[go: up one dir, main page]
More Web Proxy on the site http://driver.im/

Cao et al., 2021 - Google Patents

A resource allocation strategy in fog-cloud computing towards the Internet of Things in the 5G era

Cao et al., 2021

Document ID
18356547580264743070
Author
Cao B
Fu Y
Sun Z
Liu X
He H
Lv Z
Publication year
Publication venue
2021 IEEE 26th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD)

External Links

Snippet

The rapid development of Internet of Things (IoTs) will result in massive amounts of data to be processed. The 5G technology and fog computing can reduce the service delay. A challenging problem in fog computing is how to efficiently allocate resources to guarantee …
Continue reading at ieeexplore.ieee.org (other versions)

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
    • G06F15/00—Digital computers in general; Data processing equipment in general
    • G06F15/16—Combinations of two or more digital computers each having at least an arithmetic unit, a programme unit and a register, e.g. for a simultaneous processing of several programmes
    • 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
    • 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"
    • 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
    • G06F17/30943—Information retrieval; Database structures therefor; File system structures therefor details of database functions independent of the retrieved data type
    • G06F17/30946—Information retrieval; Database structures therefor; File system structures therefor details of database functions independent of the retrieved data type indexing structures
    • 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
    • G06F17/50—Computer-aided design
    • 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
    • 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
    • G06F17/30286—Information retrieval; Database structures therefor; File system structures therefor in structured data stores

Similar Documents

Publication Publication Date Title
Keshavarznejad et al. Delay-aware optimization of energy consumption for task offloading in fog environments using metaheuristic algorithms
Iranmanesh et al. DCHG-TS: a deadline-constrained and cost-effective hybrid genetic algorithm for scientific workflow scheduling in cloud computing
Cui et al. A novel offloading scheduling method for mobile application in mobile edge computing
Adhikari et al. Application offloading strategy for hierarchical fog environment through swarm optimization
Xu et al. Multiobjective computation offloading for workflow management in cloudlet‐based mobile cloud using NSGA‐II
Teng et al. Game theoretical task offloading for profit maximization in mobile edge computing
Subramoney et al. Multi-swarm PSO algorithm for static workflow scheduling in cloud-fog environments
Laili et al. Parallel scheduling of large-scale tasks for industrial cloud–edge collaboration
Chen et al. Scheduling independent tasks in cloud environment based on modified differential evolution
CN108416465A (en) A kind of Workflow optimization method under mobile cloud environment
Zhou et al. Deep reinforcement learning-based algorithms selectors for the resource scheduling in hierarchical cloud computing
Cao et al. A resource allocation strategy in fog-cloud computing towards the Internet of Things in the 5G era
Nguyen et al. Rethinking virtual link mapping in network virtualization
CN114980216B (en) Dependency task unloading system and method based on mobile edge calculation
Chen et al. Data-driven task offloading method for resource-constrained terminals via unified resource model
Lin et al. Task scheduling algorithm based on Pre-allocation strategy in cloud computing
Huang et al. Multi objective scheduling in cloud computing using MOSSO
Agarwal et al. An Adaptive Genetic Algorithm-Based Load Balancing-Aware Task Scheduling Technique for Cloud Computing.
Cui et al. Many-objective joint optimization of computation offloading and service caching in mobile edge computing
Wu et al. A genetic-ant-colony hybrid algorithm for task scheduling in cloud system
Rostami et al. TMaLB: A Tolerable Many-Objective Load Balancing Technique for IoT Workflows Allocation
Nazari et al. IETIF: Intelligent Energy‐Aware Task Scheduling Technique in IoT/Fog Networks
Xiao et al. An efficient service-aware virtual machine scheduling approach based on multi-objective evolutionary algorithm
Su et al. RVEAPE: An Approach to computation offloading for connected autonomous vehicles
Xuan et al. Novel virtual network function service chain deployment algorithm based on Q-learning