Sustainability in IT and Infrastructure Design andOperationalisation:
Addressing environmental issues,energy/material/carbon footprint and defining KPIYuri Demchenko
CCI Seminar, University of Amsterdam
Presented 10 September 2021
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Outline• Sustainability in UN SDG and European Strategy on Sustainability and Horizon EuropeMission Goals• Sustainability definition and KPI• Defining Architecture model for Data Driven Applications optimisation• Data Analytics Operationalisation with SRE– DevOps and SRE– SRE for Data Science and AI projects• Discussion

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Horizon Europe Preparation and Concepts• Mission-oriented research and innovation in the European Union• Focus on UN 17 SDG discussed during preparation stage

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5 Mission areas in HE••••

Adaptation to climate change including societal transformation
CancerClimate-neutral and smart cities
Healthy oceans, seas, coastal andinland waters• Soil health and food• Contributing to Sustainability– European policy on cloud computing– Industry 5.0– European Data Spaces

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(1) HORIZON-CL4-2021-DATA-01-01: Technologies and solutions for compliance, privacy
preservation, green and responsible data operations (RIA) – total 52Mln / 10Mln prj•Activities are expected to start at TRL 2-3 and achieve TRL 4-5 by the end of the project

Expected Outcome: Project results are expected to contribute to the following expected outcomes:•improve the efficiency and the use of trustworthy digital technologies to address the requirements of citizens,companies and administrations/public organisations on privacy and commercial and administrative
confidentiality as well as responsible, fair and environmentally friendly (e.g. in terms of energy/carbon/material
footprint) data operations in data spaces, across the data life cycle.

Scope: Digital technologies, methods, architectures and processes for user-friendly, safe, trustworthy,compliant, fair, transparent, accountable and environmentally sustainable collection, storage, processing,querying, analytics and delivery of data.
••••

The technologies shall facilitate sharing and manipulation of data in compliance with prevailing and emerging legislation (e.g. GDPR) fordata processors and data subjects/rightholders and other stakeholders.
The technologies and solutions shall enable safe and secure data handling, sharing and re-use in the context of common European data
spaces in various situations and application areas.

The scope also includes the combination of technological and social innovation, technologies and solutions thatenable environmentally sustainable data operations (e.g. by optimising/minimising/de-centralisingprocessing, transfer and storage of data and avoiding unnecessary data manipulations, using energyharvesting sensors/devices etc.),
Technologies and solutions for ensuring human, fair and ethically sound collection, processing andmanipulation of data, in line with the principles of responsible/trustworthy AI.

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Industrial Data Spaces: Sovereign Data – How sustainability canbe addressed?
•Decentral Control
Federated
Architecture

Sovereignty
Data Ownership

•

Trust

Scalability

CertifiedParticipants

Network Effects•

Ecosystem

Security

Platforms andServices

On-Demand Data
ExchangeOpenness

Governance

Neutrality andUser Community

Mutual Rules ofthe Game

Data sharing andSovereignty are key to makevalue of data
Data Sovereignty achievedby attaching policy andusage conditions to data
Enabling regulated
environment for dataprocessing– Enclave computing inmodern cloud technology[ref] IDS/IDSA slides courtesy Prof. Dr.-Ing. Boris Otto,
Fraunhofer ISST,Industrial Data Space Association, and Prof.
Dr. Jan Jürjens Fraunhofer, ISST, University Koblenz-Landau

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SDG Goals and relation to ICT
Selected SDG goals (data technologies
linked)• 9. Innovation and Infrastructure• 11. Sustainable cities and communities• 12. Responsible consumption• 7. Renewable Energy• 13. Climate actions?
• 16. Peace and justice?
• 17. Partnership for the goals

ICT as facilitating factor for SDG:
• SDG related initiatives in Data Science
Data related technologies
Discussion topic 1• FAIR Data principles?
• GDPR?
• Data Spaces, GAIA-X and IDSAArchitecture?
• Data sharing and data markets?
• Stock exchanges and Blockchain?
• Cloud – edge – mobile access smartphones?
Industry and production• Automation, Robotics, AI

