OPTIMIZATIONS FOR APPLICATION DEPENDENCY MAPPING

• US 20160359705A1
• Filed: 06/06/2016
• 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, Alizadeh Attar, Mohammadreza, Madani, Omid, Jeyakumar, Vimalkumar, Scheib, Ellen Christine, Yadav, Navindra
• Granted Patent: US 10,230,597 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

Application dependency mapping can be automated in a network. The network can capture traffic data for flows passing through the network using a sensor network that provides multiple perspectives for the traffic. The network can analyze the traffic data to identify endpoints of the network. The network can also identify particular network configurations from the traffic data, such as a load balancing schema or a subnetting schema. The network can partition the endpoints based on the network configuration(s) and perform similarity measurements of endpoints in each partition to determine clusters of each partition. The clusters can make up nodes of an application dependency map, and relationships between and among the clusters can make up edges of the application dependency map.

Claims

1. A method comprising:

capturing network traffic data for a plurality of flows in a network using a plurality of sensors that includes at least a first sensor on a first physical server of the network, a second sensor on a virtual partition of the network, and a third sensor on a networking device of the network;

determining a plurality of endpoints of the network using the network traffic data;

partitioning the plurality of endpoints into a plurality of partitions based on one or more network configurations;

determining a respective plurality of clusters for each of the plurality of partitions using the network traffic data; and

generating an application dependency map using the respective plurality of clusters.

2. The method of claim 1, wherein the one more network configurations include a load balancing schema, and the method further comprises:

identifying one or more endpoints forming a server farm corresponding to the load balancing schema,

wherein a first partition of the plurality of partitions includes the one or more endpoints.

3. The method of claim 2, further comprising:

analyzing the network traffic data to identify the load balancing schema.

4. The method of claim 3, wherein the network traffic data includes one or more packet attributes of first flows of a first endpoint of the first partition, host attributes of the first endpoint, virtualization attributes of the first endpoint, process attributes of processes initiating the first flows, or user attributes of process owners of the first flows.

5. The method of claim 3, further comprising:

determining a portion of the network traffic data corresponds to a server load balancing algorithm.

6. The method of claim 2, further comprising:

generating a first feature vector representing the first partition using at least one or more first features of a first endpoint of the first partition and one or more second features of a second endpoint of the first partition; and

determining a similarity between the one or more endpoints of the first partition and a third endpoint of the plurality of endpoints using at least the first feature vector and a second feature vector of the third endpoint.

7. The method of claim 1, wherein the one or more network configurations include a subnetting schema, and the method further comprises:

determining a respective subnet mask for each of the plurality of endpoints,

wherein each of the plurality of partitions corresponds to a respective unique subnet mask.

8. The method of claim 7, further comprising:

encoding data corresponding to the respective subnet mask of a first endpoint of the plurality of endpoints as a feature of the first endpoint.

9. The method of claim 1, further comprising:

displaying at least a portion of the application dependency map that corresponds to a first application of the network as a set of clusters corresponding to the first application;

receiving a request for a different view of the portion of the application dependency map that corresponds to the first application; and

displaying the portion as a respective set of endpoints of each cluster of the set of clusters corresponding to the first application.

10. The method of claim 1, further comprising:

displaying at least a portion of the application dependency map that corresponds to a first application of the network as a set of clusters corresponding to the first application;

receiving a selection of a first cluster of the set of clusters; and

displaying a set of endpoints of the first cluster.

11. A system comprising:

a processor; and

memory including instructions that, upon being executed by the processor, cause the system to;

capture network traffic data for a plurality of flows in a network using a plurality of sensors that includes at least a first sensor on a first physical server of the network, a second sensor on a virtual partition of the network, and a third sensor on a networking device of the network;

determine a plurality of endpoints of the network using the network traffic data;

partition the plurality of endpoints into a plurality of partitions based on one or more network configurations;

determine a respective plurality of clusters for each of the plurality of partitions using the network traffic data; and

generate an application dependency map using the respective plurality of clusters.

12. The system of claim 11, wherein the one more network configurations include a load balancing schema, and the instructions upon being executed further cause the system to:

identify one or more endpoints forming a server farm corresponding to the load balancing schema,

wherein a first partition of the plurality of partitions includes the one or more endpoints.

13. The system of claim 12, wherein the instructions upon being executed further cause the system to:

analyze the network traffic data to identify the load balancing schema.

14. The system of claim 13, wherein the network traffic data includes one or more packet attributes of first flows of a first endpoint of the first partition, host attributes of the first endpoint, virtualization attributes of the first endpoint, process attributes of processes initiating the first flows, or user attributes of process owners of the first flows.

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

determine a portion of the network traffic data corresponds to a server load balancing algorithm.

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

capture network traffic data for a plurality of flows in a network using a plurality of sensors that includes at least a first sensor on a first physical server of the network, a second sensor on a virtual partition of the network, and a third sensor on a networking device of the network;

determine a plurality of endpoints of the network using the network traffic data;

partition the plurality of endpoints into a plurality of partitions based on one or more network configurations;

determine a respective plurality of clusters for each of the plurality of partitions using the network traffic data; and

generate an application dependency map using the respective plurality of clusters.

17. The non-transitory computer-readable medium of claim 16, wherein the one or more network configurations include a subnetting schema, and the instructions upon being executed further cause the processor to:

determine a respective subnet mask for each of the plurality of endpoints,

wherein each of the plurality of partitions corresponds to a respective unique subnet mask.

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

encode data corresponding to the respective subnet mask of a first endpoint of the plurality of endpoints as a feature of the first endpoint.

19. The non-transitory computer-readable medium of claim 16, wherein the instructions further cause the processor to:

display at least a portion of the application dependency map that corresponds to a first application of the network as a set of clusters corresponding to the first application;

receive a request for a different view of the portion of the application dependency map that corresponds to the first application; and

display the portion as a respective set of endpoints of each cluster of the set of clusters corresponding to the first application.

20. The non-transitory computer-readable medium of claim 16, wherein the instructions further cause the processor to:

display at least a portion of the application dependency map that corresponds to a first application of the network as a set of clusters corresponding to the first application;

receive a selection of a first cluster of the set of clusters; and

display a set of endpoints of the first cluster.