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: [Choose a Suitable DVFS Policy]
Tactic sort:
Awesome Tactic
Type: Architectural Tactic
Category: resource-allocation
Title
[Choose a Suitable DVFS Policy]
Description
Selects and configures a Dynamic Voltage and Frequency Scaling (DVFS) policy tailored to the computational demands of different software modules. This involves using OS-level CPU governors—such as the conservative governor for gradual frequency scaling or assigning high-frequency cores only to heavy-load modules—to reduce unnecessary energy usage while preserving performance for dSemanding tasks
Participant
Scientific software developers
Related software artifact
OS kernel parameters (e.g., CPU governor settings), task schedulers, vCPU resource configuration
Context
Scientific software with modular workflows (e.g., HADDOCK) running in controlled computing environments (e.g., cloud or HPC clusters), where tasks have heterogeneous resource demands and frequency scaling policies can be adjusted per workload
Software feature
CPU frequency control, Energy management
Tactic intent
To improve energy efficiency by aligning CPU frequency behavior with the actual resource demands of software modules, avoiding over-provisioning and reducing unnecessary energy spikes
Target quality attribute
Energy efficiency
Other related quality attributes
Performance efficiency
Measured impact
Energy consumption; Execution time; CPU and memory utilization
