[go: up one dir, main page]

Faraji-Mehmandar et al., 2022 - Google Patents

A self-learning approach for proactive resource and service provisioning in fog environment: M. Faraji-Mehmandar et al

Faraji-Mehmandar et al., 2022

Document ID
4000882702668389290
Author
Faraji-Mehmandar M
Jabbehdari S
Javadi H
Publication year
Publication venue
The Journal of Supercomputing

External Links

Snippet

With increasing growth in IoT, the number of devices connected to the Internet is constantly growing. Moreover, the increase in the volume of data and their transmission through the Internet of Things, as well as the existence of inadequate bandwidth, limits cloud-based …
Continue reading at link.springer.com (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
    • G06F9/5072—Grid computing
    • 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
    • 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]
    • G06F9/5005—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
    • 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
    • G06Q10/0631—Resource planning, allocation or scheduling for a business operation
    • 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
    • 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
    • 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
    • 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
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computer systems based on biological models
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance or administration or management of packet switching networks
    • H04L41/14—Arrangements for maintenance or administration or management of packet switching networks involving network analysis or design, e.g. simulation, network model or planning
    • 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

Similar Documents

Publication Publication Date Title
Nezami et al. Decentralized edge-to-cloud load balancing: Service placement for the Internet of Things
Shahidinejad et al. Joint computation offloading and resource provisioning for e dge‐cloud computing environment: A machine learning‐based approach
Khorsand et al. A self‐learning fuzzy approach for proactive resource provisioning in cloud environment
Ayoubi et al. An autonomous IoT service placement methodology in fog computing
Maurer et al. Adaptive resource configuration for cloud infrastructure management
Zhao et al. A QoS-aware IoT service placement mechanism in fog computing based on open-source development model
Faraji Mehmandar et al. A dynamic fog service provisioning approach for IoT applications
Jalali Khalil Abadi et al. A comprehensive survey on scheduling algorithms using fuzzy systems in distributed environments
Faraji-Mehmandar et al. A self-learning approach for proactive resource and service provisioning in fog environment: M. Faraji-Mehmandar et al
Chen et al. Dynamic QoS optimization architecture for cloud-based DDDAS
Faraji‐Mehmandar et al. A proactive fog service provisioning framework for Internet of Things applications: An autonomic approach
Cardellini et al. Self-adaptive container deployment in the fog: A survey
Panwar et al. Dynamic resource provisioning for service-based cloud applications: A Bayesian learning approach
Nieto et al. Deep Reinforcement Learning techniques for dynamic task offloading in the 5G edge-cloud continuum
Gu et al. Deep reinforcement learning for job scheduling and resource management in cloud computing: An algorithm-level review
Faraji-Mehmandar et al. Fuzzy Q-learning approach for autonomic resource provisioning of IoT applications in fog computing environments
Barua et al. AI-Driven Resource Allocation Framework for Microservices in Hybrid Cloud Platforms
Sha et al. A multi-objective QoS-aware IoT service placement mechanism using Teaching Learning-Based Optimization in the fog computing environment
Choupani et al. Joint resource autoscaling and request scheduling for serverless edge computing
Femminella et al. Comparison of Reinforcement Learning Algorithms for Edge Computing Applications Deployed by Serverless Technologies.
Aiswarya et al. Intelligent resource provisioning and optimization in fog computing using deep reinforcement learning
Enami et al. Resource selection in computational grids based on learning automata
Zuo et al. CSADE: a delay-sensitive scheduling method based on task admission and delay evaluation on edge–cloud collaboration
Kusic et al. Approximation modeling for the online performance management of distributed computing systems
Shamsa et al. A distributed load balancing method for IoT/Fog/Cloud environments with volatile resource support