ISO/IEC JTC 1 SC 42 Artificial Intelligence - Working Group 4
Use Cases & Applications   04/16/2024

Editor's comments and enhancements are shown in green. [✓ Reviewed]

The quality of use case submissions will be evaluated for inclusion in the Working Group's Technical Report based on the application area, relevant AI technologies, credible reference sources (see References section), and the following characteristics:

  • [1] Data Focus & Learning: Use cases for AI system which utilizes Machine Learning, and those that use a fixed a priori knowledge base.
  • [2] Level of Autonomy: Use cases demonstrating several degrees (dependent, autonomous, human/critic in the loop, etc.) of AI system autonomy.
  • [3] Verifiability & Transparency: Use cases demonstrating several types and levels of verifiability and transparency, including approaches for explainable AI, accountability, etc.
  • [4] Impact: Use cases demonstrating the impact of AI systems to society, environment, etc.
  • [5] Architecture: Use cases demonstrating several architectural paradigms for AI systems (e.g., cloud, distributed AI, crowdsourcing, swarm intelligence, etc.)
  • [6] Functional aspects, trustworthiness, and societal concerns
  • [7] AI life cycle components include acquire/process/apply.
These characteristics are identified in red in the use case.

No. 35 ID: Use Case Name: Causality-based Thermal Prediction for Data Center
Application
Domain		Other
Deployment
Model		On-premise systems
StatusPrototype
ScopeData Center
Objective(s)Minimize energy usage in managing data center
Short
Description
(up to
150 words) 		Data centers tend to be overcooled to prevent computing machines from failing due to heat. A reliable fine-grained control that could regulate air control unit (ACU) supply air temperature or flow is needed to avoid overcooling. Methods that are based on correlation-based techniques do not generalize well. Hence, we seek to uncover the causal relationship between ACUs supplying cool air and temperature at the cabinets to prioritize which ACUs should be regulated to control a hot-spot near a cabinet
Complete Description First, we perform experiments in 6SigmaRoom for the layout of the data center being studied. We collect time-series data for supply air temperature and flow per ACU, and for inlet temperature at the cabinets. Next, we test the recorded time series for checking if Granger-causality (G-causality) can be established between the supply air temperature from an ACU to a cabinet. G-causality establishes the unidirectional temporal precedence for data center control actions from ACUs that leads to changes in specific cabinet temperatures. A variable X is said to Granger-Cause Y if, including data about past terms from X, leads to a better prediction of the future value of Y (i.e., Yt+1) than predicting Yt+1 based solely on past terms from Y.

We show by way of simulation that the ACU flows that Granger-Cause reduction in temperature at a cabinet provide a larger share of influence (based on Zone of Influence/Thermal Correlation Index from the simulation) on the cabinet. This could allow an operator to come up with a better control strategy to control hotspots in a data center by regulating ACU supply air temperature/flows.

StakeholdersData center owner; Data center users; Environment
Stakeholders'
Assets, Values
Systems'
Threats &
Vulnerabilities		Incorrect AI system use; Security threats
Performance
Indicators (KPIs) 		Seq. No. Name Description Reference to mentioned
use case objectives
1 Zone of Influence/ Thermal Correlation Index Extent of influence of ACUs on data center racks. Helps in improved control.
2 Training efficiency Amount of necessary data for training might lead to practical obstacles in application.
AI Features Task(s)Prediction
Method(s)Regression
Hardware64 GB RAM Windows server
TopologyNA
Terms &
Concepts Used		Granger Causality
Standardization
Opportunities
Requirements
  • Standardization towards testin
Challenges
& Issues		Data sufficiency
Societal Concerns Description Promoting sustainable industries, and investing in scientific research and innovation, are all important ways to facilitate sustainable development.
SDGs to
be achieved 		Industry, Innovation, and Infrastructure
Data Characteristics
Description
Source
Type
Volume (size)
Velocity
Variety
Variability
(rate of change)
Quality
Scenario Conditions
No. Scenario
Name 		Scenario
Description 		Triggering Event Pre-condition Post-Condition






Training Scenario Name:
Step No. Event Name of
Process/Activity 		Primary
Actor 		Description of
Process/Activity 		Requirement






Specification of training data
Scenario Name Evaluation
Step No. Event Name of
Process/Activity 		Primary
Actor 		Description of
Process/Activity 		Requirement






Input of Evaluation
Output of Evaluation
Scenario Name Execution
Step No. Event Name of
Process/Activity 		Primary
Actor 		Description of
Process/Activity 		Requirement






Input of Execution
Output of Execution
Scenario Name Retraining
Step No. Event Name of
Process/Activity 		Primary
Actor 		Description of
Process/Activity 		Requirement






Specification of retraining data
References
No. Type Reference Status Impact of
use case 		Originator
Organization 		Link
1 Conference Causality-based Thermal Prediction for Data Center. 2018 IEEE 23rd International Conference on Emerging Technologies and Factory Automation (ETFA). Turin, Italy. 4-7 Sept. 2018. Published Use case taken from this reference ABB Link

  • Peer-reviewed scientific/technical publications on AI applications (e.g. [1]).
  • Patent documents describing AI solutions (e.g. [2], [3]).
  • Technical reports or presentations by renowned AI experts (e.g. [4])
  • High quality company whitepapers and presentations
  • Publicly accessible sources with sufficient detail

    This list is not exhaustive. Other credible sources may be acceptable as well.

    Examples of credible sources:

    [1] B. Du Boulay. "Artificial Intelligence as an Effective Classroom Assistant". IEEE Intelligent Systems, V 31, p.76-81. 2016.

    [2] S. Hong. "Artificial intelligence audio apparatus and operation method thereof". N US 9,948,764, Available at: https://patents.google.com/patent/US20150120618A1/en. 2018.

    [3] M.R. Sumner, B.J. Newendorp and R.M. Orr. "Structured dictation using intelligent automated assistants". N US 9,865,280, 2018.

    [4] J. Hendler, S. Ellis, K. McGuire, N. Negedley, A. Weinstock, M. Klawonn and D. Burns. "WATSON@RPI, Technical Project Review".
    URL: https://www.slideshare.net/jahendler/watson-summer-review82013final. 2013