Abstract: An online system receives time series data and analyzes the data for identifying trend changes or level shifts in the time series. The time series data may describe resource utilization of systems, for example, bandwidth of computer networks. The online system uses prediction models, for example, machine learning based prediction models using regression to predict data values for the time series. The online system determines error residue values based on difference between predicted data values and actual data values of the time series. The online system determines level change in the error residue to identify change-points representing trend changes or level-shifts in the original time-series. The online system takes corrective action based on the change-point information and the trend following the occurrence of the change-point, for example, sending alerts or instructions for causing reconfiguration of the systems such as the computer network.

Abstract: An online system receives time series data and analyzes the data for identifying trend changes or level shifts in the time series. The time series data may describe resource utilization of systems, for example, bandwidth of computer networks. The online system uses prediction models, for example, machine learning based prediction models using regression to predict data values for the time series. The online system determines error residue values based on difference between predicted data values and actual data values of the time series. The online system determines level change in the error residue to identify change-points representing trend changes or level-shifts in the original time-series. The online system takes corrective action based on the change-point information and the trend following the occurrence of the change-point, for example, sending alerts or instructions for causing reconfiguration of the systems such as the computer network.

Abstract: An online system receives time series data and analyzes the data for identifying trend changes or level shifts in the time series. The time series data may describe resource utilization of systems, for example, bandwidth of computer networks. The online system uses prediction models, for example, machine learning based prediction models using regression to predict data values for the time series. The online system determines error residue values based on difference between predicted data values and actual data values of the time series. The online system determines level change in the error residue to identify change-points representing trend changes or level-shifts in the original time-series. The online system takes corrective action based on the change-point information and the trend following the occurrence of the change-point, for example, sending alerts or instructions for causing reconfiguration of the systems such as the computer network.

Abstract: An online system receives time series data and analyzes the data for identifying trend changes or level shifts in the time series. The time series data may describe resource utilization of systems, for example, bandwidth of computer networks. The online system uses prediction models, for example, machine learning based prediction models using regression to predict data values for the time series. The online system determines error residue values based on difference between predicted data values and actual data values of the time series. The online system determines level change in the error residue to identify change-points representing trend changes or level-shifts in the original time-series. The online system takes corrective action based on the change-point information and the trend following the occurrence of the change-point, for example, sending alerts or instructions for causing reconfiguration of the systems such as the computer network.

Abstract: An online system receives time series data and analyzes the data for identifying trend changes or level shifts in the time series. The time series data may describe resource utilization of systems, for example, bandwidth of computer networks. The online system uses prediction models, for example, machine learning based prediction models using regression to predict data values for the time series. The online system determines error residue values based on difference between predicted data values and actual data values of the time series. The online system determines level change in the error residue to identify change-points representing trend changes or level-shifts in the original time-series. The online system takes corrective action based on the change-point information and the trend following the occurrence of the change-point, for example, sending alerts or instructions for causing reconfiguration of the systems such as the computer network.

Abstract: An online system receives time series data and analyzes the data for identifying trend changes or level shifts in the time series. The time series data may describe resource utilization of systems, for example, bandwidth of computer networks. The online system uses prediction models, for example, machine learning based prediction models using regression to predict data values for the time series. The online system determines error residue values based on difference between predicted data values and actual data values of the time series. The online system determines level change in the error residue to identify change-points representing trend changes or level-shifts in the original time-series. The online system takes corrective action based on the change-point information and the trend following the occurrence of the change-point, for example, sending alerts or instructions for causing reconfiguration of the systems such as the computer network.

Abstract: Methods, systems, and computer program products for distributed and localized policy and charging control in cellular networks are provided herein.

Abstract: Methods, systems, and computer program products for distributed and localized policy and charging control in cellular networks are provided herein.

Abstract: Methods and arrangements for managing variable-priority flows in a network. Requests for transmission of digital content are received from client devices, each request being associated with a priority, the requests including at least one lower-priority delayable request and at least one higher-priority request. A usable bandwidth for delivery of the requested digital content through the network is estimated and, within the estimated usable bandwidth, there is controlled at least one flow associated with the at least one lower-priority request in the presence of at least one flow for the at least one higher-priority request. Other variants and embodiments are broadly contemplated herein.

Abstract: Methods and arrangements for characterizing software base-station workloads. Input system parameters are mapped to work-determining parameters which act to determine computational requirements of a dynamic workload. A synthetic experiment is undertaken to measure the computational requirements determined by the work-determining parameters.

Abstract: Methods, systems, and articles of manufacture for dynamically delayed delivery of content in a network are provided herein. A method includes receiving, from a user device coupled to a network, a request for a content from the network; estimating a measure of available bandwidth on the network for delivery of the requested content at each of multiple periods of time; generating multiple schedules for the delivery of the requested content, wherein each of the multiple schedules is based on (i) the measure of available bandwidth on the network at one of the multiple periods of time and (ii) a predefined set of scheduling rules associated with the user device and the network; and delivering the requested content to the user device according to a schedule selected from the multiple schedules.

Abstract: Methods, systems, and articles of manufacture for dynamically delayed delivery of content in a network are provided herein. A method includes receiving, from a user device coupled to a network, a request for a content from the network; estimating a measure of available bandwidth on the network for delivery of the requested content at each of multiple periods of time; generating multiple schedules for the delivery of the requested content, wherein each of the multiple schedules is based on (i) the measure of available bandwidth on the network at one of the multiple periods of time and (ii) a predefined set of scheduling rules associated with the user device and the network; and delivering the requested content to the user device according to a schedule selected from the multiple schedules.

Abstract: Methods and arrangements for undertaking delayed delivery of digital content. At least one request for transmission of digital content is received from a client device. There is estimated a usable bandwidth for delivery of the digital content during a predetermined time period. There is offered to the client device two or more delivery schedules for delayed digital content delivery during the predetermined time period. Other variants and embodiments are broadly contemplated herein.

Abstract: Methods, apparatuses, and computer program products for controlling communication between whitespace devices are provided.

Abstract: Methods and arrangements for managing variable-priority flows in a network. Requests for transmission of digital content are received from client devices, each request being associated with a priority, the requests including at least one lower-priority delayable request and at least one higher-priority request. A usable bandwidth for delivery of the requested digital content through the network is estimated and, within the estimated usable bandwidth, there is controlled at least one flow associated with the at least one lower-priority request in the presence of at least one flow for the at least one higher-priority request. Other variants and embodiments are broadly contemplated herein.

Abstract: Methods and arrangements for undertaking delayed delivery of digital content. At least one request for transmission of digital content is received from a client device. There is estimated a usable bandwidth for delivery of the digital content during a predetermined time period. There is offered to the client device two or more delivery schedules for delayed digital content delivery during the predetermined time period. Other variants and embodiments are broadly contemplated herein.

Abstract: Methods and arrangements for characterizing software base-station workloads. Input system parameters are mapped to work-determining parameters which act to determine computational requirements of a dynamic workload. A synthetic experiment is undertaken to measure the computational requirements determined by the work-determining parameters.

Abstract: Methods, apparatuses, and computer program products for controlling communication between whitespace devices are provided.

Abstract: Methods, apparatuses, and computer program products for controlling communication between whitespace devices are provided.

Abstract: A method, an apparatus and an article of manufacture for communicating power consumption information. The method includes comparing power consumption information of at least one user with power consumption information of at least one peer user to generate a ranking of each user in order of power consumption, monitoring the user power consumption derived from non-smart appliance usage and to generate a recommendation pertaining to at least one smart appliance for the at least one user, and publishing the generated ranking and recommendation information to a network for communication with the at least one user.