SYSTEM AND METHOD FOR NETWORK INFORMATION MAPPING AND DISPLAYING

• US 20160359679A1
• Filed: 04/27/2016
• Published: 12/08/2016
• Est. Priority Date: 06/05/2015
• Status: Active Grant

• Current Assignee: Cisco Technology, Inc. (Cisco Systems, Inc.)
• Original Assignee: Cisco Technology, Inc. (Cisco Systems, Inc.)
• Inventors: Parandehgheibi, Ali, Madani, Omid, Jeyakumar, Vimalkumar, Scheib, Ellen Christine, Yadav, Navindra, Alizadeh Attar, Mohammadreza
• Granted Patent: US 10,516,585 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 16/122 using management policies backup systems G06F11/1446; file migration policies for HSM systems G06F16/185
• CPC Class Codes: G06F 16/137 Hash-based content-based indexing of textual data G06F16/31
• CPC Class Codes: G06F 16/162 Delete operations erasing in storage systems G06F3/0652
• CPC Class Codes: G06F 16/17 Details of further file system functions
• CPC Class Codes: G06F 16/173 Customisation support for file systems, e.g. localisation, multi-language support, personalisation
• CPC Class Codes: G06F 16/174 Redundancy elimination performed by the file system management of the data involved in backup or backup restore using de-duplication of the data G06F11/14
• CPC Class Codes: G06F 16/1744 using compression, e.g. sparse files
• CPC Class Codes: G06F 16/1748 De-duplication implemented within the file system, e.g. based on file segments de-duplication techniques in storage systems for the management of data blocks G06F3/0641
• CPC Class Codes: G06F 16/2322 using timestamps
• CPC Class Codes: G06F 16/235 Update request formulation
• CPC Class Codes: G06F 16/2365 Ensuring data consistency and integrity
• CPC Class Codes: G06F 16/24578 using ranking
• CPC Class Codes: G06F 16/248 Presentation of query results
• CPC Class Codes: G06F 16/285 Clustering or classification
• CPC Class Codes: G06F 16/288 Entity relationship models
• CPC Class Codes: G06F 16/29 Geographical information databases
• CPC Class Codes: G06F 16/9535 Search customisation based on user profiles and personalisation
• CPC Class Codes: G06F 2009/4557 Distribution of virtual machine instances; Migration and load balancing
• CPC Class Codes: G06F 2009/45587 Isolation or security of virtual machine instances
• CPC Class Codes: G06F 2009/45591 Monitoring or debugging support
• CPC Class Codes: G06F 2009/45595 : Network integration; Enabling network access in virtual machine instances
• CPC Class Codes: G06F 21/53 : by executing in a restricted environment, e.g. sandbox or secure virtual machine
• CPC Class Codes: G06F 21/552 : involving long-term monitoring or reporting
• CPC Class Codes: G06F 21/556 : involving covert channels, i.e. data leakage between processes inhibiting the analysis of circuitry or operation with measures against power attack G06F21/755
• CPC Class Codes: G06F 21/566 : Dynamic detection, i.e. detection performed at run-time, e.g. emulation, suspicious activities
• CPC Class Codes: G06F 2221/033 : Test or assess software
• CPC Class Codes: G06F 2221/2101 : Auditing as a secondary aspect
• CPC Class Codes: G06F 2221/2105 : Dual mode as a secondary aspect
• CPC Class Codes: G06F 2221/2111 : Location-sensitive, e.g. geographical location, GPS
• CPC Class Codes: G06F 2221/2115 : Third party
• CPC Class Codes: G06F 2221/2145 : Inheriting rights or properties, e.g., propagation of permissions or restrictions within a hierarchy
• CPC Class Codes: G06F 3/0482 : Interaction with lists of selectable items, e.g. menus
• CPC Class Codes: G06F 3/04842 : Selection of displayed objects or displayed text elements G06F3/0482 takes precedence
• CPC Class Codes: G06F 3/04847 : Interaction techniques to control parameter settings, e.g. interaction with sliders or dials
• CPC Class Codes: G06F 9/45558 : Hypervisor-specific management and integration aspects
• CPC Class Codes: G06N 20/00 : Machine learning
• CPC Class Codes: G06N 99/00 : Subject matter not provided for in other groups of this subclass
• CPC Class Codes: G06T 11/206 : Drawing of charts or graphs
• CPC Class Codes: H04J 3/0661 : using timestamps
• CPC Class Codes: H04J 3/14 : Monitoring arrangements for SDH/SONET rings H04J3/085
• CPC Class Codes: H04L 1/242 : by comparing a transmitted test signal with a locally generated replica
• CPC Class Codes: H04L 41/046 : comprising network management agents or mobile agents therefor
• CPC Class Codes: H04L 41/0668 : by dynamic selection of recovery network elements, e.g. replacement by the most appropriate element after failure
• CPC Class Codes: H04L 41/0803 : Configuration setting
• CPC Class Codes: H04L 41/0806 : for initial configuration or provisioning, e.g. plug-and-play
• CPC Class Codes: H04L 41/0816 : the condition being an adaptation, e.g. in response to network events
• CPC Class Codes: H04L 41/0893 : Assignment of logical groups to network elements
• CPC Class Codes: H04L 41/0894 : Policy-based network configuration management
• CPC Class Codes: H04L 41/12 : Discovery or management of network topologies
• CPC Class Codes: H04L 41/16 : using machine learning or artificial intelligence
• CPC Class Codes: H04L 41/22 : comprising specially adapted graphical user interfaces [GUI]
• CPC Class Codes: H04L 41/40 : using virtualisation of network functions or resources, e.g. SDN or NFV entities
• CPC Class Codes: H04L 43/02 : Capturing of monitoring data
• CPC Class Codes: H04L 43/026 : using flow identification
• CPC Class Codes: H04L 43/04 : Processing captured monitoring data, e.g. for logfile generation
