Empire Technology Development LLC

Junwei Cao

Wei Chen

Xiao Lin

Junwei Li

Han Qian

Zhen Wang

Fan Zhang

Guoyu Tu

Yuxin Wan

Zijing Yang

Technologies effective to control a distribution of energy from an energy source to a first device and to a second device are generally described. In some examples, a controller may receive first device data and second device data, from the first device and the second device, respectively. The first device data may indicate first energy consumption by the first device and a first current energy quantity of the first device. The second device data may indicate second energy consumption by the second device and a second current energy quantity of the second device. The controller may control a first switch and a second switch, based on the first and second device data, to allow a first transmission of first energy from the energy source to the first device, and to interrupt a second transmission of second energy from the energy source to the second device.

Energy flow controller

Technology is described for flexibly pricing cloud computing services. In some examples, the technology may receive at a cloud service provider a first set of information from an Internet service provider that provides an Internet service. The first set of information may relate to expected user behavior of a representative user of the Internet service provider. The technology may identify one or more computing resources employed by the Internet service provided by the Internet service provider and analyze the first set of information to determine a cycle. The cycle is a function of the computing resources expected to be employed by the representative user of the Internet service provider. The technology may compute a service fee for each instance of the cycle and allocates the service fee to the Internet service provider.

Flexibly pricing cloud computing services

Technologies and implementations for managing cloud resources.

Resource management in a cloud computing environment

An example method to determine a quality-of-experience (QoE) metric for a network communication includes receiving a media signal from the network communication, wherein the media signal includes a voice component, extracting an experience indicator from the voice component, wherein the experience indicator is a voice feature descriptive of a service quality of the network communication, evaluating the experience indicator, retrieving a quality-of-service (QoS) metric if the evaluated experience indicator reflects the service quality of the network possibly being subpar, and determining the QoE metric for the network communication based on the evaluated experience indicator and the retrieved QoS metric for the network communication.

Quality-of-experience measurement for voice services

Technologies are generally provided for maintaining performance level of a database being migrated between different cloud-based service providers employing machine learning. In some examples, data requests submitted to an original data store/database may be submitted to a machine learning-based filter for recording and analysis. Based on the results of the data requests and the filter analyses, new key value structures for a new data store/database may be created. The filter may assign performance scores to the original data requests (made to the original data store) and data requests made to the newly-created key value structures. The filter may then compare the performance scores associated with the created key value structures to each other and to performance scores associated with the original data requests and may select the created key value structures with performance scores that are at least substantially equal to those of the original data requests for the new data store.

Machine learning for database migration source

Examples are disclosed for facilitating recovery from failures associated with a storage array having a plurality of storage devices.

Triple parity encoding to facilitate data recovery

Technologies are generally described for determining a quality-of-experience (QoE) of a network communication. In some examples, a process for determining a QoE metric for a network communication includes receiving a media signal from the network communication. The process may also include extracting an experience indicator from the media signal, wherein the experience indicator corresponds to a participant's subjective usage experience associated with a service quality of the network communication, and determining the QoE metric for the network communication based on the experience indicator and a quality-of-service (QoS) metric for the network communication.

Technologies are presented for a hybrid cloud-based service model combining separate database/separate schema, shared database/separate schema, and shared database/shared schema architectures suitable for serving multiple tenants while addressing varying security needs. Roles and security level needs of different tenants may be determined based on tenant declaration or inference from tenant attributes. Tenants may then be assigned to suitable clouds or sub-clouds based on their security level needs. In some examples, a claims-based access control authorization model such as federation may be employed to support interactions between the three different types of clouds or sub-clouds under the umbrella of a single cloud-based service provider while maintaining application and data security.

Hybrid multi-tenancy cloud platform

Technologies are generally described for methods, instructions, and client applications for device discovery in a ubiquitous computing environment. In some examples, the methods, instructions, and client applications may facilitate the organization of features of devices in a ubiquitous computing environment into a series of hierarchical hash numbers, the ordering of the hierarchical hash numbers corresponding to the respective devices, and the searching for a particular one of the devices by attempting to match hashed search criteria to the ordered hierarchical hash numbers at one of the devices in the ubiquitous computing environment.

Device discovery in a ubiquitous computing environment

Embodiments of the present disclosure set forth a method for forecasting data usage in a region covered by a cellular network. The method includes defining a first subregion surrounding a first point of interest in the region; and calculating a forecasted usage amount of the cellular network in the first subregion in a second time slot based on a first usage amount of the first subregion in a first time slot, a second usage amount of a second subregion geographically adjacent to the first subregion in the first time slot, a first probability associated with mobile devices in the first subregion migrating to the second subregion, and a second probability associated with mobile devices in the second subregion migrating to the first subregion.

Growing community of inventors

Beijing, China

Junwei Cao