Zheng et al., 2022 - Google Patents
Deep reinforcement learning-based workload scheduling for edge computingZheng et al., 2022
View HTML- Document ID
- 1254077553366005509
- Author
- Zheng T
- Wan J
- Zhang J
- Jiang C
- Publication year
- Publication venue
- Journal of Cloud Computing
External Links
Snippet
Edge computing is a new paradigm for providing cloud computing capacities at the edge of network near mobile users. It offers an effective solution to help mobile devices with computation-intensive and delay-sensitive tasks. However, the edge of network presents a …
- 230000002787 reinforcement 0 title abstract description 12
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]
- G06F9/5061—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
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
- G06F17/30861—Retrieval from the Internet, e.g. browsers
-
- 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
- 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
- G06N3/12—Computer systems based on biological models using genetic models
- G06N3/126—Genetic algorithms, i.e. information processing using digital simulations of the genetic system
-
- 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
- H04L67/1002—Network-specific arrangements or communication protocols supporting networked applications in which an application is distributed across nodes in the network for accessing one among a plurality of replicated servers, e.g. load balancing
- H04L67/1004—Server selection in load balancing
- H04L67/101—Server selection in load balancing based on network conditions
-
- 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
- H04L67/32—Network-specific arrangements or communication protocols supporting networked applications for scheduling or organising the servicing of application requests, e.g. requests for application data transmissions involving the analysis and optimisation of the required network resources
-
- 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
- G06Q30/00—Commerce, e.g. shopping or e-commerce
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATIONS NETWORKS
- H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
- H04W16/22—Traffic simulation tools or models
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F15/00—Digital computers in general; Data processing equipment in general
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Zheng et al. | Deep reinforcement learning-based workload scheduling for edge computing | |
Chen et al. | Deploying data-intensive applications with multiple services components on edge | |
Deng et al. | Dynamical resource allocation in edge for trustable internet-of-things systems: A reinforcement learning method | |
Wang et al. | Traffic and computation co-offloading with reinforcement learning in fog computing for industrial applications | |
Hossain et al. | Edge computational task offloading scheme using reinforcement learning for IIoT scenario | |
Sun et al. | Multi-objective optimization of resource scheduling in fog computing using an improved NSGA-II | |
Tang et al. | Migration modeling and learning algorithms for containers in fog computing | |
Chen et al. | Deep reinforcement learning for computation offloading in mobile edge computing environment | |
Maia et al. | An improved multi-objective genetic algorithm with heuristic initialization for service placement and load distribution in edge computing | |
Li et al. | Energy-aware task offloading with deadline constraint in mobile edge computing | |
Rashidi et al. | A hybrid heuristic queue based algorithm for task assignment in mobile cloud | |
Wei et al. | Application scheduling in mobile cloud computing with load balancing | |
Wang et al. | Optimal deployment of cloudlets based on cost and latency in Internet of Things networks | |
Li et al. | DQN-enabled content caching and quantum ant colony-based computation offloading in MEC | |
Heng et al. | Energy aware cloud‐edge service placement approaches in the Internet of Things communications | |
Liu et al. | Joint hybrid caching and replacement scheme for UAV-assisted vehicular edge computing networks | |
Jiang et al. | A reinforcement learning-based computing offloading and resource allocation scheme in F-RAN | |
Qin et al. | User‐Edge Collaborative Resource Allocation and Offloading Strategy in Edge Computing | |
Tyagi et al. | GM-WOA: a hybrid energy efficient cluster routing technique for SDN-enabled WSNs | |
Sarkar et al. | Deep learning-based energy-efficient computational offloading strategy in heterogeneous fog computing networks | |
Xu et al. | Energy-efficient virtual machine scheduling across cloudlets in wireless metropolitan area networks | |
Gupta et al. | Toward intelligent resource management in dynamic Fog Computing‐based Internet of Things environment with Deep Reinforcement Learning: A survey | |
Tiwary et al. | CPS: a dynamic and distributed pricing policy in cyber foraging systems for fixed state cloudlets | |
Li et al. | A multi-objective task offloading based on BBO algorithm under deadline constrain in mobile edge computing | |
Chen et al. | A pricing approach toward incentive mechanisms for participant mobile crowdsensing in edge computing |