All Tags
AWS
algorithm-design
architecture
cloud-principles
cost-reduction
data-centric
data-compression
data-processing
deployment
design
edge-computing
energy-footprint
hardware
libraries
locality
machine-learning
management
measured
migration
model-optimization
model-training
performance
queries
rebuilding
scaling
services
strategies
template
workloads
Tactic: Perform specialized tasks that occur infrequently in the cloud
Tactic sort:
Awesome Tactic
Type: Architectural Tactic
Category: resource-allocation
Title
Perform specialized tasks that occur infrequently in the cloud
Description
Specialized tasks that occur infrequently might need specialized hardware. To benefit from economies of scale, it is more efficient to share these resources among more consumers. Training a ML model is an example of a time-consuming but infrequent task that requires many (specific) resources. Hence, it can be more efficient to perform this task in the cloud as cloud consumers do not need to purchase these specific resources for a workload that is not frequently used. A consumer who purchases hardware that is infrequently used and otherwise runs idle has a negative effect on energy efficiency. If multiple consumers share the hardware in the cloud, the hardware will be more efficiently used and, therefore, is expected to have a positive effect on the energy efficiency.
Participant
< unknown >
Related software artifact
Specialized tasks
Context
Migration from on-premise to cloud
Software feature
< unknown >
Tactic intent
To benefit from economies of scale in cloud, to reduce costs of purchasing specialized hardware
Target quality attribute
Cost-efficiency
Other related quality attributes
Energy-efficiency
Measured impact
< unknown >