Xu et al., 2019 - Google Patents
An energy-aware computation offloading method for smart edge computing in wireless metropolitan area networksXu et al., 2019
- Document ID
- 15973167453379675201
- Author
- Xu X
- Li Y
- Huang T
- Xue Y
- Peng K
- Qi L
- Dou W
- Publication year
- Publication venue
- Journal of Network and Computer Applications
External Links
Snippet
Currently, the booming popularity and growth of mobile devices in urban cities leads to the surge of various computation-intensive mobile applications, such as virtual reality and online video, which are strict with the computing capability and battery life of the mobile devices. To …
- 238000005265 energy consumption 0 abstract description 101
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]
- G06F9/5061—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
- G06F9/5088—Techniques for rebalancing the load in a distributed system involving task migration
-
- 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]
- G06F9/5094—Allocation of resources, e.g. of the central processing unit [CPU] where the allocation takes into account power or heat criteria
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02B—INDEXING SCHEME RELATING TO CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. INCLUDING HOUSING AND APPLIANCES OR RELATED END-USER APPLICATIONS
- Y02B60/00—Information and communication technologies [ICT] aiming at the reduction of own energy use
- Y02B60/10—Energy efficient computing
- Y02B60/14—Reducing energy-consumption by means of multiprocessor or multiprocessing based techniques, other than acting upon the power supply
- Y02B60/142—Resource allocation
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02B—INDEXING SCHEME RELATING TO CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. INCLUDING HOUSING AND APPLIANCES OR RELATED END-USER APPLICATIONS
- Y02B60/00—Information and communication technologies [ICT] aiming at the reduction of own energy use
- Y02B60/10—Energy efficient computing
- Y02B60/16—Reducing energy-consumption in distributed systems
- Y02B60/167—Resource sharing
-
- 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
- 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"
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02B—INDEXING SCHEME RELATING TO CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. INCLUDING HOUSING AND APPLIANCES OR RELATED END-USER APPLICATIONS
- Y02B60/00—Information and communication technologies [ICT] aiming at the reduction of own energy use
- Y02B60/50—Techniques for reducing energy-consumption in wireless communication networks
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Xu et al. | An energy-aware computation offloading method for smart edge computing in wireless metropolitan area networks | |
Qi et al. | A QoS-aware virtual machine scheduling method for energy conservation in cloud-based cyber-physical systems | |
Chen et al. | Multitask offloading strategy optimization based on directed acyclic graphs for edge computing | |
Lu et al. | Optimization of lightweight task offloading strategy for mobile edge computing based on deep reinforcement learning | |
Xu et al. | An IoT-oriented data placement method with privacy preservation in cloud environment | |
Chen et al. | Deploying data-intensive applications with multiple services components on edge | |
US8144590B2 (en) | Distributed resource allocation in stream processing systems | |
CN113918240B (en) | Task unloading method and device | |
Ren et al. | Collaborative edge computing and caching with deep reinforcement learning decision agents | |
Liao et al. | Joint multi-user DNN partitioning and task offloading in mobile edge computing | |
CN114741191B (en) | A multi-resource allocation method for computationally intensive task correlation | |
Tong et al. | Response time and energy consumption co-offloading with SLRTA algorithm in cloud–edge collaborative computing | |
CN114745386A (en) | A neural network segmentation and unloading method in multi-user edge intelligence scenarios | |
CN117112207A (en) | A hybrid resource scheduling method based on deep reinforcement learning | |
Zhang et al. | Energy minimization task offloading mechanism with edge-cloud collaboration in IoT networks | |
Xu et al. | A meta reinforcement learning-based virtual machine placement algorithm in mobile edge computing | |
Zhang et al. | Resource scheduling of green communication network for large sports events based on edge computing | |
Yu et al. | Drag-JDEC: A deep reinforcement learning and graph neural network-based job dispatching model in edge computing | |
Wang et al. | Energy-efficient collaborative task offloading in multi-access edge computing based on deep reinforcement learning | |
Huang et al. | Computation offloading for multimedia workflows with deadline constraints in cloudlet-based mobile cloud | |
Yang et al. | An energy-efficient virtual machine placement and route scheduling scheme in data center networks | |
CN107092339A (en) | The task shunt method of mobile cloud computing node isomery | |
CN118175158A (en) | Multi-layer collaborative task unloading optimization method crossing local edge and cloud resource | |
Li et al. | Deep reinforcement learning-based power and caching joint optimal allocation over mobile edge computing | |
CN116501484A (en) | A Game Theory-Based Resource Allocation Method for Mobile Edge Computing |