Qin et al., 2021 - Google Patents
Optimal workload allocation for edge computing network using application predictionQin et al., 2021
View PDF- Document ID
- 7774282879310173214
- Author
- Qin Z
- Cheng Z
- Lin C
- Lu Z
- Wang L
- Publication year
- Publication venue
- Wireless Communications and Mobile Computing
External Links
Snippet
By deploying edge servers on the network edge, mobile edge computing network strengthens the real‐time processing ability near the end devices and releases the huge load pressure of the core network. Considering the limited computing or storage resources …
- 238000004422 calculation algorithm 0 abstract description 82
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
- G06F9/50—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
- G06F9/5011—Allocation of resources, e.g. of the central processing unit [CPU] to service a request the resources being hardware resources other than CPUs, Servers and Terminals
-
- 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
- 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
-
- 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
-
- 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
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATIONS NETWORKS
- H04W72/00—Local resource management, e.g. wireless traffic scheduling or selection or allocation of wireless resources
- H04W72/04—Wireless resource allocation
-
- 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
- H04L67/28—Network-specific arrangements or communication protocols supporting networked applications for the provision of proxy services, e.g. intermediate processing or storage in the network
-
- 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
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATIONS NETWORKS
- H04W72/00—Local resource management, e.g. wireless traffic scheduling or selection or allocation of wireless resources
- H04W72/12—Dynamic Wireless traffic scheduling; Dynamically scheduled allocation on shared channel
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Xu et al. | Dynamic resource allocation for load balancing in fog environment | |
Akhlaqi et al. | Task offloading paradigm in mobile edge computing-current issues, adopted approaches, and future directions | |
Alqahtani et al. | Reliable scheduling and load balancing for requests in cloud-fog computing | |
Wang et al. | MAP based modeling method and performance study of a task offloading scheme with time-correlated traffic and VM repair in MEC systems | |
Alizadeh et al. | Task scheduling approaches in fog computing: A systematic review | |
Ghanbari et al. | Resource allocation mechanisms and approaches on the Internet of Things | |
Liu et al. | A task scheduling algorithm based on classification mining in fog computing environment | |
Li et al. | Method of resource estimation based on QoS in edge computing | |
Chen et al. | LOCUS: User-perceived delay-aware service placement and user allocation in MEC environment | |
Liu et al. | Multi-objective resource allocation in mobile edge computing using PAES for Internet of Things | |
Donida Labati et al. | Computational intelligence in cloud computing | |
Qin et al. | Optimal workload allocation for edge computing network using application prediction | |
Alashaikh et al. | A survey on the use of preferences for virtual machine placement in cloud data centers | |
Li et al. | Resource scheduling based on improved spectral clustering algorithm in edge computing | |
Qayyum et al. | Mobility-aware hierarchical fog computing framework for Industrial Internet of Things (IIoT) | |
Mahmoudi et al. | SDN-DVFS: an enhanced QoS-aware load-balancing method in software defined networks | |
Raykar et al. | A novel traffic load balancing approach for scheduling of optical transparent antennas (OTAs) on mobile terminals | |
Chen et al. | A lightweight SFC embedding framework in SDN/NFV-enabled wireless network based on reinforcement learning | |
Fahimullah et al. | Machine learning-based solutions for resource management in fog computing | |
Afzali et al. | An efficient resource allocation of IoT requests in hybrid fog–cloud environment | |
Mordacchini et al. | Self-organizing energy-minimization placement of QoE-constrained services at the edge | |
Lakzaei et al. | A joint computational and resource allocation model for fast parallel data processing in fog computing | |
Xu et al. | Achieving concurrency in cloud‐orchestrated Internet of Things for resource sharing through multiple concurrent access | |
Li et al. | Delay-aware resource allocation for data analysis in cloud-edge system | |
Faraji et al. | A solution for resource allocation through complex systems in fog computing for the internet of things |