jStanley: Placing a Green Thumb on Java Collections

•Download as PPTX, PDF•

0 likes•77 views

Software developers are more and more eager to understand their code’s energy performance. However, even with such knowledge it is di cult to know how to improve the code. Indeed, little tool support exists to understand the energy consumption pro le of a software system and to eventually (automatically) improve its code. In this paper we present a tool termed jStanley which automatically nds collections in Java programs that can be replaced by others with a positive impact on the energy consumption as well as on the execution time. In seconds, developers obtain information about energy-eager collection usage. jStanley will further suggest alternative collections to improve the code, making it use less time, energy, or a combination of both. The preliminary evaluation we ran using jStanley shows energy gains between 2% and 17%, and a reduction in execution time between 2% and 13%. A video can be seen at https://greensoftwarelab.github.io/jStanley.

Software

jStanley: Placing a Green Thumb on
Java Collections
Rui Pereira*‡, Pedro Simão§†, Jácome Cunha§‡, João Saraiva*‡
http://greenlab.di.uminho.pt* HASLab/INESC Tec
§ NOVA LINCS
‡ Universidade do Minho
† DI, FCT, Universidade NOVA de Lisboa
Portugal
{ruipereira, jacome, jas}@di.uminho.pt, p.simao@campus.fct.unl.pt
ASE 2018 @ Montpellier, France, September 3-7

Global energy system is unsustainable 3
greenlab.di.uminho.pt

Green Computing
 Caught the attention of many companies allowing them to save:
greenlab.di.uminho.pt
4
“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]

Green Software
 Problems (extend to programmers):
 How to analyze
 How to interpret
 How to improve
greenlab.di.uminho.pt
5
Mining questions about software energy consumption
- [MSR’14]
Integrated energy-directed test suite optimization
- [ISTA’14]

Can we save energy by refactoring Java
programs to use different data structure
implementations?
greenlab.di.uminho.pt
6

greenlab.di.uminho.pt
7
 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

greenlab.di.uminho.pt
8
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
Continues…
Sets Lists Maps
Towards a Ranking of Java data structures
Design - Methods

greenlab.di.uminho.pt
9
Towards a Ranking of Java data structures
Results (25k pop)

greenlab.di.uminho.pt
10
Towards a Ranking of Java data structures

Optimizing Energy Consumption of Java Programs
greenlab.di.uminho.pt
11
Methodology
1. Compute which collections/methods are used in the programs
2. Look up the appropriate energy tables for the used collections/methods
3. Choose the most energy efficient alternative

Automatically with jStanley
greenlab.di.uminho.pt
12

Conclusion
greenlab.di.uminho.pt
15
 We have presented a tool, jStanley, that can automatically improve Java code
 Improvements include less energy consumption, time, or a combination of both
 It’s a plug-in for Eclipse
 Static analysis, giving results in seconds
 Quick fix suggestions will display how much is the improvement expected to be
 We are working on memory consumption to include in the jStanley
 We will run a broader study
 Measure improvements
 Measure execution time

This document discusses detecting anomalous energy consumption in Android applications. It introduces the Android Power Tutor consumption model for modeling app energy usage. The talk will cover instrumenting app source code to collect energy data, classifying source code methods by their energy impact, and GreenDroid, an Android framework for energy profiling. Open research questions are discussed, such as when optimizations like faster code can be greener, how to adapt spectrum-based fault localization for energy issues, and green-aware software product lines.

PhD Pre-Thesis

Rui Pereira

This document summarizes the pre-thesis work of Rui Pereira. The research aims to accurately detect and identify energy faults in software systems, optimize these faults without compromising functionality, and provide programmers with energy usage information. The research plan involves analyzing software to detect energy faults, visualizing energy usage data, and optimizing faults. The research questions examine detecting and identifying energy faults, optimizing faults without losing functionality, and presenting energy-aware information to programmers.

Locating Energy Hotspots in Source Code

GreenLabAtDI

This document summarizes the SPELL technique for locating energy hotspots in source code. SPELL is a spectrum-based approach that analyzes software executions at different energy levels to identify components that consume more energy. It calculates similarity scores between test cases and components using metrics like component category similarity and global similarity. These scores help pinpoint parts of the source code likely containing energy inefficient operations. The technique was found to help developers optimize programs with 26% less average energy consumption in less time compared to traditional profilers.

Can smallholders mitigate global warming? A bio-geo-chemist presents the case...

ILRI

How Data Saves Time

NetApp

Fyp Project Pitch

udheyakumare

Let's Talk a Bit About: Green Software

GreenLabAtDI

The document discusses energy efficiency in software and programming languages. It provides data on the relative energy efficiency of different programming paradigms and languages, showing that compiled languages tend to be more efficient than interpreted ones. It also analyzes the energy usage of different Java collections and finds variations between 2-17%. Mobile energy patterns are discussed as well, with a catalog of 22 common patterns identified. Quick changes and tools for measuring energy on mobile are also summarized.

