Liu et al., 2018 - Google Patents
A reinforcement learning-based resource allocation scheme for cloud roboticsLiu et al., 2018
View PDF- Document ID
- 2187852695166537750
- Author
- Liu H
- Liu S
- Zheng K
- Publication year
- Publication venue
- IEEE Access
External Links
Snippet
In recent years, robotic systems combined with cloud computing capability have become an emerging topic of discussion in academic fields. The concept of cloud robotics allows the system to offload computing-intensive tasks from the robots to the cloud. An appropriate …
- 238000004805 robotic 0 title abstract description 33
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for programme control, e.g. control unit
- G06F9/06—Arrangements for programme control, e.g. control unit using stored programme, i.e. using internal store of processing equipment to receive and retain programme
- G06F9/46—Multiprogramming arrangements
- G06F9/50—Allocation of resources, e.g. of the central processing unit [CPU]
- G06F9/5005—Allocation of resources, e.g. of the central processing unit [CPU] to service a request
- G06F9/5027—Allocation of resources, e.g. of the central processing unit [CPU] to service a request the resource being a machine, e.g. CPUs, Servers, Terminals
- G06F9/505—Allocation of resources, e.g. of the central processing unit [CPU] to service a request the resource being a machine, e.g. CPUs, Servers, Terminals considering the load
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for programme control, e.g. control unit
- G06F9/06—Arrangements for programme control, e.g. control unit using stored programme, i.e. using internal store of processing equipment to receive and retain programme
- G06F9/46—Multiprogramming arrangements
- G06F9/48—Programme initiating; Programme switching, e.g. by interrupt
- G06F9/4806—Task transfer initiation or dispatching
- G06F9/4843—Task transfer initiation or dispatching by program, e.g. task dispatcher, supervisor, operating system
- G06F9/4881—Scheduling strategies for dispatcher, e.g. round robin, multi-level priority queues
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for programme control, e.g. control unit
- G06F9/06—Arrangements for programme control, e.g. control unit using stored programme, i.e. using internal store of processing equipment to receive and retain programme
- G06F9/46—Multiprogramming arrangements
- G06F9/50—Allocation of resources, e.g. of the central processing unit [CPU]
- G06F9/5061—Partitioning or combining of resources
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for programme control, e.g. control unit
- G06F9/06—Arrangements for programme control, e.g. control unit using stored programme, i.e. using internal store of processing equipment to receive and retain programme
- G06F9/46—Multiprogramming arrangements
- G06F9/50—Allocation of resources, e.g. of the central processing unit [CPU]
- G06F9/5083—Techniques for rebalancing the load in a distributed system
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management, e.g. organising, planning, scheduling or allocating time, human or machine resources; Enterprise planning; Organisational models
- G06Q10/063—Operations research or analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation, e.g. linear programming, "travelling salesman problem" or "cutting stock problem"
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network-specific arrangements or communication protocols supporting networked applications
- H04L67/10—Network-specific arrangements or communication protocols supporting networked applications in which an application is distributed across nodes in the network
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N99/00—Subject matter not provided for in other groups of this subclass
- G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Liu et al. | A reinforcement learning-based resource allocation scheme for cloud robotics | |
| Qu et al. | Model-assisted learning for adaptive cooperative perception of connected autonomous vehicles | |
| Hao et al. | Deep reinforcement learning for edge service placement in softwarized industrial cyber-physical system | |
| Sun et al. | Autonomous resource slicing for virtualized vehicular networks with D2D communications based on deep reinforcement learning | |
| Mishra et al. | A collaborative computation and offloading for compute-intensive and latency-sensitive dependency-aware tasks in dew-enabled vehicular fog computing: A federated deep Q-learning approach | |
| Alelaiwi | An efficient method of computation offloading in an edge cloud platform | |
| Li et al. | SMDP-based coordinated virtual machine allocations in cloud-fog computing systems | |
| US20170329643A1 (en) | Distributed node intra-group task scheduling method and system | |
| Liu et al. | Multi-user dynamic computation offloading and resource allocation in 5G MEC heterogeneous networks with static and dynamic subchannels | |
| Li et al. | Task computation offloading for multi-access edge computing via attention communication deep reinforcement learning | |
| Wang et al. | Joint server assignment and resource management for edge-based MAR system | |
| Dong et al. | NOMA-based energy-efficient task scheduling in vehicular edge computing networks: A self-imitation learning-based approach | |
| Mirmohseni et al. | LBPSGORA: create load balancing with particle swarm genetic optimization algorithm to improve resource allocation and energy consumption in clouds networks | |
| Arul et al. | Integration of IoT and edge cloud computing for smart microgrid energy management in VANET using machine learning | |
| Shafik et al. | Internet of things-based energy efficiency optimization model in fog smart cities | |
| Tang et al. | Computation offloading and resource allocation in failure-aware vehicular edge computing | |
| Wu et al. | Cloud-edge–end collaborative task offloading in vehicular edge networks: A multilayer deep reinforcement learning approach | |
| Xiao et al. | Collaborative cloud-edge service cognition framework for DNN configuration toward smart IIoT | |
| Wang et al. | An energy saving based on task migration for mobile edge computing | |
| Lu et al. | Predictive computation offloading and resource allocation in DT-empowered vehicular networks | |
| Wu et al. | Federated reinforcement learning-empowered task offloading for large models in vehicular edge computing | |
| Rao et al. | A flawless QoS aware task offloading in IoT driven edge computing system using Chebyshev based sand cat swarm optimization | |
| Sinthiya et al. | Low-cost task offloading scheme for mobile edge cloud and internet cloud using genetic algorithm | |
| Lackinger et al. | Inference load-aware orchestration for hierarchical federated learning | |
| Qin et al. | User‐Edge Collaborative Resource Allocation and Offloading Strategy in Edge Computing |