Name
Abstract | ||
|---|---|---|
Today's cloud network platforms allow tenants to construct sophisticated virtual network topologies among their VMs on a shared physical network infrastructure. However, these platforms provide little support for tenants to diagnose problems in their virtual networks. Network virtualization hides the underlying infrastructure from tenants as well as prevents deploying existing network diagnosis tools. This paper makes a case for providing virtual network diagnosis as a service in the cloud. We identify a set of technical challenges in providing such a service and propose a Virtual Network Diagnosis (VND) framework. VND exposes abstract configuration and query interfaces for cloud tenants to troubleshoot their virtual networks. It controls software switches to collect flow traces, distributes traces storage, and executes distributed queries for different tenants for network diagnosis. It reduces the data collection and processing overhead by performing local flow capture and on-demand query execution. Our experiments validate VND's functionality and shows its feasibility in terms of quick service response and acceptable overhead; our simulation proves the VND architecture scales to the size of a real data center network. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1145/2523616.2523621 | SoCC |
Keywords | Field | DocType |
sophisticated virtual network topology,network virtualization hide,cloud network platform,physical network infrastructure,existing network diagnosis tool,real data center network,vnd architecture scale,network diagnosis,virtual network,virtual network diagnosis,latency,virtualization,cloud computing | Troubleshooting,Virtual network,Virtualization,Computer science,Computer network,Network topology,Real-time computing,Software,Data center,Network virtualization,Cloud computing,Distributed computing | Conference |
Citations | PageRank | References |
15 | 1.06 | 16 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wenfei Wu | 1 | 399 | 28.74 |
| Guohui Wang | 2 | 1088 | 60.78 |
| Aditya Akella | 3 | 4138 | 268.44 |
| Anees Shaikh | 4 | 1510 | 113.98 |