Abstract:
Disclosed herein is a multi-level analysis for determining a root cause of a network problem by performing a first level of the multi-level process that includes collecting data from one or more network components, generating a set of system metrics where each system metric of the set representing a portion of the data, ranking the set of system metrics based on a level of correlation of each system metric to the network problem to yield a ranked set of system metrics, and providing a visual representation of the first level of the multi-level process. A second level of the multi-level process includes receiving an input identifying one or more of the ranked set of system metrics to be excluded from analysis and performing a conditional analysis using only ones of the set of system metrics that are not identified for exclusion.
Abstract:
An example method includes detecting, using sensors, packets throughout a datacenter. The sensors can then send packet logs to various collectors which can then identify and summarize data flows in the datacenter. The collectors can then send flow logs to an analytics module which can identify the status of the datacenter and detect an attack.
Abstract:
An application and network analytics platform can capture comprehensive telemetry from servers and network devices operating within a network. The platform can discover flows running through the network, applications generating the flows, servers hosting the applications, computing resources provisioned and consumed by the applications, and network topology, among other insights. The platform can generate various models relating one set of application and network performance metrics to another. For example, the platform can model application latency as a function of computing resources provisioned to and/or actually used by the application, its host's total resources, and/or the distance of its host relative to other elements of the network. The platform can change the model by moving, removing, or adding elements to predict how the change affects application and network performance. In some situations, the platform can automatically act on predictions to improve application and network performance.
Abstract:
Disclosed herein is a multi-level analysis for determining a root cause of a network problem by performing a first level of the multi-level process that includes collecting data from one or more network components, generating a set of system metrics where each system metric of the set representing a portion of the data, ranking the set of system metrics based on a level of correlation of each system metric to the network problem to yield a ranked set of system metrics, and providing a visual representation of the first level of the multi-level process. A second level of the multi-level process includes receiving an input identifying one or more of the ranked set of system metrics to be excluded from analysis and performing a conditional analysis using only ones of the set of system metrics that are not identified for exclusion.
Abstract:
The disclosed technology relates to assisting with the migration of networked entities. A system may be configured to collect operations data for a service from at least one endpoint host in a network, calculate at least one metric for the service based on the operations data, retrieve a migration configuration and platform data for a target platform, generate a predicted cost for the migration configuration based on the migration configuration, the at least one metric, and the platform data, and provide the predicted cost for the migration configuration to a user.
Abstract:
Disclosed herein is a multi-level analysis for determining a root cause of a network problem by performing a first level of the multi-level process that includes collecting data from one or more network components, generating a set of system metrics where each system metric of the set representing a portion of the data, ranking the set of system metrics based on a level of correlation of each system metric to the network problem to yield a ranked set of system metrics, and providing a visual representation of the first level of the multi-level process. A second level of the multi-level process includes receiving an input identifying one or more of the ranked set of system metrics to be excluded from analysis and performing a conditional analysis using only ones of the set of system metrics that are not identified for exclusion.
Abstract:
A method provides for receiving network traffic from a host having a host IP address and operating in a data center, and analyzing a malware tracker for IP addresses of hosts having been infected by a malware to yield an analysis. When the analysis indicates that the host IP address has been used to communicate with an external host infected by the malware to yield an indication, the method includes assigning a reputation score, based on the indication, to the host. The method can further include applying a conditional policy associated with using the host based on the reputation score. The reputation score can include a reduced reputation score from a previous reputation score for the host.
Abstract:
An example method includes detecting, using sensors, packets throughout a datacenter. The sensors can then send packet logs to various collectors which can then identify and summarize data flows in the datacenter. The collectors can then send flow logs to an analytics module which can identify the status of the datacenter and detect an attack.
Abstract:
A network can achieve compliance by defining and enforcing a set of network policies to secure protected electronic information. The network can monitor network data, host/endpoint data, process data, and user data for traffic using a sensor network that provides multiple perspectives. The sensor network can include sensors for networking devices, physical servers, hypervisors or shared kernels, virtual partitions, and other network components. The network can analyze the network data, host/endpoint data, process data, and user data to determine policies for traffic. The network can determine expected network actions based on the policies, such as allowing traffic, denying traffic, configuring traffic for quality of service (QoS), or redirecting traffic along a specific route. The network can update policy data based on the expected network actions and actual network actions. The policy data can be utilized for compliance.
Abstract:
Methods, systems, and computer readable media are provided for determining, in a virtualized network system, a relationship of a sensor relative to other sensors. In a virtualized computing system in which a plurality of software sensors are deployed and in which there are one or more traffic flows, captured network data is received from the plurality of sensors, the captured network data from a given sensor of the plurality of sensors indicating one or more traffic flows detected by the given sensor. The received captured network data is analyzed to identify, for each respective sensor, a first group of sensors, a second group of sensors, and a third group of sensors, wherein all traffic flows observed by the first group of sensors are also observed by the second group of sensors, and all traffic flows observed by the second group of sensors are also observed by the third group of sensors. For each respective sensor, a location of each respective sensor relative to other sensors within the virtualized computing system is determined based upon whether the respective sensor belongs to the first group of sensors, the second group of sensors, or the third group of sensors.