Sierpinski triangle based data-center network architecture in cloud computing / Qi Han - Qi, Han (2016)
This thesis reports on the research to develop of a data center network (DCN) architecture to solve the problem of network performance in cloud-oriented data centers. Computational clouds are increasingly becoming popular for the provisioning of computing resources and service on demand basis. A DCN is an important component of data centers that consists of a large number of hosted servers and switches connected with high speed communication links. As a backbone in data centers, a DCN enables the deployment of resources centralization and on-demand access of the information and services of data centers to users. In recent years, the scale of the DCN has constantly increased with the widespread use of cloud-oriented services and applications configured over virtual machines (VMs), and the unprecedented amount of data delivery in/between data centers, whereas the traditional DCN tree-based architecture lacks aggregate bandwidth, scalability and cost effectiveness for coping with the increasing demands of tenants in accessing the services of cloud-oriented data centers. To solve this problem, the method developed in this research is used to mitigate the aggregation throughput and improve the network performance of DCN by using a novel DCN architecture. The proposed method, called Sierpinski Triangle Based (STB) DCN architecture, is developed on the basis of the well-know Sierpinski triangle fractal to mitigate throughput bottleneck in aggregate layers as accumulated in tree-based structure. STB is a fault-tolerant architecture which provides at least two parallel paths for each pair of servers. It also supports various bandwidth-intensive applications by providing high network throughput for allto- all traffic. STB architecture was implemented in a real cloud data center environment and evaluated in Network Simulator 2 (NS2) simulation. The performance of STB architecture is validated by comparing the results with the traditional tree-based, and DCell DCN architectures. Theoretical analysis and implementation experiences verify that the iii proportion of server to entire nodes in STB is same with DCell but higher than that of tree-based architecture. The average shortest path length is restricted between 5.0 and 6.7, when node failure proportion remains between 0.02 and 0.2, shorter than DCell in a 4-level architecture. The results of the experiment also show that the STB architecture has higher throughout than both traditional tree-based and DCell architectures from the scale of 12 to 363 servers with/without link failure happens. From the results of both simulation and experiment in actual devices, we speculate that STB still can achieve better network performance in throughput, server utilization, average shortest path length than DCell and tree-based architectures in real large-scale cloud-oriented DCN.