Abstract: In accordance with one embodiment, a source leaf device receives a packet. The source leaf device identifies a flowlet associated with the packet and a destination leaf device to which the packet is to be transmitted. The source leaf device may determine whether the flowlet is a new flowlet. The source leaf device may select an uplink of the source leaf device via which to transmit the flowlet to the destination leaf device according to whether the flowlet is a new flowlet. The source leaf device may then transmit the packet to the destination leaf device via the uplink.

Abstract: Systems and methods are provided for automatically discovering applications/clusters in a network and mapping dependencies between the applications/clusters. A network monitoring system can capture network flow data using sensors executing on physical and/or virtual servers of the network and sensors executing on networking devices connected to the servers. The system can determine a graph including nodes, representing at least the servers, and edges, between pairs of the nodes of the graph indicating the network flow data includes one or more observed flows between pairs of the servers represented by the pairs of the nodes. The system can determine a dependency map, including representations of clusters of the servers and representations of dependencies between the clusters, based on the graph. The system can display a first representation of a first cluster of the dependency map and information indicating a confidence level of identifying the first cluster.

Abstract: In accordance with one embodiment, a source leaf device receives a packet. The source leaf device identifies a flowlet associated with the packet and a destination leaf device to which the packet is to be transmitted. The source leaf device may determine whether the flowlet is a new flowlet. The source leaf device may select an uplink of the source leaf device via which to transmit the flowlet to the destination leaf device according to whether the flowlet is a new flowlet. The source leaf device may then transmit the packet to the destination leaf device via the uplink.

Abstract: In accordance with one embodiment, a source leaf device receives a packet. The source leaf device identifies a flowlet associated with the packet and a destination leaf device to which the packet is to be transmitted. The source leaf device may determine whether the flowlet is a new flowlet. The source leaf device may select an uplink of the source leaf device via which to transmit the flowlet to the destination leaf device according to whether the flowlet is a new flowlet. The source leaf device may then transmit the packet to the destination leaf device via the uplink.

Abstract: Systems, methods, and computer-readable media are provided for determining a packet's round trip time (RTT) in a network. A system can receive information of a packet sent by a component of the network and further determine an expected acknowledgement (ACK) sequence number associated with the packet based upon received information of the packet. The system can receive information of a subsequent packet received by the component and determine an ACK sequence number and a receiving time of the subsequent packet. In response to determining that the ACK sequence number of the subsequent TCP packet matches the expected ACK sequence number, the system can determine a round trip time (RTT) of the packet based upon the received information of the packet and the received information of the subsequent packet.

Abstract: An approach for detecting anomalous flows in a network using header field entropy. This can be useful in detecting anomalous or malicious traffic that may attempt to “hide” or inject itself into legitimate flows. A malicious endpoint might attempt to send a control message in underutilized header fields or might try to inject illegitimate data into a legitimate flow. These illegitimate flows will likely demonstrate header field entropy that is higher than legitimate flows. Detecting anomalous flows using header field entropy can help detect malicious endpoints.

Abstract: This disclosure generally relates to a method and system for generating a communication graph of a network using an application dependency mapping (ADM) pipeline. In one aspect of the disclosure, the method comprises receiving network data (e.g., flow data and process information at each node) from a plurality of sensors associated with a plurality of nodes of the network, determining a plurality of vectors and an initial graph of the plurality of nodes based upon the network data, determining similarities between the plurality of vectors, clustering the plurality of vectors into a plurality of clustered vectors based upon the similarities between the plurality of vectors, and generating a communication graph of the network system based upon the plurality of clustered vectors.

Abstract: This disclosure generally relate to a method and system for mapping application dependency information. The present technology relates techniques that enable user-adjustable application dependency mapping of a network system. By collecting internal network data using various sensors in conjunction with external user inputs, the present technology can provide optimized application dependency mapping using user inputs.

Abstract: This disclosure generally relate to a method and system for mapping application dependency information. The present technology relates techniques that enable user-adjustable application dependency mapping of a network system. By collecting internal network data using various sensors in conjunction with external user inputs, the present technology can provide optimized application dependency mapping using user inputs.

Abstract: Systems, methods, and computer-readable media are provided for determining a packet's round trip time (RTT) in a network. A system can receive information of a packet sent by a component of the network and further determine an expected acknowledgement (ACK) sequence number associated with the packet based upon received information of the packet. The system can receive information of a subsequent packet received by the component and determine an ACK sequence number and a receiving time of the subsequent packet. In response to determining that the ACK sequence number of the subsequent TCP packet matches the expected ACK sequence number, the system can determine a round trip time (RTT) of the packet based upon the received information of the packet and the received information of the subsequent packet.

Abstract: Systems, methods, and computer-readable media are provided for determining a packet's round trip time (RTT) in a network. A system can receive information of a packet sent by a component of the network and further determine an expected acknowledgement (ACK) sequence number associated with the packet based upon received information of the packet. The system can receive information of a subsequent packet received by the component and determine an ACK sequence number and a receiving time of the subsequent packet. In response to determining that the ACK sequence number of the subsequent TCP packet matches the expected ACK sequence number, the system can determine a round trip time (RTT) of the packet based upon the received information of the packet and the received information of the subsequent packet.

