Journal of Systems and Software
Towards characterizing cloud backend workloads: insights from Google compute clusters
ACM SIGMETRICS Performance Evaluation Review
The impact of virtualization on network performance of amazon EC2 data center
INFOCOM'10 Proceedings of the 29th conference on Information communications
Characterising a grid site's traffic
Proceedings of the 19th ACM International Symposium on High Performance Distributed Computing
Modeling and synthesizing task placement constraints in Google compute clusters
Proceedings of the 2nd ACM Symposium on Cloud Computing
Utilizing green energy prediction to schedule mixed batch and service jobs in data centers
HotPower '11 Proceedings of the 4th Workshop on Power-Aware Computing and Systems
Utilizing green energy prediction to schedule mixed batch and service jobs in data centers
ACM SIGOPS Operating Systems Review
DATE '12 Proceedings of the Conference on Design, Automation and Test in Europe
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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.