ) and total amount of request content (Rtotal ) from TakNET CWMN as presented in section 3. Accordingly, the number of content request generating by mobile router i can be expressed as follows:
5.
Cache configuration
PERFORMANCE EVALUATION
In this section, we analyze the performance of NDN approach through the implemented testbed using NFD softwares and tools. The performance of NDN is compared to the CDN approach where the platform is implemented based on the lighthttpd server and wget program. In addition, we further improve the CDN performance by setting all mobile routers as the replica server so the comparison to NDN is not biased. Hence, each mobile router in CDN scenario has ability to cache the content as the node in NDN system. Besides, the running time is set as 1 hour in all experiments.
(1)
Notice that BWi is a fraction of data usage of mobile router i compared to the total traffic of the network (see Figure 3). Apart from the number of request content (Ri ), Intervali is defined as an interval time for sending each content request which can be computed as follows: T ime Intervali = Ri
Intervali (s) 23.45 1.59 5.01 9.53 24.23 25.61 809.19
To perform content distribution service, all 24 content files are stored in the content server, and the clients from each mobile router attempt to request these contents simultaneously regarding the configuration from the previous section. Apart from storing the contents in the content server, CDN technology allows the replica server to cache some selected content beforehand. In our scenario, we choose top three popular contents (e.g. ContentA.txt, ContentB.txt and ContentC.txt) to be cached in the replica server before running the experiment. In fact, the fraction of requests for these three contents is about 55.8% of the total number of requests in our log files (see Figure 2(b)). This should be a good tradeoff to justify the number of caching content in our scenario. As for the NDN case, cache storage of each mobile router is initialized as empty. During the experiment, if there is a content flows through it from the path between content source and the requesting user, the mobile router will cache this content automatically.
Total Number of Content Request and Sending Interval
Ri = Rtotal × BWi
Ri (times) 153 2259 718 378 149 141 4
5.1
Performance on Content Delivery
We first evaluate the performance of the distribution of top 24 popular contents in TakNET using NDN and CDN technology. The Round Trip Time delay (RTT) is measured when a user sends a request until the content is completely delivered to the requesting user. Figure 5 shows the average RTT of all contents being requested during the experiment on each mobile router. The results show that CDN approach achieves better
(2)
where T ime is a total time for running the experiment. In this study, we scale down the observation period of the logs file from TakNET by setting T ime to 1 hour. Table 1 shows the number of request content and sending interval of each mobile router. 47
RTT compared to the NDN. As a matter of fact, we deploy the CDN platform by using two widely used commercial grade tools: the lighthttpd web server and wget program which have experienced many years for development and performance-tuning.
for the CDN, the cache miss ratio is maximum, since all requested contents of MR2 are not the popular contents which are not stored in the cache beforehand. 1
0.8
Cache Miss Ratio
25 NDN CDN 20
Average RTT (ms)
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Figure 6: Comparing cache miss ratio between NDN and CDN
Mobile Router
Figure 5: Average round trip time for content delivery
In addition, we also investigate the caching performance of CDN while varying the cache buffer size of each replica server from 0 to 24. Figure 7 shows the cache miss ratio of CDN compared with the cache size of mobile router. As expected, cache miss is consistently decreased when cache buffer size is increased. To explore the optimal value of cache buffer size, we base the cache miss performance of CDN with the average cache miss of NDN. Regarding the cache miss ratio of NDN in Figure 6, the total cache misses collected from all mobile routers is 122 times which is approximately 3.2% (total content request is 3802) In order to achieve the same cache miss ratio as NDN (0.032), the CDN requires the number of cache contents up to 75% of all request contents (18 from 24) which is not straightforward for the dynamic environment where users usually have different interests. In this context, it will require a large volume of storage on each mobile router to support the cache buffer size.
Furthermore, performing lookup of content names in NDN increases the computational overhead of packet forwarding as also mentioned in [17]. However, the mobile routers used in this experiment are lightweight and have limited resources in terms of memory and CPU processor, thus the performance of NDN can be affected by these implicit factors.