Abstract: Systems and methods are provided for automatically discovering applications/clusters in a network and mapping dependencies between the applications/clusters. A network monitoring system can capture network flow data using sensors executing on physical and/or virtual servers of the network and sensors executing on networking devices connected to the servers. The system can determine a graph including nodes, representing at least the servers, and edges, between pairs of the nodes of the graph indicating the network flow data includes one or more observed flows between pairs of the servers represented by the pairs of the nodes. The system can determine a dependency map, including representations of clusters of the servers and representations of dependencies between the clusters, based on the graph. The system can display a first representation of a first cluster of the dependency map and information indicating a confidence level of identifying the first cluster.

Abstract: An example method includes a sensor detecting multiple packets of a flow during a specified total time period (e.g., a reporting time period). The total time period can be subdivided into multiple time periods. The sensor can analyze the detected packets to determine an amount of network utilization for each of the time periods. The sensor can then generate a flow summary based on the network utilization and the flow and send the flow summary to an analytics engine. Multiple other sensors can do similarly for their respective packets and flows. The analytics engine can receive the flow summaries from the various sensors and determine a correspondence between flow with high network utilization at a specific time period and a node or nodes. These nodes that experienced multiple flows with high network utilization for a certain period of time can be identified as experiencing a microburst.

Abstract: Systems and methods are provided for automatically discovering applications/clusters in a network and mapping dependencies between the applications/clusters. A network monitoring system can capture network flow data using sensors executing on physical and/or virtual servers of the network and sensors executing on networking devices connected to the servers. The system can determine a graph including nodes, representing at least the servers, and edges, between pairs of the nodes of the graph indicating the network flow data includes one or more observed flows between pairs of the servers represented by the pairs of the nodes. The system can determine a dependency map, including representations of clusters of the servers and representations of dependencies between the clusters, based on the graph. The system can display a first representation of a first cluster of the dependency map and information indicating a confidence level of identifying the first cluster.

Abstract: In accordance with one embodiment, a source leaf device receives a packet. The source leaf device identifies a flowlet associated with the packet and a destination leaf device to which the packet is to be transmitted. The source leaf device may determine whether the flowlet is a new flowlet. The source leaf device may select an uplink of the source leaf device via which to transmit the flowlet to the destination leaf device according to whether the flowlet is a new flowlet. The source leaf device may then transmit the packet to the destination leaf device via the uplink.

Abstract: Systems, methods, and computer-readable media are provided for determining whether a node in a network is a server or a client. In some examples, a system can collect, from one or more sensors that monitor at least part of data traffic being transmitted via a pair of nodes in a network, information of the data traffic. The system can analyze attributes of the data traffic such as timing, port magnitude, degree of communication, historical data, etc. Based on analysis results and a predetermined rule associated with the attributes, the system can determine which node of the pair of nodes is a client and which node is a server.

Abstract: An approach for detecting anomalous flows in a network using header field entropy. This can be useful in detecting anomalous or malicious traffic that may attempt to “hide” or inject itself into legitimate flows. A malicious endpoint might attempt to send a control message in underutilized header fields or might try to inject illegitimate data into a legitimate flow. These illegitimate flows will likely demonstrate header field entropy that is higher than legitimate flows. Detecting anomalous flows using header field entropy can help detect malicious endpoints.

Abstract: In accordance with one embodiment, a source leaf device receives a packet. The source leaf device identifies a flowlet associated with the packet and a destination leaf device to which the packet is to be transmitted. The source leaf device may determine whether the flowlet is a new flowlet. The source leaf device may select an uplink of the source leaf device via which to transmit the flowlet to the destination leaf device according to whether the flowlet is a new flowlet. The source leaf device may then transmit the packet to the destination leaf device via the uplink.

Abstract: Application dependency mapping (ADM) can be automated in a network. The network can determine whether certain nodes form a cluster of a tier of an application. The network can monitor network data and process data for traffic passing through the network using a sensor network that provides multiple perspectives for the traffic. The network can analyze the network data and process data to determine respective feature vectors for nodes. A feature vector may represent a combination of the features corresponding to the network data and the features corresponding to the process data of a node. The network can compare the similarity of the respective feature vectors and determine each node's cluster based on similarity measures between nodes.

Abstract: An approach for detecting anomalous flows in a network using header field entropy. This can be useful in detecting anomalous or malicious traffic that may attempt to “hide” or inject itself into legitimate flows. A malicious endpoint might attempt to send a control message in underutilized header fields or might try to inject illegitimate data into a legitimate flow. These illegitimate flows will likely demonstrate header field entropy that is higher than legitimate flows. Detecting anomalous flows using header field entropy can help detect malicious endpoints.