Ozkan et al., 2025 - Google Patents
Federated carbon intelligence for sustainable AI: Real-time optimization across heterogeneous hardware fleetsOzkan et al., 2025
View HTML- Document ID
- 10806166820273458375
- Author
- Ozkan M
- Ozkan C
- Publication year
- Publication venue
- MRS Energy & Sustainability
External Links
Snippet
As AI infrastructure expands globally, managing the sustainability of large-scale inference workloads across diverse hardware fleets has become a critical challenge. While prior frameworks such as EcoServe and Google's carbon-intelligent computing have addressed …
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F1/00—Details of data-processing equipment not covered by groups G06F3/00 - G06F13/00, e.g. cooling, packaging or power supply specially adapted for computer application
- G06F1/26—Power supply means, e.g. regulation thereof
- G06F1/32—Means for saving power
- G06F1/3203—Power Management, i.e. event-based initiation of power-saving mode
- G06F1/3234—Action, measure or step performed to reduce power consumption
-
- 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
- G06F9/48—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
- 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
- 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
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F2217/00—Indexing scheme relating to computer aided design [CAD]
- G06F2217/78—Power analysis and optimization
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Hameed et al. | A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems | |
| Wang et al. | Reinforcement learning based task scheduling for environmentally sustainable federated cloud computing | |
| US12463875B2 (en) | Apparatus, articles of manufacture, and methods to partition neural networks for execution at distributed edge nodes | |
| Guitart | Toward sustainable data centers: a comprehensive energy management strategy | |
| Deng et al. | Reliability‐aware server consolidation for balancing energy‐lifetime tradeoff in virtualized cloud datacenters | |
| US8056046B2 (en) | Integrated system-of-systems modeling environment and related methods | |
| He | A unified metric architecture for ai infrastructure: A cross-layer taxonomy integrating performance, efficiency, and cost | |
| Rahmani et al. | Burst‐aware virtual machine migration for improving performance in the cloud | |
| Ozkan et al. | Federated carbon intelligence for sustainable AI: Real-time optimization across heterogeneous hardware fleets | |
| Saleem et al. | A survey on dynamic application mapping approaches for real-time network-on-chip-based platforms | |
| Yang et al. | A Survey on Task Scheduling in Carbon-Aware Container Orchestration | |
| Dey et al. | P‐EdgeCoolingMode: an agent‐based performance aware thermal management unit for DVFS enabled heterogeneous MPSoCs | |
| Rahmani et al. | SPP: Stochastic process-based placement for VM consolidation in cloud environments | |
| Hewage et al. | A framework for carbon-aware real-time workload management in clouds using renewables-driven cores | |
| Chauhan et al. | A survey of deep reinforcement learning techniques for Energy-efficient green cloud computing | |
| Pinky et al. | Enhanced Task Scheduling With Metaheuristics for Delay and Energy Optimization in Cloud‐Fog Computing | |
| Liu et al. | A prediction-based multi-objective vm consolidation approach for cloud data centers | |
| Pasricha et al. | Data analytics enables energy-efficiency and robustness: from mobile to manycores, datacenters, and networks (special session paper) | |
| Gill et al. | Sustainable cloud computing realization for different applications: a manifesto | |
| US20240193617A1 (en) | Methods and apparatus to assign workloads based on emissions estimates | |
| Zhang | A method to manage the energy consumption of cloud centers for predictability in neuro-fuzzy networks | |
| Sixdenier et al. | SIDAM: A design space exploration framework for multi-sensor embedded systems powered by energy harvesting | |
| Majeed et al. | Energy efficiency in big data complex systems: a comprehensive survey of modern energy saving techniques | |
| Wang et al. | Intelligent scheduling with deep fusion of hardware-software energy-saving principles for greening stochastic nonlinear heterogeneous super-systems | |
| Loukil et al. | Self‐Adaptive On‐Chip System Based on Cross‐Layer Adaptation Approach |