• CPC Class Codes: H04L 43/045 : for graphical visualisation of monitoring data
• CPC Class Codes: H04L 43/062 : related to network traffic
• CPC Class Codes: H04L 43/08 : Monitoring or testing based on specific metrics, e.g. QoS, energy consumption or environmental parameters
• CPC Class Codes: H04L 43/0805 : by checking availability
• CPC Class Codes: H04L 43/0811 : by checking connectivity
• CPC Class Codes: H04L 43/0829 : Packet loss
• CPC Class Codes: H04L 43/0841 : Round trip packet loss
• CPC Class Codes: H04L 43/0858 : One way delays
• CPC Class Codes: H04L 43/0864 : Round trip delays
• CPC Class Codes: H04L 43/0876 : Network utilisation, e.g. volume of load or congestion level
• CPC Class Codes: H04L 43/0882 : Utilisation of link capacity
• CPC Class Codes: H04L 43/0888 : Throughput
• CPC Class Codes: H04L 43/10 : Active monitoring, e.g. heartbeat, ping or trace-route
• CPC Class Codes: H04L 43/106 : using time related information in packets, e.g. by adding timestamps
• CPC Class Codes: H04L 43/12 : Network monitoring probes
• CPC Class Codes: H04L 43/16 : Threshold monitoring
• CPC Class Codes: H04L 43/20 : the monitoring system or the monitored elements being virtualised, abstracted or software-defined entities, e.g. SDN or NFV
• CPC Class Codes: H04L 45/306 : Route determination based on the nature of the carried application
• CPC Class Codes: H04L 45/38 : Flow based routing
• CPC Class Codes: H04L 45/46 : Cluster building
• CPC Class Codes: H04L 45/507 : Label distribution
• CPC Class Codes: H04L 45/66 : Layer 2 routing, e.g. in Ethernet based MAN's
• CPC Class Codes: H04L 45/74 : Address processing for routing
• CPC Class Codes: H04L 47/11 : Identifying congestion
• CPC Class Codes: H04L 47/20 : Traffic policing
• CPC Class Codes: H04L 47/2441 : relying on flow classification, e.g. using integrated services [IntServ]
• CPC Class Codes: H04L 47/2483 : involving identification of individual flows
• CPC Class Codes: H04L 47/28 : in relation to timing considerations
• CPC Class Codes: H04L 47/31 : by tagging of packets, e.g. using discard eligibility [DE] bits
• CPC Class Codes: H04L 47/32 : by discarding or delaying data units, e.g. packets or frames
• CPC Class Codes: H04L 61/5007 : Internet protocol [IP] addresses
• CPC Class Codes: H04L 63/0227 : Filtering policies mail message filtering H04L51/212
• CPC Class Codes: H04L 63/0263 : Rule management
• CPC Class Codes: H04L 63/06 : for supporting key management in a packet data network cryptographic mechanisms or cryptographic arrangements for key management H04L9/08
• CPC Class Codes: H04L 63/0876 : based on the identity of the terminal or configuration, e.g. MAC address, hardware or software configuration or device fingerprint
• CPC Class Codes: H04L 63/1408 : by monitoring network traffic monitoring network traffic per se H04L43/00
• CPC Class Codes: H04L 63/1416 : Event detection, e.g. attack signature detection
• CPC Class Codes: H04L 63/1425 : Traffic logging, e.g. anomaly detection
• CPC Class Codes: H04L 63/1433 : Vulnerability analysis
• CPC Class Codes: H04L 63/1441 : Countermeasures against malicious traffic countermeasures against attacks on cryptographic mechanisms H04L9/002
• CPC Class Codes: H04L 63/145 : the attack involving the propagation of malware through the network, e.g. viruses, trojans or worms
• CPC Class Codes: H04L 63/1458 : Denial of Service
• CPC Class Codes: H04L 63/1466 : Active attacks involving interception, injection, modification, spoofing of data unit addresses, e.g. hijacking, packet injection or TCP sequence number attacks
• CPC Class Codes: H04L 63/16 : Implementing security features at a particular protocol layer
• CPC Class Codes: H04L 63/20 : for managing network security; network security policies in general filtering policies H04L63/0227
• CPC Class Codes: H04L 67/01 : Protocols
• CPC Class Codes: H04L 67/10 : in which an application is distributed across nodes in the network software deployment G06F8/60; multiprogramming arrangements G06F9/46
• CPC Class Codes: H04L 67/1001 : for accessing one among a plurality of replicated servers
• CPC Class Codes: H04L 67/12 : specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
• CPC Class Codes: H04L 67/51 : Discovery or management thereof, e.g. service location protocol [SLP] or web services
• CPC Class Codes: H04L 67/535 : Tracking the activity of the user network monitoring arrangements H04L43/00; recording of computer activity G06F11/34
• CPC Class Codes: H04L 67/75 : Indicating network or usage conditions on the user display
• CPC Class Codes: H04L 69/16 : Implementation or adaptation of Internet protocol [IP], of transmission control protocol [TCP] or of user datagram protocol [UDP]
• CPC Class Codes: H04L 69/22 : Parsing or analysis of headers
• CPC Class Codes: H04L 7/10 : Arrangements for initial synchronisation
• CPC Class Codes: H04L 9/0866 : involving user or device identifiers, e.g. serial number, physical or biometrical information, DNA, hand-signature or measurable physical characteristics
• CPC Class Codes: H04L 9/3239 : involving non-keyed hash functions, e.g. modification detection codes [MDCs], MD5, SHA or RIPEMD
• CPC Class Codes: H04L 9/3242 : involving keyed hash functions, e.g. message authentication codes [MACs], CBC-MAC or HMAC
• CPC Class Codes: H04W 72/54 : based on quality criteria
• CPC Class Codes: H04W 84/18 : Self-organising networks, e.g. ad-hoc networks or sensor networks

