National University of Defense Technology

Lailong Luo

Deke Guo

Long Zheng

Yahui Wu

Guoming Tang

Pengtao Fu

Xinyi Li

Shangsen Li

Hao Yuan

Yi Wang

Xueshan Luo

Siyuan Gu

Geyao Cheng

Junxu Xia

Xiaofeng Cao

Yan Li

Yiting Chen

Bowen Sun

A method and system for deduplication caching using an unreliable edge resource include acquiring a total storage capacity of all edge servers, searching for candidate cache files by a similarity-based hierarchical clustering (SHC) method, and acquiring file clusters of all the candidate cache files after clustering, where the candidate cache files each include a deduplicated data chunk, and based on the file clusters and an reliability of all of the edge servers, selecting, by a heuristic algorithm, a file cluster from the file clusters to cache to the edge server until a size of cached content reaches the total storage capacity. The present disclosure makes a trade-off between file availability and space efficiency, thereby effectively improving the cache hit rate in the limited edge caching space.

Unreliable edge

A method and an apparatus for data filtering are provided in this disclosure. The method is applied to a filtering system, including m buckets, each bucket including b storage slots, and each storage slot including a first domain and a second domain. The first domain is configured for recording fingerprint information of an element fingerprint of a data element, and the second domain is configured for matching and recording identifiers corresponding to the fingerprint information stored in the first domain. The method includes: determining an element fingerprint of a data element to be processed in response to an obtained target instruction; and performing a target operation on the data element based on at least the element fingerprint and stored information of the first domain and the second domain in each storage slot. The target operation at least includes one or more of inserting, querying and deleting operations.

Method and apparatus for data filtering

A method for updating a stable cuckoo filter used for membership testing of data streams, executed by a processor, is described. The method includes the steps of: performing a first hash on a first element to be inserted into the stable cuckoo filter to determine a first candidate bucket; performing a second hash on a fingerprint of the first element to determine a second candidate bucket; selecting a target candidate bucket from a group consisting of the first candidate bucket and the second candidate bucket; inserting the first element into the target candidate bucket; updating the stable cuckoo filter according to one or more of a random update strategy, an insertion failure update strategy, a scanning strategy, a skip scanning update strategy, a blocked design strategy and a local time-sensitive update strategy; and obtaining an updated stable cuckoo filter.

Stable cuckoo filter for data streams

A battery energy storage system (BESS) including: a battery storage configured to store surplus renewable energy; a controller coupled to the battery storage and configured to control charging operations and discharging operations of the battery storage; a generation meter coupled to the controller and configured to measure renewable energy; a renewable energy generator coupled to the generation meter and configured to generate renewable energy; and a standard meter coupled to the controller and configured to measure energy provided by a power grid; wherein the controller is configured to manage energy expenditure of the BESS according to the following steps: initializing a replay buffer configured to store state transition samples; initializing a main net configured to generate a current Q-value; initializing a target net configured to generate a target Q-value; obtaining an environment state of the BESS; and selecting an action based on an &#x3f5;-greedy policy.

BESS aided renewable energy supply using deep reinforcement learning for 5G and beyond

A system for streaming data items of a streaming application via a Vertical Cuckoo Filter (VCF), including: one or more computers communicating with a provider via a network; wherein the one or more computers includes: a processor; and a storage device coupled to the processor, wherein the storage device is configured to store the VCF, an insert module, a lookup module, and a delete module, the VCF includes buckets with one or more slots and includes at least two bitmasks, the insert module is configured to perform an insert operation of a first fingerprint of a first item from the provider on the VCF, the lookup module is configured to perform a lookup operation of a second fingerprint of a second item from the provider on the VCF, the delete module is configured to perform a delete operation of a third fingerprint of a third item from the VCF.

Vertical cuckoo filters

A Mobile-Assisted edge computing framework including: one or more requests to be processed; an operator configured to assign the one or more requests to a static edge node; and a cloud configured to cache and pre-fetch services for the one or more requests; wherein the one or more requests include handled requests and unhandled requests, the static edge node is configured to process the handled requests, and one or more mobile edge nodes are configured to process the unhandled requests; and wherein the static edge node is configured to auction the unhandled requests to the one or more mobile edge nodes, the auction including: generating a candidate set of the one or more mobile edge nodes; assigning an unhandled request to a target mobile edge node of the candidate set; and processing the unhandled request via the target mobile edge node.

Mobile-assisted edge computing framework for emerging IoT applications

The present disclosure relates to an online task dispatching and scheduling system. The system includes an end device; an access point (AP) configured to receive a task from the end device; one or more edge servers configured to receive the task from the AP, the one or more edge servers including a task waiting queue, a processing pool, a task completion queue, and a scheduler, wherein the AP further includes a dispatcher utilizing Online Learning (OL) for determining a real-time state of network conditions and server loads; and the AP selects a target edge server from the one or more edge servers to which the task is to be dispatched; and wherein the scheduler utilizes Deep Reinforcement Learning (DRL) in generating a task scheduling policy for the one or more edge servers.

Online task dispatching and scheduling system and method thereof

The present disclosure relates to an edge federation system, wherein the edge federation system includes: an end device; an edge service provider (ESP) configured to contract services from the end device; an edge infrastructure provider (EIP) configured to provide edge computing resources to the contracted services from the ESP; a cloud server configured to provide service to the end device; and a consortium for managing resources between the EIP and the cloud server, wherein the consortium is further configured to calculate available resources of the EIP, calculate available resources of the cloud server, and optimize the distribution of services from the EIP and the cloud server to the end device.

Growing community of inventors

Hunan, China

Lailong Luo