The document discusses quantifying the energy consumption of Java data structures and methods to optimize energy usage. It presents a study ranking common data structures by energy efficiency. The study found refactoring Java programs to use more efficient data structure implementations based on the rankings can decrease energy consumption by 4-11%. A methodology is introduced to automatically analyze Java programs, identify data structure usage, and suggest refactors to lower energy usage.

1. The Influence of the Java Collection
Framework on Overall Energy
Consumption
Rui Pereira*, Marco Couto*, Jácome Cunha§,
João Paulo Fernandes‡, and João Saraiva*
greenlab.di.uminho.pt
* HASLab/INESC Tec, Universidade do Minho
§ NOVA LINCS, Universidade Nova de Lisboa
‡ RELEASE, Universidade da Beira Interior
{ruipereira, mcouto, jas}@di.uminho.pt, jacome@fct.unl.pt, jpf@di.ubi.pt

2. Going Green
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+ =

3. Global energy system is unsustainable
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4. Green Computing
 Caught the attention of many companies allowing them to save:
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“close to 50% of the energy costs of an organization can be
attributed to the IT departments”
- [PICMET, 2009]
“up to 90% of energy used by ICT hardware can be attributed to
software”
- [The Greenhouse Gas Protocol Report, 2013]

5. Green Software
 Reducing energy consumption through software analysis and optimization
 Problem:
 How to analyze
 How to interpret
 How to improve
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6. Green Software
 Problems (extend to programmers):
 How to analyze
 How to interpret
 How to improve
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Mining questions about software energy consumption
- [MSR’14]
Integrated energy-directed test suite optimization
- [ISTA’14]

7. Can we save energy by refactoring Java
programs to use different data structure
implementations?
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8. Research Questions
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 (RQ1) Can we define an energy consumption quantification of Java data
structures and their methods?
 (RQ2) Can we use such quantification to decrease the energy consumption
of software systems?

9. Towards a Ranking of Java data structures
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Design
 Simple scenario of:
 Storing
 Retrieving
 Deleting
 String values

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 Java Collections Framework (JCF) library
ConcurrentSkipListSet ArrayList ConcurrentHashMap
CopyOnWriteArraySet AttributeList ConcurrentSkipListMap
HashSet CopyOnWriteArrayList HashMap
LinkedHashSet LinkedList Hashtable
TreeSet RoleList IdentityHashMap
RoleUnresolvedList LinkedHashMap
Stack Properties
Vector SimpleBindings
TreeMap
UIDefaults
WeakHashMap
Sets Lists Maps
Towards a Ranking of Java data structures
Design – Data Structures

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add add clear
addAll addAll containsKey
clear add(index) containsValue
contains addAll(index) entrySet
containsAll clear get
iterateAll contains iterateAll
iterator containsAll keySet
remove get put
removeAll indexOf putAll
retainAll iterator remove
toArray lastIndexOf values
listIterator
listIterator(index)
remove
removeAll
Continues…
Sets Lists Maps
Towards a Ranking of Java data structures
Design - Methods

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 Defined population sizes (popsize) of
 25,000
 250,000
 1,000,000
 When a second data structure is needed (i.e. addAll)
 SecondaryCol = 10% of popsize
 Half existing & half new (shuffled)
Towards a Ranking of Java data structures
Design - Benchmark

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 Linux 3.13.0-74-generic OS
 8GB Ram
 Intel(R) Core(TM) i3-3240 CPU @ 3.40GHz
 Java Interpreter/Compiler versions 1.8.0_66
 RAPL/jRAPL
Towards a Ranking of Java data structures
Execution - Specifications

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 Warm-up
 Instantiated
 Populated w/ popsize
 Performed simple actions on the data structure
 Each test x20
 Extracted time/Joules consumed
 Removed lowest/highest 20%
 336 different test (Collection.method) configurations
 6720 executions for each popsize
 20,000+ different executions
Towards a Ranking of Java data structures
Execution – For every test

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Towards a Ranking of Java data structures
Results (25k pop)

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Towards a Ranking of Java data structures

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Towards a Ranking of Java data structures

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Towards a Ranking of Java data structures

19. Is faster, Greener?!
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Is slower, Greener?!
Faster
Slower

20. Research Questions
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 (RQ1) Can we define an energy consumption quantification of Java data
structures and their methods?
 (RQ2) Can we use such quantification to decrease the energy consumption
of software systems?

21. Optimizing Energy Consumption of Java Programs
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Methodology
1. Compute which implementations/methods are used in the programs
2. Look up the appropriate energy tables for the used implementations/methods
3. Choose the most energy efficient alternative

22. Optimizing Energy Consumption of Java Programs
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Applying the methodology – Data acquisition
 Obtained Java projects of a Journalism support platform
 First year OO course
 Collaborators, Journalists, Readers, Editors
 Write chronicles and reports
 Give likes and comments
 Etc.
 Average of
 36 classes
 104 methods
 2000 lines of code

23. Optimizing Energy Consumption of Java Programs
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Applying the methodology – Data acquisition
 Obtained 7 test cases to simulate usage
 Size varied vetween 2000-10000 for each test case/each entity
 Chosen popsize – 25,000 (smallest)
 Applied methodology on 5 projects
 Detected usage of any JCF implementation
 Detected which methods were used for each implementation
 Chose the most efficient implementation for each project
 Measured the changes before and after

24. Optimizing Energy Consumption of Java Programs
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Applying the methodology – Example

25. Optimizing Energy Consumption of Java Programs
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Applying the methodology – Results
 Between 4.37% - 11.05%
 Average of 6.2%

26. FW + Conclusion
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 Presented detailed study of the energy consumption of Sets, Lists, and Maps
 Quantification of the energy spent by each method (RQ1 Answer)
 Introduced a very simple methodology to optimize Java programs (RQ2 Answer)
 Consider other object types (int, objects, etc.)
 Implement an automatic refactoring plugin
 Found @ our page: http://greenlab.di.uminho.pt/

27. greenlab.di.uminho.pt
27 Portuguese National Science Foundation (FCT)
 Starts July 2016
 Grants available for young researchers
 MSc
 PhD
 Postdoc
 Universities:
 University of Minho (Braga)
 University of Beira Interior (Covilhã)
 University of Porto (Porto)
 New University of Lisbon (Lisbon)

28. The Influence of the Java Collection
Framework on Overall Energy
Consumption
Rui Pereira*, Marco Couto*, Jácome Cunha§,
João Paulo Fernandes‡, and João Saraiva*
greenlab.di.uminho.pt
* HASLab/INESC Tec, Universidade do Minho
§ NOVA LINCS, Universidade Nova de Lisboa
‡ RELEASE, Universidade da Beira Interior
{ruipereira, mcouto, jas}@di.uminho.pt, jacome@fct.unl.pt, jpf@di.ubi.pt