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Sustainability breakout and mapping• Sustainability in HE (Mission areas) is considered in the context of SDG• Societal impact– People and skills• Energy efficiency as part of Environmental impact– Energy re-use• Resources– Circular economy• Technological continuity for sustainability– Architectural continuity and design patterns re-usability– Interoperability and technology/device lifecycle (compatibility between devices of differentgeneration)– Standardisation (LTE is a good example and 5G as currently available technology)

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Definition: Sustainable IT
https://circularcomputing.com/what-is-sustainable-it/• Sustainable IT, also known as Green IT, covers the manufacturing, use, management anddisposal of information technology in a way that minimises its impact on theenvironment.
– Read about resources required for production one laptop• The Green Software model includes– Software lifecycle, sustainability criteria, product metrics, procedures for stakeholders, stock
recommendations, and tools that support environmentally friendly sustainable development,
acquisition, supply, and use• Sustainable capabilities in social axis
– Practices, adding value to customers, stakeholders, and society to provide long-term benefits
in economic, social, and environmental pillars

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Sustainability Indicators: Key to Operationalise Sustainability• Three main pillars of Sustainability:– Economic, environmental, and social.

• Sustainability indicator is a measurable aspect of environmental, economic, orsocial systems that is useful for monitoring changes in system characteristics
relevant to the continuation of human and environmental well being– Sustainability indicators must cover economic, social, and sustainable aspects of humanactivities– Sustainability indicators should be politically relevant, resonant, scientifically valid andmeasurable, i.e. obtained information must be actionable.

• Global Standards for Sustainability Reporting – GRI, Global Sustainability
Standards Board (GSSB)
https://www.globalreporting.org/standards/

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IT Sustainability Indicators for the IT Industry (10 July 2015) byhttps://www.norea.nl/
https://www.deitauditor.nl/business-en-it/it-sustainability-indicators-for-the-it-industry/• The IT sustainability indicators are developed according to the criteria for financial
statements that are set out in the International Accounting Standards Committee(IASC) framework: comprehensibility, relevance, reliability and comparability.
• The GRI Reporting framework was adopted as basis for the development of the
proposed framework, because it is the world’s most widely used sustainability
reporting framework.
– Most of the core GRI Reporting framework indicators are included in the proposed framework.
However, for simplicity, the framework does not distinguish between core and additional
indicators.
– The GRI framework was extended with IT sustainability indicators.

• The proposed framework to help companies with their voluntary reporting on IT
sustainability’s social, environmental and economic aspects:– 3 aspects: Social, Economic, Environmental– 13 categories, including materials, products and services; energy, water and cooling;
biodiversity; effluents and waste; data center; IT office; compliance– 44 indicators

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Economic Sustainability KPI relevant to IT Operation• GRI and IASC Framework: 13 categories defined– data center– IT office– compliance– products and services– Energy– water and cooling– Materials– effluents and waste– Biodiversity

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Sustainable IT components
https://www.apc.org/sites/default/files/PracticalGuideSustain
ableIT_Section_1.pdf• Hardware• Software• Network• Storage• Care/Management• Disposal• Resources

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Architecture Model for Data Driven Applications (DDA) Optimisation:
Monitoring points (indicated) to support KPI (to be defined)
NetworkAccess NetworkRANNetworkNetwork

Optimisation targets(not independent)

NetworkNetwork

Speed Opt

Data
Workflow

Data Collection

Storage

ShareStore

Load &Preprocess

Storage

Storage

Visualise,
Report, Store

Data Analysis

Storage

Storage

Algorithm Opt

Archiving

Conf&Econom
Opt
Conf&Operat
Opt
Conf&Mngnt
Opt

Infra (DS)
LayersIoT
Sensor Netw

Far Edge

Cloud

Near Edge
CDN

Manage, Monitor

Storage/Archives

Energy Opt
Speed&Perf
Opt

CPU

Storage

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Storage

Compute• VM• Container• Cluster

Storage

Network

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Problems identified: Sustainability modeling, Monitoring,
Optimisation• Revise existing definitions of Sustainability• Define model for data lifecycle and whole infra stack– Exchange data between domains (data spaces – federated data spaces)••••

