Tuli et al., 2022 - Google Patents
SimTune: Bridging the simulator reality gap for resource management in edge-cloud computingTuli et al., 2022
View HTML- Document ID
- 6538811430186306176
- Author
- Tuli S
- Casale G
- Jennings N
- Publication year
- Publication venue
- Scientific Reports
External Links
Snippet
Industries and services are undergoing an Internet of Things centric transformation globally, giving rise to an explosion of multi-modal data generated each second. This, with the requirement of low-latency result delivery, has led to the ubiquitous adoption of edge and …
- 230000001537 neural 0 abstract description 36
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
- 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
- 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
- G06F17/5009—Computer-aided design using simulation
-
- 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
- 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
- G06F15/163—Interprocessor communication
-
- 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
- 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
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/30—Monitoring
- G06F11/34—Recording or statistical evaluation of computer activity, e.g. of down time, of input/output operation; Recording or statistical evaluation of user activity, e.g. usability assessment
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computer systems utilising knowledge based models
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Tuli et al. | COSCO: Container orchestration using co-simulation and gradient based optimization for fog computing environments | |
Xie et al. | Real-time prediction of docker container resource load based on a hybrid model of ARIMA and triple exponential smoothing | |
US20200293838A1 (en) | Scheduling computation graphs using neural networks | |
US11283863B1 (en) | Data center management using digital twins | |
CN115427967A (en) | Determine multivariate time series data dependencies | |
Yu et al. | Workflow performance prediction based on graph structure aware deep attention neural network | |
Tuli et al. | SimTune: Bridging the simulator reality gap for resource management in edge-cloud computing | |
Barba-Gonzaléz et al. | Multi-objective big data optimization with jmetal and spark | |
Li et al. | Adaptive management and multi-objective optimization of virtual machine in cloud computing based on particle swarm optimization | |
Huang et al. | A Simulation‐Based Approach of QoS‐Aware Service Selection in Mobile Edge Computing | |
Plebani et al. | Fog computing and data as a service: A goal-based modeling approach to enable effective data movements | |
Nezafat Tabalvandani et al. | Reliability-aware web service composition with cost minimization perspective: a multi-objective particle swarm optimization model in multi-cloud scenarios | |
Yadav et al. | A survey of the workload forecasting methods in cloud computing | |
Wu et al. | Intent-driven cloud resource design framework to meet cloud performance requirements and its application to a cloud-sensor system | |
Yu et al. | Faasdeliver: Cost-efficient and qos-aware function delivery in computing continuum | |
Hosseini Shirvani | A survey study on task scheduling schemes for workflow executions in cloud computing environment: classification and challenges | |
Huang et al. | Performance modelling and analysis for IoT services | |
Velu et al. | CloudAIBus: a testbed for AI based cloud computing environments | |
Khaledian et al. | AI-based & heuristic workflow scheduling in cloud and fog computing: a systematic review | |
Qiao et al. | EdgeOptimizer: A programmable containerized scheduler of time-critical tasks in Kubernetes-based edge-cloud clusters | |
Mangiaracina et al. | Efficient data as a service in fog computing: An adaptive multi-agent based approach | |
Lin et al. | Learning to make auto-scaling decisions with heterogeneous spot and on-demand instances via reinforcement learning | |
Zhang et al. | Monitoring-based task scheduling in large-scale SaaS cloud | |
Ilager | Machine learning-based energy and thermal efficient resource management algorithms for cloud data centres | |
Herrera et al. | Multi-Layered Continuous Reasoning for Cloud-IoT Application Management |