5.2
Impact on Cache Functionality
In this section, we measure the cache miss ratio which is a ratio of cache miss rate compared to the number of content request. The cache miss rate is counted when a request message fails to find the content from the cache inside mobile router. Figure 6 shows the cache miss ratio of each mobile router. Clearly, the pull based distributed cache of NDN outperforms the push based of CDN. As mentioned before, NDN applies the dynamic distributed cache to provide high accessibility for the users while reducing duplications and redundancies. On the contrary, CDN approach has limited choice to choose the content to cache in the mobile router which could not support all users’ interest. Therefore, several requests must be forwarded to the the main content server. In practice, users’ behavior may change dynamically, and users usually have different interests for requesting content. Thus, the pull based content delivery like NDN is capable for dynamic caching and possible placing the request content at the right mobile routers defined by the frequently requested path. Notice that, cache miss ratio of MR7 is significantly high in both NDN and CDN scenarios. This is because MR7 has only 4 content requests during the experiment and only two of them are the same content. Thus the cache miss ratio of MR7 is increased up to 0.75 in NDN case. As
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Figure 7: Varying cache size in CDN
48
5.3
Impact on Bandwidth Usage
fied content requests. Here, we define active node as a mobile router generating the content requests. In this experiment, we increase the number of active node from 1 to 7 . Note that all mobile routers still apply the same configuration as the previous section. In the first experiment, we start by MR4 (at the rightmost of topology) to send request towards the content server by allowing content can be cached along the reverse path forwarding. Finally, we increase the number of active nodes from right to left respectively.
In this section, the total byte sent on T x interface of each node is measured. As expected, the overall bandwidth usage of CDN is significantly increased compared to the NDN. This is because the high cache miss ratio of CDN leading to several requests must be forwarded to the central central. Intuitively, redundancy traffic is generated in the network. Figure 8 shows bandwidth usage from all devices in our experiment including content server and all mobile routers (MR1 to MR7). The bandwidth usage measured from MR5, MR6 and MR7 are quite similar in both CDN and NDN. Due to the fact that these mobile routers have small number of content requests. For instance, MR7 requests only 4 contents during the experiment. On the other hand, considering the nodes which have larger number of content requests (i.e. MR1 and MR2), CDN abruptly consumes more bandwidth than NDN.
Cache Usage (contents)
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Figure 9: Cache usage
6
Figure 9 shows the summation of cached usage of all mobile routers. Generally, the cache usage of NDN is increased when the multiple active nodes are introduced. However, when the number of active nodes is increased from 2 to 4, the number of cache usage remains unchanged. Considering the case of the number of active nodes is 4 (i.e. MR1-MR4), these active nodes are located in the same path towards the content server (see Figure 4). Intuitively, they can benefit from the reverse path forwarding by retrieving the content directly from the caches. For instance, MR4 firstly requests the ContentA.txt from the content server, this content will be stored in all mobile routers along the reverse path forwarding (MR1, MR2, MR3 and MR4). Consequently, when MR3 requests ContentA.txt, it can directly retrieve from the content store without further forwarding request. As for the CDN case, all mobile routers apply the same cache buffer size, since they are all configured as the replica server. According to the analysis from Figure 7, a value of 18 is considered as the optimal cache buffer size, since CDN can achieve the same cache miss ratio as NDN. Therefore the total cache usage of CDN can be computed as 126 (18X7). Apparently, NDN can reduce the cache buffer effectively compared to the CDN, since the former can benefit from the dynamic distributed caches which intuitively reduces duplications and redundancies. Even in the worse case scenario where the number of active nodes is 7, NDN still uses lower cache buffer size than CDN around 0.8%.
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Node
Figure 8: Bandwidth usage The reason are two folds: first it is possible that a mobile router generating large number of content requests usually produces high cache miss rate. Therefore, a lot of content requests must be generated and forwarded to the content server. Second, MR1 and MR2 are the intermediate nodes between other MRs and the content server. Apparently, these two mobile routers have to carry a large volume of traffic. However, NDN is powerful in dynamic caching, since the content can be cached along the path regarding the frequently request. Thus, the edge mobile router such as MR6 can directly retrieve the contents from its neighbors without unnecessary route towards the content server. Furthermore, NDN can also reduce redundancy traffic at the content server, since the contents can be dynamically distributed along the path. This is proven that NDN can alleviate the well known flash crowd problem.
5.4
Observing the Cache Usage
In this section, we evaluate the efficiency of caching function of NDN by considering how many contents were cached in the network after delivering all satis49
6.
