Google Inc.

Luminate Wireless, Inc.

Kevin Yu

Yuan Yuan

Michial Allen Gunter

Xinyi Zhang

David James Carmichael

Carroll Philip Gossett

Emmanouil Koukoumidis

C Philip Gossett

Xiaofang Jiang

Wenbo Zhu

Yu Xie

Julien Laganier

Caroll Philip Gossett

Matthew Lawlor

Xiaofan Jiang

Described herein are systems, mediums, and methods for simultaneously determining multiple types of changes in time series data. A plurality of potential change points may be selected in a signal representing the time series data. For each potential change point the signal may be a split into two segments including data points before and data points after the potential change point, respectively. Estimate of cumulative distribution function for each segment may be determined. A normalized difference of the respective estimates of cumulative distribution functions may be determined. The normalized difference may be compared to a threshold value to determine whether the potential change point qualifies as valid change point. The techniques discussed herein may determine at least two valid change points representing at least two different types of changes in the signal.

Non-parametric change point detection

Systems and methods are described herein for detecting an anomaly in a discrete signal, where samples in the signal correspond to amounts of data flow in a network within a time interval. The discrete signal is received, and a sequence of likelihoods corresponding to the sample values in the signal is generated. The likelihoods are based at least in part on a historical probability distribution of previously received sample values, and a likelihood is a probability of occurrence of a corresponding sample value in the signal. Likelihood change points are identified in the likelihood sequence, and the discrete signal is segmented into a plurality of segments at samples corresponding to the identified change points. A segment is identified as an anomaly based on a comparison between a statistic of the segment and a statistic of the historical probability distribution.

Statistics-based anomaly detection

Described herein are systems, mediums, and methods for detecting blockage of network traffic at a network server. A signal representative of time series data associated with network traffic is received at a processor for analysis. A data segmentation algorithm and an anomaly detection algorithm are applied in series to the received data. The segmentation algorithm detects regime shifts in the data. Data between regime shifts is considered a segment of data. The anomaly detection algorithm analyzes each segment individually to determine whether anomalies exist in the segment. If a cyclic pattern is found in the segment, the cyclic pattern is extracted from the segment leaving a residual data for analysis. A probability distribution of the residual data is determined for analysis. When an anomaly is detected in the segment o based on the probability distribution of the residual data, it is determined that blockage has occurred on the network traffic.

Anomaly detection in time series data

Methods and apparatus for supporting a data center relocation with regard to an access point are described. A primary gateway connection is established between the access point and a primary gateway in a first data center, and a secondary gateway connection is established between the access point and a secondary gateway in a second data center. A path switch request is sent via the secondary gateway connection. A DNS server sends an IP address corresponding to the second data center. The data center for the access point is switched from the first data center to the second data center. In some embodiments, the transition from the first data center to the second data center is in response to a detected communications failure. In other embodiments, the transition from the first data center to the second data center is due to scheduled maintenance.

Data center relocation methods and apparatus

Systems and methods are disclosed herein for detecting an anomaly in a discrete signal, where a long-term trend of the discrete signal is identified. Samples in the signal correspond to a number of data packets arriving at a location in a network within a time interval. The long-term trend is subtracted from the discrete signal to obtain a detrended signal. A cyclic pattern is identified in the detrended signal and is subtracted from the detrended signal to obtain a residual signal. Anomaly detection is performed on the residual signal.

Anomaly detection in a signal

A method may include identifying a repeating frame structure for communication between a MIMO base station and one or more wireless devices, the frame structure including a plurality of slots, with each slot including an uplink portion and a downlink portion, and each uplink portion and downlink portion comprising a plurality of sub-slots, receiving, at the wireless base station, an identification of a first wireless device of the one or more wireless devices, assigning to the first wireless device, a sub-slot in an uplink portion of a static slot in the frame structure, communicating, to the first wireless device, information from which the first wireless device can identify the sub-slot, and communicating with the one or more wireless devices by associating data in the first sub-slot with the first wireless device.

Dynamic TDMA system for TV white space MIMO wireless

In general, the subject matter described in this specification can be embodied in methods, systems, and program products for adaptive data unit transmission. A sliding window is filled with data units and designates a sliding window start position and a sliding window end position. A value for each of the data units in the sliding window is stored, the value representing a maximum number of times that each data unit is to be transmitted. The stored value is different among at least two of the data units. Data units are selected from the sliding window to be assembled into a packet. An assembled packet is transmitted to a receiving computerized device. A determination that the data unit positioned at the sliding window start position has been transmitted a maximum number of time is performed, and in response a different data unit is positioned at the sliding window start position.

Adaptive data unit transmission and acknowledgment

Systems and methods for reducing the cost but increasing the performance caused in streaming data between a client and a server are described herein. Embodiments of the present invention relate to reducing the cost but increasing the performance by the traditional data streaming where two TCP connections, one for each direction of traffic is used. Further embodiments allow a server in communication with one or more clients using a RPC based communication protocol to bi-directional stream not on two TCP connections but a single TCP connection. In one embodiment, this is achieved by transmitting from the client to the server an initial handshake. If the server receives the handshake, then the client streams a request stream to the server and the server streams a response stream to the client simultaneously over the same single TCP connection establishing full-duplex streaming.

Full-duplex bi-directional communication over a remote procedure call based communications protocol, and applications thereof

Systems and methods are provided for estimating missing samples in a signal. A plurality of samples in the signal is received, and a respective sample corresponds to a respective sample location in a plurality of sample locations. A subset of sample locations representing missing samples in the signal is identified, and a first and a second threshold are determined. Each threshold is an integer number of samples, and the second threshold is greater than the first threshold. A first set of consecutive sample locations from the identified subset of sample locations is formed, and the missing samples in the first set of consecutive sample locations are replaced based on a comparison between a number of locations in the first set of consecutive locations, the first threshold, and the second thresholds.

Data replacement policy

Systems and methods are disclosed herein for identifying an anomaly in a signal, where samples in the signal correspond to an amount of data flow in a network within a time interval, and an anomaly corresponds to at least one sample in the discrete signal having a likelihood value below a likelihood threshold. A historical probability distribution of the discrete signal is generated based on previously received samples. For each sample in a plurality of samples in the discrete signal, a likelihood is computed based at least in part on the historical probability distribution. A likelihood threshold is selected, and a set of consecutive samples is identified as an anomaly when each sample in the set has a computed likelihood below the likelihood threshold.

Amplitude-based anomaly detection

A method of coordinating communications between a wireless MIMO base station and a plurality of wireless communication devices is disclosed. The method includes generating a series of index numbers for controlling sub-slot assignments for the plurality of wireless communication devices, providing data representing the series of numbers to the MIMO base station and each of the plurality of wireless communication devices, and controlling simultaneous communication between the plurality of wireless communication devices and the MIMO base station by reassigning access by the plurality of wireless communication devices to a plurality of sub-slots in a communication structure of unit size.

Random shuffling mechanism for MIMO wireless system

A computer-implemented method may include receiving, at the base station, an identification and request for transmission access from a wireless computing device, determining a level of efficiency for data communications between the base station and the wireless computing device, selecting a descriptor for the level of efficiency, from among a plurality of predetermined descriptors that identify discrete efficiency classifications across a range of efficiencies, determining a share of bandwidth for communication between the base station and the wireless computing device based on the selected descriptor, and communicating electronically with the wireless computing device using the selected share of bandwidth. A higher share of bandwidth may be selected for a wireless computing device having a higher determined level of efficiency.

Growing community of inventors

Palo Alto, CA, United States of America

Kevin Yu