Model definition and simulation – use Digital Twins
Model to adopt technology change that solve many early optimis problems
Are KPI defined?
Measurement for KPI?
– Digital Twin as a container for a model– Sensitive information• Operationalising of analytic and AI models– DevOps and SRE (Site Reliability Engineering) practice for continuous monitoring and improvement (KPI and Service
Level Objectives based)– What actions to take and how? Translate model output to action• Review IoT community experience
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From DevOps to SRE: Operationalising SE/Apps, Data
Analytics/DataOps and MLOps• Benefit from current trends to operationalize engineering/development
process• SRE (Site Reliability Engineering) introduces practice of monitoring
and fulfilling Service Level Objectives (SLO, subset of SLA) by defining
Service Level Indicators (SLI)– To be leveraged to Data Analytics model monitoring and improvement• SRE may provide an approach to facilitate early DA model deploymentand testing with real data

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From Data Lifecycle to Data Science Project Stages• Data Lifecycle• Data Science project stages: Dataflow and Lifecycle• Services/Applications Development Lifecycle• DevOps cycle

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Data Lifecycle/Transformation Model•

Data Model changes
alongdata lifecycle orevolution (Variability)
Data provenance(lineage) is adiscipline to track alldata transformations
along their lifecycle
Identifying and linking
dataMultiple Data Models and structures•
Data Variety and Variability•Semantic Interoperability

Data Model (1)Data Model (2)•Data Model (3)Data Model (4)Data (inter)linking•PID/OID•
Identification•Privacy, Opacity

Data Storage (Big Data capable)•

Data
SourceData
Filter/Enrich,Classification

Data
Analytics,
Modeling,
Prediction

Data
Delivery,
Visualisation

Consumer
applications–
Data
Collection&Registration––

Persistent data/object
identifiers (PID/OID)
Traceability vs Opacity
Referral integrity

Data repurposing,
Analytics re-factoring,
Secondary processing

BDIT4DABig Data Architecture and Algorithms18

Data Science Project/Research is based on Dataflow/Lifecycle
Model selection and Training

Operation, Monitoring
and Optimisation

Operation &Serving•••

Each phase – data preparation, model training and evaluation, and model deployment – operates on its own data set. All thesedata sets need to be isolated but linked. The pollution of data sets across the data science assembly line is one of the mostfrequent mistakes in model production.
The data science project is typically starts with some historical data or sample dataset can be somewhere in existing repositories.
Data preparation may also include connecting external data sources – data blending[Ref] https://www.knime.com/blog/analytics-and-beyond
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SLICES Data Lifecycle Model and Dataflow – Work in Progress

SLICE Data Storage Infrastructure(multi-purpose: experimental, technological, archival)

Researcher

Experiment
Plan &Implement

Data Collection& Preparation

Data Analysis
Model Building

Data Archiving &Publication/Sharing

Data Curation &Quality Assurance

Data Publishing andArchiving on EOSC
Data Sharing
Infrastructure

EOSC DataArchiving &Sharing

Data Discovery (for experiment planning and operation)••

BDIT4DA

Each Data Lifecycle stage– experiment, data collection, data analysis, and finally data archiving, works
with own data set, which are however connected. All these data sets need to be stored and possibly reused in later processes.
Many experiments and research require already existing datasets that will be available in the SLICES
data repositories or can be obtained/discovered in EOSC data repositories
From DevOps to DataOps in Data Science Projects20

Compare: Services/Applications Development Lifecycle
Continuous Experimentation and ImprovementConfiguration Management & Orchestration

Dev
All in one
instance

Test/QA

Staging

2-tier app
with test DB

2-tier app withproduction
data

Prod
2-tier app withproduction data
Multi-AZ and HAAWS CloudFormation + Chef (or Puppet, Ansible)••

Easily creates test environment close to real
Powered by cloud deployment automation tools–•Continuous development – test – integration–•DevOps2021

To enable configuration Management and Orchestration, Deployment automation
CloudFormation Template, Configuration Template, Bootstrap Template

Can be used with Chef, Puppet and Ansible, deployment automation and management tools for clouds
Cloud Powered Applications Design21

