An analysis device obtains connection states of a testing terminal pair that respectively correspond to a plurality of unit moments in a first historical time segment. The testing terminal pair includes a first terminal and a second terminal, the first historical time segment is a time segment before a current time, the first historical time segment includes M consecutive unit moments, and M is a natural number greater than or equal to 2. The analysis device determines, based on the connection states of the testing terminal pair that respectively correspond to the plurality of unit moments in the first historical time segment, a connection state that is of the testing terminal pair and that corresponds to at least one unit moment in a future time segment.

The present disclosure relates to systems, methods, and computer readable media for diagnosing and mitigating memory impact events, such as memory leaks, high memory usage, or other memory issues causing a host node from performing as expected on a cloud computing system. The systems described herein involve receiving locally generated memory usage data from a plurality of host nodes. The systems described herein may aggregate the memory usage data and determine a memory impact diagnosis based on a subset of the aggregated memory usage data. The systems described herein may further apply a mitigation model for mitigating the memory impact event. The systems described herein provide an end-to-end solution for diagnosing and mitigating a variety of memory issues using a dynamic and scalable system that reduces a negative impact of memory leaks and other memory issues on a cloud computing system.

A system to collect and store in a special data structure arranged for rapid searching massive amounts of data. Performance metric data is one example. The performance metric data is recorded in time-series measurements, converted into unicode, and arranged into a special data structure having one directory for every day which stores all the metric data collected that day. The data structure at the server where analysis is done has a subdirectory for every resource type. Each subdirectory contains text files of performance metric data values measured for attributes in a group of attributes to which said text file is dedicated. Each attribute has its own section and the performance metric data values are recorded in time series as unicode hex numbers as a comma delimited list. Analysis of the performance metric data is done using regular expressions.

A system to collect and store in a special data structure arranged for rapid searching massive amounts of data. Performance metric data is one example. The performance metric data is recorded in time-series measurements, converted into unicode, and arranged into a special data structure having one directory for every day which stores all the metric data collected that day. The data structure at the server where analysis is done has a subdirectory for every resource type. Each subdirectory contains text files of performance metric data values measured for attributes in a group of attributes to which said text file is dedicated. Each attribute has its own section and the performance metric data values are recorded in time series as unicode hex numbers as a comma delimited list. Analysis of the performance metric data is done using regular expressions.

A registration device (10) includes an extracting unit (131) that extracts inner header information and outer header information of an encapsulated packet, a filter unit (132) that calculates a hash value of the inner header information and the outer header information as an address of a hash table in which arrival information indicating whether a first packet of a series of flow has arrived is registered for each address and causes, based on the hash table, inner header information and outer header information of the first packet of the series of flow to pass, and a registering unit (133) that registers the inner header information and the outer header information of the first packet, which the filter unit (132) has caused to pass, in a database in association with each other.

A registration device (10) includes an extracting unit (131) that extracts inner header information and outer header information of an encapsulated packet, a filter unit (132) that calculates a hash value of the inner header information and the outer header information as an address of a hash table in which arrival information indicating whether a first packet of a series of flow has arrived is registered for each address and causes, based on the hash table, inner header information and outer header information of the first packet of the series of flow to pass, and a registering unit (133) that registers the inner header information and the outer header information of the first packet, which the filter unit (132) has caused to pass, in a database in association with each other.

A system and method for automated caching and tabling for finding and swapping media content is disclosed. The system and method include at least: (a) detecting, by one or more computing devices, one or more media packets transmitted over a network, wherein the one or more media packets are associated with the media content; (b) generating, by the one or more computing devices, a profile for the media content based on characteristics of the one or more media packets; (c) generating, by the one or more computing devices, a hash value based on the profile; (d) transmitting for storage in a database, by the one or more computing devices, the hash value, the profile, and the one or more media packets; (e) detecting, by the one or more computing devices, one or more subsequent media packets sent over the network and addressed to a destination to determine that the one or more subsequent media packets are associated with the media content by comparing the one or more subsequent media packets to the profile via the hash value; and (f) based on the detecting in (e) the system and method can further include transmitting, by the one or more computing devices, the one or more subsequent media packets or the one or more media packets to the destination based on a predetermined criteria.

Described herein is a system and method for testing a computing device, such as a server, to minimize network impact. A computing device that is new or needs to be evaluated, such as an edge server, in a content delivery network may be determined and a sibling edge server which shares a common characteristic with the edge server may be selected. Requests received on the sibling edge server may be collected and filtered to determine a subset of the requests. The subset of the requests are transmitted to the edge server for processing and evaluation.

Examples relate to detection of anomalies in a network. Some examples determine a dictionary including a set of keys for a set of packet length values for a selected sequence of packets associated with a traffic flow over a network, each key represents a combination of two or more successive packet length values from the set of packet length values. An aggregated set of statistical features is determined based in part on the set of statistical features using a machine learning algorithm. Upon determining another set of packet length values for another selected sequence of packets, another set of statistical features for the other set of packet length values is determined. The other set of statistical features is compared with the aggregated set of statistical features. Based on the comparison, an indication that an anomaly has occurred in the traffic flow is transmitted to an administrator.

Examples relate to detection of anomalies in a network. Some examples determine a dictionary including a set of keys for a set of packet length values for a selected sequence of packets associated with a traffic flow over a network, each key represents a combination of two or more successive packet length values from the set of packet length values. An aggregated set of statistical features is determined based in part on the set of statistical features using a machine learning algorithm. Upon determining another set of packet length values for another selected sequence of packets, another set of statistical features for the other set of packet length values is determined. The other set of statistical features is compared with the aggregated set of statistical features. Based on the comparison, an indication that an anomaly has occurred in the traffic flow is transmitted to an administrator.