# A Scalable Parallel Architecture Based on Many-Core Processors for Generating HTTP Traffic
## 2 Background and Challenges
### The Existing Approaches of Traffic Generation  
Accurate modeling and generation of a realistic network workload are difficult and challenging tasks because of heterogeneity, scale and complexity of the current Internet. In literature, a substantial number of works focus on the modeling and simulation of traffic generation. At present, the traffic generation has the following methods:  
1. Traffic Replay [15; 16]  
Traffic replay tools are used to repeat real traffic scenarios. Traditionally, such a tool relies on software solutions that capture the whole traffic trace, and send the trace to the test network. Some special hardware devices should be provided. For instance, Qiao [15] realizes a traffic capture and replay system based on The Add-on Card, which interprets the detailed logic design of UDP pipeline on FPGA (Field-Programmable Gate Array). Although the Web traffic generator based on hardware implementation can produce high-volume test traffic, its poor scalability makes it difficult to integrate with the experimental network. GoReplay offers us the similar idea of reusing our existing traffic for testing, which makes it incredibly powerful. Nonetheless, these kinds of approaches can only reflect a period of time instead of a long and large time scale.  
2. Traffic Model [17]  
Some existing tools, such as WebStone [4], Web Polygraph [18], and httperf [19] are based on synthetic models of Web traffic. The network traffic model should be built by the study of the characteristics of a traffic generator. For example, [PERSON] [17] set each virtual user with a corresponding configure file, and these files determine the visiting paths, visiting moments and stay time of virtual users based on the Continuous Time Markov Chain. These models are developed analytically and then validated experimentally with measurement studies. Although it allows fine-grained control over behavioral aspects, some serious drawbacks are also inevitable. For instance, it can only reflect the macroscopic characteristics of the network traffic. In particular, such tools can successfully create realistic traffic mixes as a function of overall load. However, these tools typically cannot provide good performance.  
3. User Behavior [20; 21]  
The process of visiting the Web site can be roughly divided into the following three stages: First, a user starts a conversation by selecting a link what they are interested in; Second, after browsing, they often click another link, in which they are interested, in a relatively short time; Third, when he obtains the required information, he will stay for a long time with an inactive state. In particular, Figure 2 shows the important traffic parameters, including think-time, session-time, etc. In such a case, traffic characterization must be in line with the behavior of an individual user. In this paper, we first analyze web workload characteristics such as file sizes, mean think time, and the number of requests made to an individual file. We utilize the typical ON/OFF [20] internet accessing statistical model in which its key parameters are procured from real traffic analysis. Thus, it can generate realistic network traffic.