Overview: SRE and DevOps for Data Analytics Operationalisation• DevOps and SRE• SRE Engagement along Service lifecycle• SRE for Data Science and ML• SRE vs Platform Engineering• Note: Not much information available: Search for blogs and tweets

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SRE and Data Analytics Engagement
What are benefits of suchfusion/integration• Continuous model improvement– Essential for operational AI and DigitalTwins• Monitoring and optimization on bothmeasurable and assessable/ranked
KPI[ref] https://sre.google/workbook/engagement-model/
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https://www.pagerduty.com/blog/building-scaling-sre-team/• In general, an SRE looks to bridge the gap between development and operations teams toensure the reliability of the systems and are responsible for availability, latency,performance, efficiency, change management, and monitoring.

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SRE and DevOps
https://www.ibm.com/cloud/learn/site-reliability-engineering• DevOps principles: Reduce organizational silos, leverage tooling and automation• SRE practice: Use the same tooling to automate and improve operations asdevelopers use to develop and improve software• DevOps principles: Accept failure as normal, implement gradual changes• SRE practice: Use error budgets to continually deploy new features andfunctionality within acceptable levels of availability• DevOps principle: Measure everything• SRE practice: Base decisions to release new software on SLA metrics

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Example SRE Architecture at Cloud Posse
https://cloudposse.com/demo/site-reliability-engineering/

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SRE Pillars according to Capgemini [ref]ML/AI enabled monitoring as
a way to manage complex
customer and environment KPI:
Trustworthiness, Sustainability andResponsibility

KPIs are not only numerical

ML/AI enabled analysis
and optimisation[ref] https://www.capgemini.com/2020/08/site-reliability-engineering-2/
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Staged SRE implementation
https://www.capgemini.com/2020/08/site-reliability-engineering-2/• Leveraging DevOps process and practice to facilitate SRE implementation• SRE is based on well defined business process and KPI

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https://www.ibm.com/cloud/blog/site-reliability-engineering-cloudapproach-operations

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5 Keys to Operationalizing Data Analytics in the Cloud
https://www.informatica.com/blogs/5-keys-to-operationalizing-data-analytics-in-the-cloud.html

Problems with Data Science and AI project•
Take too long to build and realize benefits. Most analytics projects
take 6 to 12 months to go to production and gain enterprise-wideadoption.
•Lack collaboration among data engineers for analytics data pipelinebuilding and sharing. There’s also a lack of collaboration between
business and IT, that is, data analysts, data scientists, and dataengineers.
•Never go into production. According to research, 60% of theanalytical projects developed by organizations never areoperationalized, or put into use.
•Not in scaled production. Many analytics projects are neverdeployed at large scale. McKinsey research indicates only 8% ofcompanies successfully scale their analytics.
•Lack trust and governance. Without proper data quality or dataprotection, you can’t reliably use such insights or inform actions.
•Are rarely successful. In the case of AI/ML and predictive analytics
projects, Databricks’ research suggests only 1% are successful•
Aren’t complete. Many analytics projects don’t handle a variety ofdata, so they are incomplete. Without streaming data processing,analytics projects can result in decisions made without complete
data, and consequently, they don’t produce the desired results.

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Sustainability in IT and Infrastructure design and operation30Discussion• Topic 1: Sustainability of the key IT technologies of our economy• Topic 2: What existing model (engineering and business) can be using for intended
service optimisation for the whole service and/or data lifecycle• Topic 3: How much DevOps and SRE methods and practices are applicable toData Science and AI projects– Is SRE just a practice? Does SRE imply transformational methodology (re-thinking) for
operational objectives delivery?

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Discussion Topic 1: Data related technologies: How much they
contribute to sustainability?
••
•
•
•
•
•

FAIR Data principles?
GDPR?
Data Spaces and IDSA Architecture?
GAIA-X?
Data sharing and data markets?
Cloud – edge – mobile access - smartphones?
Stock exchanges and Blockchain? Ethereum 2.0?

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Discussion topic 2: Engineering and business for full apps development
cycle and operations• Discussion topic 2: What existing model (engineering and business)
can be used for intended service optimisation for the whole service
and/or data lifecycle

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Discussion topic 3: How much DevOps and SRE methods and practices areapplicable to Data Science and AI projects

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