学位论文详细信息
Software defined transport
Data Center;Flow Scheduling;Transport Rate Control;Deadline;Network Utilization;Software Defined Networking
Hong, Chi-Yao
关键词: Data Center;    Flow Scheduling;    Transport Rate Control;    Deadline;    Network Utilization;    Software Defined Networking;   
Others  :  https://www.ideals.illinois.edu/bitstream/handle/2142/72760/Chi-Yao_Hong.pdf?sequence=1&isAllowed=y
美国|英语
来源: The Illinois Digital Environment for Access to Learning and Scholarship
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【 摘 要 】

We advocate a software defined transport (SDT) architecture in which a transport controller schedules and dynamically re-schedules the flow sending rates based on current network conditions and the network operator's goals.This dissertation shows that this architecture provides both high flexibility, by allowing the operator to implement newtransport policies as needed, and fine-grained flow control, by optimizing network resource allocation at flow-level in real time.We begin with proposing a fine-grained flow scheduling protocol to complete flows quickly and meet flow deadlines.Through extensive packet-level and flow-level simulation, we demonstrate that fine-grained flow control can significantly reduce mean flow completion times by 30% or more compared with TCP, RCP, and D3.We next design a software-driven controller which centrally allocates network resource such as bandwidth and routing paths for flexibility.In particular, we develop a prototype of our design for inter-datacenter wide area networks to achieve nearly optimal network utilization and service-level fairness.After that, we address network update problem to ensure bandwidth requirements during network updates subject to network capacity and switch memory constraints.Finally, we design and implement a fast, fine-grained flow-rate controller for data center networks.We show this design provides high scalability, by rate-controlling 95% of bytes of a cluster with several thousand servers within hundreds of milliseconds with a multi-threaded resource allocation algorithm, and application-level improvement, by reducing average shuffling times of MapReduce workload by 12-20%.

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