An Environmental Chargeback for Data Center and Cloud Computing Consumers

Digital Enterprise Research Institute www.deri.ie

An Environmental Chargeback for Data
Center & Cloud Computing Consumers
Edward Curry, Souleiman Hasan, Mark White, Hugh Melvin
ed.curry@deri.org - www.edwardcurry.org

1st International Workshop on Energy-Efficient Data Centres, Madrid,
2012

Copyright 2011 Digital Enterprise Research Institute. All rights reserved.

Digital Enterprise Research Institute
National University of Ireland, Galway
Enabling Networked Knowledge

Overview

 Motivation for Environmental Chargeback

 Environmental Chargeback Model
 Requirements
 Definition Methodology
 Allocating Impacts

 Proof of Concept at DERI

 Related Work

 Conclusions & Future Work


The Impact of Search?

Figures and Image from www.google.com/green


Cost of Other Services?



Google’s Carbon Footprint

Is Google solely responsible for these
emissions?

What about the 1 billion users that use
Google’s services everyday?

Do the users bear some responsibility?



Google’s Carbon Footprint



DC Service Supply Chain

Zero Provide
CO2 Intensity Supply Power Services
IaaS
PaaS
SaaS Home User
Renewable Energy BPaaS
XaaS
Cause of Cause of
High Environmental Server 1 Environmental
CO2 Intensity Impacts Impacts
Service …
Service …
Service N Corporate CSR
Coal Power Plant User

Power Generation Data Center End Consumers
(Utility or On-site)


The Polluter Pays

 Principle of ‘The Polluter Pays’
 Acceptance by governments, businesses, and public

 End-users IT needs are reason for existence of DC
 Little information flows to consumers on the environmental
impacts of their service usage
 Little opportunity to change behavior to be more ecologically
sound

 The Challenge: Tie emissions back to point of usage, so
consumer are better informed
 Solution: An Environmental Chargeback Model


Empowering the Consumer

 Raising Consumer Awareness of Envir. Impacts
 Understand the relationships between actions and impacts
 Induce Efficient Usage of Data Center Resources
 Improving access to resource consumption information
– Can reduce usage (i.e. paper, energy)
 Empower end-users to make sustainable choices:
– Could the service be scheduled (invoked) when renewable power
sources are available?
– Could it be invoked less often?
 Embed Service Usage within Sustainable IT Practices
 Include environmental impacts in business and decision-making
processes


Model Requirements

 Equitable
 Consumer only charged for the impacts they cause. One consumer should
not subsidize the impacts of another consumer
 Accuracy & Auditability
 Charge for actual impacts, and maintain records to handle inquiries
 Understandable
 Consumer must understand the charging process & methodology
 Controllable & Predictable
 Ability to control or predict the cost of performing activity
 Flexible & Adaptable
 Support multiple service types (i.e. PaaS, IaaS, SaaS) and dynamic cost
models (i.e. include capital impacts, operational impacts
 Scalable
 Capacity to handle small- and large-scale services
 Economical
 Inexpensive to design, implement, deploy, and run, including data
collection, processing and reporting to consumers


Definition Methodology

 Step 1. Identify service and define environmental system boundary:
 Identify the target service
 Define the system boundary of the model
 Define functional units (CO2, kWh, cost per use, etc)
 Step 2. Identify billable Items and, for each one, identify the smallest
unit that will be available as a service to consumers
 Find a reasonable easily understood unit of measurement:
 Billable Service Items: resources which consumers will be charged
– Consumers will be able to purchase these items
– Servers, virtual machines, storage, email, search, etc.
 Atomic Service Units: smallest unit of measurement
– Consumer bill will detail how many units of a resource were used
– Examples Server/VM uptime, transactions, MB/GB, etc…


Definition Methodology

 Step 3. Identify, analyze and document relevant
environmental impacts:
 Determine service resource use and associated environmental
impacts within the model boundaries
 Step 4. Define an environmental cost allocation strategy for
each billable item:
 Associating impacts to billable items
 Can be fixed, variable, or mixed charging
 Should reflect actual usage instead of allocation/reservation
 Step 5. Identify, integrate, and deploy tools necessary to
collect data and to calculate environmental chargeback:
 Environmental data collection DC resource utilization, service
workload, chargeback, and customer billing & reporting
 Tools will vary based on the service and the data center.