CONCLUSIONS
[6] T. Clausen and P. Jacquet. Optimized link state routing protocol (OLSR). RFC 3626, October 2003. [7] M. Gritter and D. R. Cheriton. An Architecture for Content Routing Support in the Internet. In Proceedings of the 3rd Conference on USENIX Symposium on Internet Technologies and Systems, USITS’01, 2001. [8] V. Jacobson, D. K. Smetters, J. D. Thornton, M. F. Plass, N. H. Briggs, and R. L. Braynard. Networking Named Content. In Proceedings of the ACM CoNEXT ’09, 2009. [9] D. L. Johnson, E. M. Belding, K. Almeroth, and G. van Stam. Internet Usage and Performance Analysis of a Rural Wireless Network in Macha, Zambia. In Proceedings of the 4th ACM Workshop on Networked Systems for Developing Regions, NSDR ’10. ACM, 2010. [10] J. Jung, B. Krishnamurthy, and M. Rabinovich. Flash Crowds and Denial of Service Attacks: Characterization and Implications for CDNs and Web Sites. In Proceedings of the 11th International Conference on World Wide Web, WWW ’02, pages 293–304. ACM, 2002. [11] M. R. Lindsey, M. Papadopouli, F. Chinchilla, and A. Singh. Measurement and analysis of the spatial locality of wireless information and mobility patterns in a campus, 2003. [12] M. Mangili, F. Martignon, and A. Capone. A comparative study of Content-Centric and Content-Distribution Networks: Performance and bounds. In GLOBECOM, 2013 IEEE, Dec 2013. [13] G. Pallis and A. Vakali. Insight and perspectives for content delivery networks. Communications ACM, 49(1):101–106, Jan. 2006. [14] M. Rabinovich and O. Spatschek. Web Caching and Replication. Addison-Wesley Longman Publishing Co., Inc., Boston, MA, USA, 2002. [15] A. Tunpan, N. Tansakul, and K. Kanchanasut. TakNET: A Rural Community Wireless Mesh Networking. http://www.interlab.ait.asia/TakNet. [16] A. Vakali and G. Pallis. Content Delivery Networks: Status and Trends. IEEE Internet Computing, 7(6):68–74, Nov. 2003. [17] M. Varvello, D. Perino, and J. Esteban. Caesar: A content router for high speed forwarding. In Proceedings of the ICN Workshop on Information-centric Networking, ICN ’12, 2012. [18] G. Xylomenos, C. Ververidis, V. Siris, N. Fotiou, C. Tsilopoulos, X. Vasilakos, K. Katsaros, and G. Polyzos. A Survey of Information-Centric Networking Research. Communications Surveys Tutorials, IEEE, 16(2):1024–1049, Second 2014. [19] H. Yuan and P. Crowley. Experimental evaluation of content distribution with NDN and HTTP. In INFOCOM, 2013 Proceedings IEEE, April 2013.
In this paper, we study the performance of content distribution service in NDN environment while comparing to the classic CDN technology. The testbed for both technologies are implemented and configured in our laboratory. Our experimental results show that NDN cannot achieve better performance in terms of content delivery time. However, the results demonstrate the benefit of dynamic caching function of NDN. Interestingly, NDN generates less cache miss ratio than the CDN leading to lower bandwidth usage in overall network. Besides, we further investigate the efficiency of dynamic distributed cache function and found that the total amount of cache usage of NDN is less than the CDN case. As a matter of fact NDN is more dynamic to choose the location of the distributed cache. Due to the constraint on cost issues, the deployment of a more complex CDN may not be feasible in our scenario. This leads to minimal performance in terms of caching efficiency. However the comparison between CDN and NDN is still fair, since both approaches are evaluated under the same environment and conditions. This guides us to inform the the practical limits and benefits of architecture design of both approaches. To this end, it is interesting to apply this useful insight to design more scalable CWMN system. In future work, we plan to enhance the capability of content store by integrating the repository server inside the mobile router. Currently, the content store is mainly used the memory from RAM of mobile router. This is not straightforward, since small mobile router typically has very limited RAM memory. Therefore, we are implementing the repository server using memory from USB flash drive. Finally, it will be possible to delivery the large file size such as high definition video content through the mobile router.
7.
REFERENCES
[1] Named Data Networking/ndn-traffic-generator. https://github.com/named-data/ndn-trafficgenerator. [2] OpenWrt, a Linux distribution for embedded devices. https://openwrt.org, April 2013. [Online; accessed 8-September-2014]. [3] NFD - Named Data Networking Forwarding Daemon 0.2.0 documentation. http://named-data.net/doc/NFD/0.2.0/, August 2014. [4] B. Ahlgren, C. Dannewitz, C. Imbrenda, D. Kutscher, and B. Ohlman. A survey of information-centric networking. Communications Magazine, IEEE, 50(7):26–36, July 2012. [5] G. Carofiglio, G. Morabito, L. Muscariello, I. Solis, and M. Varvello. From content delivery today to information centric networking. Computer Networks, 57(16):3116 – 3127, 2013. 50