Abstract

This disclosure generally relate to a method and system for mapping network information. The present technology relates techniques that enable full-scale, dynamic network mapping of a network system. By collecting network and computing data using built-in sensors, the present technology can provide network information for system monitoring and maintenance. According to some embodiments, the present technology enables generating and displaying of network connections and data processing statistics related to numerous nodes in a network. The present technology provides useful insights and actionable knowledge for network monitoring, security, and maintenance, via intelligently summarizing and effectively displaying the complex network communications and processes of a network.

Claims

1. A method comprising:

receiving aggregate network flow data using a plurality of sensors associated with a network;

determining node attributes associated with a plurality of nodes of the network, the node attributes representing at least one portion of distinctive process data or distinctive network data;

determining a priority order of the node attributes based at least on one of the distinctive process data and the distinctive network data; and

displaying, on a user interface, the node attributes in the priority order.

2. The method of claim 1, wherein the plurality of sensors include at least a first sensor of a physical switch of the network, a second sensor of a hypervisor associated with the physical switch, a third sensor of a virtual machine associated with the hypervisor.

3. The method of claim 1, further comprising:

determining a respective similarity score for each of the plurality of nodes based on the node attributes, the respective similarity score indicating a similarity level among the plurality of nodes;

determining one or more nodes each having the respective similarity score higher than a threshold

displaying the one or more nodes and the respective similarity score on the user interface.

