Analysis of RBQ: a new cooperative web caching mechanism that adapts to link congestion

2003 ◽  
Author(s):  
Eugenio M. de la Rosa ◽  
John H. Hartman ◽  
Terril Hurst
Keyword(s):  
Web Caching ◽  
Caching Mechanism ◽  
Link Congestion

scholarly journals Accelerating the Internet in the presence of Big Data: Reducing user delays by leveraging historical user request patterns for web caching

2019 ◽  
Vol 16 ◽  
pp. 1-10
Author(s):  
Chetan Kumar ◽  
Sean Marston
Keyword(s):  
Big Data ◽  
Large Scale ◽  
Web Caching ◽  
The Internet ◽  
Proxy Caching ◽  
Data Usage ◽  
User Request ◽  
Large Scale Networks ◽  
Caching Mechanism ◽  
Significant Intensification

Approximately 4 billion people have access to the Internet, additionally 23 billion devices are connected as of 2018. This has allowed for a substantial growth in data collection which has allowed for Big Data to flourish. The continued increase in user, devices, and Big Data usage has created a significant intensification in Internet traffic. This in turn has the potential to increase user delays when accessing data on the Internet. There are a number of ways to help reduce user latency, web caching is able to reduce web user delays in addition to reducing network traffic and the load on web servers. In this study we propose a proxy level web caching mechanism leveraging historical web patterns to help reduce user latency and accelerate the Internet. In addition we survey the state of the art of other caching approaches. Our investigation shows that using historical patterns as part of a proxy caching mechanisms in large scale networks can significantly shorten the latency for users in this era of Big Data

A new approach for a proxy-level web caching mechanism

2008 ◽  
Vol 46 (1) ◽  
pp. 52-60 ◽  
Author(s):  
Chetan Kumar ◽  
John B. Norris
Keyword(s):  
Web Caching ◽  
New Approach ◽  
Caching Mechanism

Improving Hit Ratio and Byte Hit Ratio Using Combined Pre-Fetching and Web Caching

2014 ◽  
Vol 9 (8) ◽  
pp. 1426
Author(s):  
Baskaran Rajendran Kuttuva ◽  
Kalaiarasan Chellan
Keyword(s):  
Web Caching

Surviving switch failures in cloud datacenters

2021 ◽  
Vol 51 (2) ◽  
pp. 2-9
Author(s):  
Rachee Singh ◽  
Muqeet Mukhtar ◽  
Ashay Krishna ◽  
Aniruddha Parkhi ◽  
Jitendra Padhye ◽  
...  
Keyword(s):  
Operating System ◽  
Network Traffic ◽  
Survival Rates ◽  
Cloud Provider ◽  
Significant Heterogeneity ◽  
Power Losses ◽  
Software Bugs ◽  
Hardware Impairments ◽  
Link Congestion

Switch failures can hamper access to client services, cause link congestion and blackhole network traffic. In this study, we examine the nature of switch failures in the datacenters of a large commercial cloud provider through the lens of survival theory. We study a cohort of over 180,000 switches with a variety of hardware and software configurations and find that datacenter switches have a 98% likelihood of functioning uninterrupted for over 3 months since deployment in production. However, there is significant heterogeneity in switch survival rates with respect to their hardware and software: the switches of one vendor are twice as likely to fail compared to the others. We attribute the majority of switch failures to hardware impairments and unplanned power losses. We find that the in-house switch operating system, SONiC, boosts the survival likelihood of switches in datacenters by 1% by eliminating switch failures caused by software bugs in vendor switch OSes.

Routing algorithm of minimizing maximum link congestion on grid networks

2014 ◽  
Vol 21 (5) ◽  
pp. 1713-1732 ◽  
Author(s):  
Jun Xu ◽  
Jianfeng Yang ◽  
Chengcheng Guo ◽  
Yann-Hang Lee ◽  
Duo Lu
Keyword(s):  
Routing Algorithm ◽  
Grid Networks ◽  
Link Congestion
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