Analyzing Effect of Edge Computing on Reduction of Web Response Time

Modern webpages consist of many rich objects dynamically produced by servers and client terminals at diverse locations, so we face an increase in web response time. To reduce the time, edge computing, in which dynamic objects are generated and delivered from edge nodes, is effective. For ISPs and CDN providers, it is desirable to estimate the effect of reducing the web response time when introducing edge computing. Therefore, in this paper, we derive a simple formula that estimates the lower bound of the reduction of the response time by modeling flows obtaining objects of webpages. We investigate the effect of edge computing in each webpage category, e.g., News and Sports, using data measured by browsing about 1,000 popular webpages from 12 locations in the world on PlanetLab.

[1]  Gustavo Alonso,et al.  Analysis of Caching and Replication Strategies for Web Applications , 2007, IEEE Internet Computing.

[2]  Ramesh K. Sitaraman,et al.  The Akamai network: a platform for high-performance internet applications , 2010, OPSR.

[3]  Steve Souders,et al.  High performance web sites - essential knowledge for frontend engineers: 14 steps to faster-loading web sites , 2007 .

[4]  Hao Hu,et al.  Improving Web Sites Performance Using Edge Servers in Fog Computing Architecture , 2013, 2013 IEEE Seventh International Symposium on Service-Oriented System Engineering.

[5]  Noriyuki Takahashi,et al.  Analysis of Process Assignment in Multi-tier mobile Cloud Computing and Application to Edge Accelerated Web Browsing , 2015, 2015 3rd IEEE International Conference on Mobile Cloud Computing, Services, and Engineering.

[6]  William E. Weihl,et al.  Edgecomputing: extending enterprise applications to the edge of the internet , 2004, WWW Alt. '04.

[7]  Vyas Sekar,et al.  Understanding website complexity: measurements, metrics, and implications , 2011, IMC '11.

[8]  Masayuki Murata,et al.  Investigating structure of modern web traffic , 2015, 2015 IEEE 16th International Conference on High Performance Switching and Routing (HPSR).

[9]  Qun Li,et al.  Fog Computing: Platform and Applications , 2015, 2015 Third IEEE Workshop on Hot Topics in Web Systems and Technologies (HotWeb).

[10]  Sateesh Addepalli,et al.  Fog computing and its role in the internet of things , 2012, MCC '12.

[11]  John P. Rula,et al.  Content delivery and the natural evolution of DNS: remote dns trends, performance issues and alternative solutions , 2012, Internet Measurement Conference.

[12]  Wolfgang Mühlbauer,et al.  Web content cartography , 2011, IMC '11.

[13]  Amit Aggarwal,et al.  Computing on the Edge: A Platform for Replicating Internet Applications , 2003, WCW.

[14]  David Wetherall,et al.  Demystifying Page Load Performance with WProf , 2013, NSDI.

[15]  David R. Choffnes,et al.  Drafting Behind Akamai: Inferring Network Conditions Based on CDN Redirections , 2009, IEEE/ACM Transactions on Networking.

[16]  Nick Feamster,et al.  Answering “What-If” Deployment and Configuration Questions With WISE: Techniques and Deployment Experience , 2008, IEEE/ACM Transactions on Networking.

[17]  Turcotte Ja,et al.  When seconds count. , 1990 .

[18]  James Mickens.  Silo: Exploiting JavaScript and DOM Storage for Faster Page Loads , 2010, WebApps.

[19]  Nick Feamster,et al.  Characterizing and Mitigating Web Performance Bottlenecks in Broadband Access Networks , 2013 .

[20]  Ron Kohavi,et al.  Online Experiments: Lessons Learned , 2007, Computer.

[21]  Weisong Shi,et al.  A survey on dynamic Web content generation and delivery techniques , 2009, J. Netw. Comput. Appl..

[22]  Keqiang He,et al.  Next stop, the cloud: understanding modern web service deployment in EC2 and azure , 2013, Internet Measurement Conference.