Characterizing network traffic in a cluster-based, multi-tier data center

With the increasing use of various Web-based services, design of high performance, scalable and dependable data centers has become a critical issue. Recent studies show that a clustered, multi-tier architecture is a cost-effective approach to design such servers. Since these servers are highly distributed and complex, understanding the workloads driving them is crucial for the success of the ongoing research to improve them. In view of this, there has been a significant amount of work to characterize the workloads of Web-based services. However, all of the previous studies focus on a high level view of these servers, and analyze request-based or session-based characteristics of the workloads. In this paper, we focus on the characteristics of the network behavior within a clustered, multi-tiered data center. Using a real implementation of a clustered three-tier data center, we analyze the arrival rate and inter-arrival time distribution of the requests to individual server nodes, the network traffic between tiers, and the average size of messages exchanged between tiers. The main results of this study are; (1) in most cases, the request inter-arrival rates follow log-normal distribution, and self-similarity exists when the data center is heavily loaded, (2) message sizes can be modeled by the log-normal distribution, and (3) service times fit reasonably well with the Pareto distribution and show heavy tailed behavior at heavy loads.