4. The method of claim 3, further comprising:

ranking the one or more nodes based at least in part on the respective similarity score in a descending order; and

displaying the one or more nodes in the descending order on the user interface.

5. The method of claim 3, further comprising:

generating one or more node clusters based at least in part on the similarity level of the respective similarity score.

6. The method of claim 5, further comprising:

determining one or more cluster attributes of the one or more node clusters, the cluster attributes representing at least one portion of cluster distinctive process data or cluster distinctive network data;

determining a priority order of the cluster attributes based at least on one of the cluster distinctive process data and the cluster distinctive network data; and

displaying, on the user interface, the one or more cluster attributes in the priority order.

7. The method of claim 1, further comprising:

determining, based at least in part on the aggregate network flow data, a first dependency map executing in the network, the first dependency map indicating a pattern of network traffic.

8. The method of claim 7, further comprising:

receiving one or more adjustments to the aggregate network flow data;

determining updated node attributes associated with each of the plurality of nodes;

generating an updated priority order of node attributes based at least on updated distinctive process data and updated distinctive network data; and

displaying, on the user interface, the updated node attributes in the updated priority order.

9. The method of claim 8, further comprising:

determining, based at least in part on adjusted aggregate network flow data, a second dependency map executing in the network; and

comparing the second dependency map to the first dependency map to generate a summary of the one or more adjustments including changing the number of nodes in a node cluster.

10. A system comprising:

one or more processors; and

memory including instructions that, upon being executed by the one or more processors, cause the system to;

receive network flow data using a plurality of sensors associated with a network;

determine node attributes associated with a plurality of nodes, the node attributes representing at least one portion of distinctive process data or distinctive network data;

determine a respective similarity score for each of the plurality of nodes based on the node attributes, the respective similarity score indicating a similarity level among the plurality of nodes; and

display the respective similarity score on the user interface.

11. The system of claim 10, wherein the instructions upon being executed further cause the system to:

generate one or more node clusters based at least in part on the similarity level of the respective similarity score.

12. The system of claim 10, wherein the plurality of sensors are configured to preprocess the network flow data before sending.

13. The system of claim 10, wherein the instructions upon being executed further cause the system to:

determine, based at least in part on the network flow data, an application dependency map executing in the network.

14. The system of claim 10, wherein the instructions upon being executed further cause the system to:

receive one or more adjustments to the network flow data;

determine updated node attributes associated with each of the plurality of nodes;

regenerate an updated priority order of node attributes based at least on updated distinctive process data and updated distinctive network data; and

display, on the user interface, the updated node attributes in the updated priority order.

15. A non-transitory computer-readable storage medium having stored therein instructions that, upon being executed by a processor, cause the processor to:

receive aggregate network flow data using a plurality of sensors associated with a network;

determine node attributes associated with each of the plurality of nodes, the node attributes representing at least one portion of distinctive process data or distinctive network data;

receive one or more adjustments to the aggregate network flow data;

determine updated node attributes associated with each of the plurality of nodes;

generate an updated priority order of node attributes based at least on updated distinctive process data and updated distinctive network data; and

display, on a user interface, the updated node attributes in the updated priority order.

16. The non-transitory computer-readable storage medium of claim 15, wherein the instructions upon being executed further cause the processor to:

determine, based at least in part on the aggregate network flow data, a first dependency map executing in the network, the first dependency map indicating a pattern of network traffic;

determine, based at least in part on the adjusted aggregate network flow data, a second dependency map executing in the network; and

compare the second dependency map to the first dependency map to generate a summary of the one or more adjustments.

17. The non-transitory computer-readable storage medium of claim 15, wherein the instructions upon being executed further cause the processor to:

determine a respective similarity score for each of the plurality of nodes based on the node attributes, the respective similarity score indicating a similarity level among the plurality of nodes; and

display the respective similarity score on the user interface.

18. The non-transitory computer-readable storage medium of claim 17, wherein the instructions upon being executed further cause the processor to:

generate one or more node clusters based at least in part on the similarity level of the respective similarity score.

19. The non-transitory computer-readable storage medium of claim 15, wherein the plurality of sensors include at least a first sensor of a physical switch of the network, a second sensor of a hypervisor associated with the physical switch, a third sensor of a virtual machine associated with the hypervisor.

20. The non-transitory computer-readable storage medium of claim 15, wherein the instructions upon being executed further cause the processor to:

determine, based at least in part on the aggregate network flow data, an application dependency map executing in the network.