All Tags
AWS
ai
algorithm-design
architecture
browser
cloud
cloud-efficiency
cloud-principles
cost-reduction
data-centric
data-compression
data-processing
deployment
design
documentation
edge-computing
email-sharing
energy-efficiency
energy-footprint
enterprise-optimization
green-ai
hardware
libraries
llm
locality
machine-learning
maintainability
management
measured
microservices
migration
mobile
model-optimization
model-training
multi-objective
network-traffic
parameter-tuning
performance
queries
rebuilding
scaling
services
storage-optimization
strategies
tabs
template
testing
workloads
Tactic(s) categorized as "resource-adaptation"
- Adopt use-case driven design (AT)
- Apply edge computing (AT)
- Apply granular scaling (AT)
- Choose fitting deployment paradigm (AT)
- Compress infrequently accessed data (AT)
- Optimize search & query strategies (AT)
- Rebuild software cloud-native (AT)
- Use batch instead of real-time data processing (AT)
- [Adjust vCPU Frequency by Workload] (AT)
- [Optimize Module Parameters for Efficiency] (AT)
- [Runtime Cost-Benefit–Aware Strategy Switching]
- [Using Cost-Benefit–Driven Strategy] (AT)
- Merge Overloaded Microservices to Reduce Energy Waste (AT)
- Disable Hardware
- On-Demand Components
- Use Energy-Efficient Hardware
- Adaptive Ensemble (AT)
- Detection Based Model Reconstruction (AT)
- Detection Based Model Repository (AT)
- Early Stopping in Training (AT)
- Energy Efficient Hardware (AT)