System for monitoring and managing datacenters
US-2016359872-A1 · Dec 8, 2016 · US
US10404564B2 · US · B2
| Field | Value |
|---|---|
| Publication number | US-10404564-B2 |
| Application number | US-201715410595-A |
| Country | US |
| Kind code | B2 |
| Filing date | Jan 19, 2017 |
| Priority date | Jan 19, 2017 |
| Publication date | Sep 3, 2019 |
| Grant date | Sep 3, 2019 |
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Disclosed is a method for continuous in-line monitoring of data-centric traffic to guarantee application performance. The method includes, in each switch of a plurality of switches in a network fabric, grouping all packets entering each respective switch of the plurality of switches based on either 5-tuple applications or EPG based applications, collecting performance statistics at every hop in the network fabric across all flows in-line in a flow table maintained in each respective switch and periodically exporting the performance statistics to analysis module.
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What is claimed is: 1. A method comprising: grouping all packets entering each switch of a plurality of switches in a network fabric, the grouping based on end point group (EPG) based applications; collecting performance statistics at every hop in the network fabric across all flows in-line in a flow table maintained at each switch; and periodically exporting the performance statistics to an analysis module. 2. The method of claim 1 , wherein the performance statistics include one or more of latency, jitter, or microburst statistics. 3. The method of claim 1 , further comprising: computing a maximum latency and an average latency experienced by the packets at every hop. 4. The method of claim 1 , further comprising: isolating one of the plurality of switches based on the performance statistics, wherein, the performance statistics identify a spike experienced by an individual frame, and the spike identify a problem associated with the one of the plurality of switches. 5. The method of claim 1 , wherein the EPG based applications comprise one or more of Src EPG, Dst EPG, or Protocol. 6. The method of claim 1 , further comprising: sending the packets entering each switch to a local central processing unit at each switch, and analyzing each of the flows via a light weight flow analyzer module at each switch. 7. The method of claim 6 , further comprising: storing, via the light weight flow analyzer module at each switch, traffic information in a time series database. 8. A system comprising: a processor; and a computer-readable storage device storing instructions which, when executed by the processor, cause the processor to perform operations comprising: grouping all packets entering each switch of a plurality of switches in a network fabric, the grouping based on end point group (EPG) based applications; collecting performance statistics at every hop in the network fabric across all flows in-line in a flow table maintained at each switch; and periodically exporting the performance statistics to an analysis module. 9. The system of claim 8 , wherein the performance statistics comprise one or more of latency, jitter, or microburst statistics. 10. The system of claim 8 , wherein the instructions, when executed by the processor, cause the processor to perform further operations comprising: computing a maximum latency and an average latency experienced by the packets at every hop. 11. The system of claim 8 , wherein the instructions, when executed by the processor, cause the processor to perform further operations comprising: isolating one of the plurality of switches based on the performance statistics, wherein, the performance statistics identify a spike experienced by an individual frame, and the spike identify a problem associated with the one of the plurality of switches. 12. The system of claim 8 , wherein the EPG based applications comprise one or more of Src EPG, Dst EPG, or Protocol. 13. The system of claim 8 , further comprising: a local central processing unit at each switch, the local central processing unit configured to receive the packets entering a respective one of the plurality of switches; and a light weight flow analyzer module at each switch, the light weight flow analyzer module configured to analyze a respective one of the flows. 14. The system of claim 13 , wherein the light weight flow analyzer module is configured to store traffic information in a time series database. 15. A non-transitory computer-readable storage device storing instructions which, when executed by a processor, cause the processor to perform operations comprising: grouping all packets entering each switch of a plurality of switches in a network fabric, the grouping based on end point group (EPG) based applications; collecting performance statistics at every hop in the network fabric across all flows in-line in a flow table maintained at each switch; and periodically exporting the performance statistics to an analysis module. 16. The non-transitory computer-readable storage device of claim 15 , wherein the performance statistics comprise one or more of latency, jitter, or microburst statistics. 17. The non-transitory computer-readable storage device of claim 15 , wherein the instructions, when executed by the processor, cause the processor to perform further operations comprising: computing a maximum latency and an average latency experienced by the packets at every hop. 18. The non-transitory computer-readable storage device of claim 15 , wherein the instructions, when executed by the processor, cause the processor to perform further operations comprising: isolating one of the plurality of switches based on the performance statistics, wherein, the performance statistics identify a spike experienced by an individual frame, and the spike identify a problem associated with the one of the plurality of switches. 19. The method of claim 1 , wherein the performance statistics include latency, jitter, and microburst statistics. 20. The system of claim 8 , wherein the performance statistics include latency, jitter, and microburst statistics.
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