Allocating Impacts

 Capital Impacts – Initial Setup
 Amortized over est. life of item as fixed charge
 Building the data center facilities
– Lifespan of 10 to 15 years
 IT Equipment (Server, storage, cabling, etc.)
– Servers have a lifespan of 3 to 5 years
 Software i.e. cost of search index vs. user search
– Lifespan in days, weeks, months,…
 Operational Impacts – Running
 Straightforward allocation by usage
 Power generation and water for cooling

Allocating Impacts

Environmental CO2 intensity
Data Collection

DC Resource kWh Chargeback CO2/atomic unit
Billing
Utilization Model

Service
Workload atomic unit


Proof of Concept

 Developed a proof of concept
 Instantiation has been realized in the DERI data center

 Steps 1-3
 Service: Transaction-based data service
 System boundary: carbon dioxide from power generation
 Units: CO2 (gCO2), kilowatts (kW) and kilowatt-hours (kWh)
 Billable Service Items: User accounts
 Atomic Service Units: Single data transactions
 Environmental Impacts: 27 servers, power supplied is mixture
of fossil fuel & renewable sources (variable CO2)


Proof of Concept

 Step 4. Define allocation strategy for each billable item:
 Computational workload of all transactions is similar,
– Treat transactions as equal from impact allocation perspective

Total Service Energy ´ CO2 Intensity
CO2 per Transaction =
Number of Transactions


Proof of Concept

 Step 5. Data collection and reporting
 Leverages existing monitoring infrastructures
– Real-Time Web Service for Power CO2 Intensity
– DC Resource Energy Monitor
– Data Service Workload Monitor
 Charge calculated with real-time assessment sliding window
– Encoded as rules within a Complex Event Processing (CEP) engine
– CEP receives events allocates impacts in real-time
 Billing System
 Limitations
 Network & data storage excluded due to insufficient metering
 Approach ignores transactions initiated prior to the start of the
window and those not completed prior at end of window
 No Capital charges included in current version


Chargeback in Action


Linked dataspace for Energy
Intelligence

 Uses W3C web

Applications
standards for sharing Decision Support
Systems
Energy Analysis
Model
Energy and
Sustainability Dashboards
Situation Awareness
Apps

and integrating Complex Events

Services
Support
Entity Complex Event
Data Provenance Search &
energy data Management
Service
Catalog Query
Processing
Engine

 Linked Data
 Semantic Sensor
Linked Data
Networks

Adapter Adapter Adapter Adapter Adapter
Sources


iEnergy – Personal Usage


Experience

 Metering and Monitoring
 Piggy-backed on existing infrastructure
 Service & Infrastructure Complexity
 Shared and federated across multiple data centers
will be more difficult to allocate impacts
 Stakeholder Collaboration
 Require collaboration from more players, such as
service managers and developers
 Security and Privacy
 Considered within wider area of security and privacy
for data centers and cloud computing

Related Work

 Model complements existing research on DC EE
 SLA@SOI, GAMES, FIT4Green, OPTIMIS, ALL4Green, etc, …
 Green Grid Metrics
 Power usage effectiveness (PUE), Data Center infrastructure
Efficiency (DCiE), Water Usage Effectiveness (WUE), Carbon
Usage Effectiveness (CUE), Data Center compute Efficiency
(DCcE), The Data Center Productivity (DCP) framework
 Focus on DC efficiency

 Not Consumer-centric
 Do not inform consumer of cost of their service usage
 Do not give information necessary to change behavior to be more
sustainable


Conclusion & Future Work

 Environmental Chargeback Model
 Correlate service utilization back to service consumers
 Provide visibility into service & associated resource utilization
 Enable consumers to understand environmental footprint
 Bring transparency to sustainability of outsourced enterprise IT
 Encourage use of green power with lower footprint

 Future Work
 User evaluation to determine if model can effectively change user
behavior and reduce the impacts of services
 Deployment challenges in different environments (i.e. homogenous &
heterogeneous), at large scale (i.e. warehouse)
 Methods for allocation of capital environmental impacts


Further Reading

Curry, E.; Hasan, S.; White, M.; and Melvin, H. 2012. An
Environmental Chargeback for Data Center and Cloud Computing
Consumers. In Huusko, J.; de Meer, H.; Klingert, S.; and Somov, A., eds.,
First International Workshop on Energy-Efficient Data Centers. Madrid,
Spain: Springer Berlin / Heidelberg.

www.edwardcurry.org


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