The Green Lab - Experimentation in Software Energy Efficiency (ICSE)

Giuseppe Procaccianti

1) The document presents an optimization model for designing biogas infrastructure in Wisconsin using object-oriented programming in Julia. The model considers factors like costs, emissions, and trade-offs to determine optimal placement of dairy farm waste processing facilities. 2) The model defines variables, constraints, objectives and stakeholders to generate solutions for minimizing costs and emissions. Solutions show reasonable placement of more processing facilities when stakeholders value emissions savings highly. 3) Future work will implement a more complex, stochastic multi-stakeholder formulation using the CVaR method to find a compromise solution over different stakeholders rather than a single "utopia point" solution. This will provide insights into how dissatisfactions change with the CVaR parameter.

Electrical Audit of Computer Labs on Campus

Michael Pérez

The document summarizes an electrical audit of computers on campus to analyze their energy consumption and carbon emissions. It outlines developing an Excel calculator to input equipment in campus computer labs and output their estimated energy usage. A pilot study was conducted on two labs which found significant potential savings in energy costs and carbon footprint from more efficient equipment usage. The recommendation is to gather full inventory data from all campus labs to identify further opportunities to reduce energy consumption and promote sustainability initiatives.

Implementing Workload Postponing In Cloudsim to Maximize Renewable Energy Uti...

IJERA Editor

Green datacenters has become a major research area among researchers in academy and industry. One of the recent approaches getting higher attention is supplying datacenters with renewable sources of energy, leading to cleaner and more sustainable datacenters. However, this path poses new challenges. The main problem with existing renewable energy technologies is high variability, which means high fluctuation of available energy during different time periods on a day, month or year. In our paper, we address the issue of better managing datacenter workload in order to achieve higher utilization of available renewable energy. We implement an algorithm in CloudSim simulator which decides to postpone or urgently run a specific job asking for datacenter resources, based on job’s deadline and available solar energy. The aim of this algorithm is to make workload energy consumption through 24 hours match as much as possible the solar energy availability in 24 hours. Two typical, clear and cloudy days, are taken in consideration for simulation. The results from our experiments show that, for the chosen workload model, jobs are better managed by postponing or urgently running them, in terms of leveraging available solar energy. This yields up to 17% higher utilization of daily solar energy

Scheduling techniques for reducing processor energy use in MacOS

bane5isp

Can the Cloud Be Green?

Peter May-Ostendorp

ENERGY-EFFICIENT SCHEDULING OF VIRTUAL MACHINES USING ANT COLONY OPTIMIZATIO...

Abhishek Panda

solar air heater Using ANN

RAJBALA PURNIMA PRIYA

This document summarizes a research paper on using artificial neural networks (ANN) to predict the performance of solar air heaters. Solar air heaters convert solar energy to thermal energy and have various applications. ANN is a machine learning technique that can be used to optimize, simulate, and predict non-linear systems like solar air heaters. The summary discusses common ANN models like multi-layer perceptrons that are used for performance prediction. Data collection, preprocessing, and dividing the data into training and testing sets are important steps. ANN techniques can successfully predict thermal and exergetic performance of different solar air heater designs. Proper selection of input parameters and training data is key to accurate predictions.

environmental scivis via dynamic and thematc mapping

Neale Misquitta

Towards a Green Ranking for Programming Languages

GreenLabAtDI

This document discusses measuring and comparing the energy efficiency of programming languages. It notes that while faster languages are often more energy efficient, there may be languages that are slower but use less energy. It describes using benchmarks from the Computer Language Benchmarks Game to measure the energy usage of executing equivalent programs in different languages. Preliminary results found unexpected differences in energy usage versus time between languages, and that C was still the most energy efficient despite not always being the fastest. Future work is needed to compare more languages and understand factors like memory usage that impact energy efficiency.

Distributed system

MD Redaan

MapReduce is a simple programming model for processing large datasets in a distributed environment. It was developed at Google and is used for tasks like indexing documents for search, clustering articles for news, and statistical machine translation. Companies like Yahoo and Facebook also use MapReduce for applications such as powering search, data mining, ad optimization, and spam detection. MapReduce hides the complexity of distributing work across large clusters and provides fault tolerance.

Poster_Final

Nikos Anastasiadis

The document analyzes the feasibility of renewable energy and efficiency measures for a new science block. It evaluates 5 scenarios using different panel and inverter technologies to determine the best system. Scenario B using Yingli panels and SMA inverters produced the highest energy yield. A proposed 16 kW system in this configuration would generate 15,522 kWh/year, reduce emissions by 164.6 tons over 20 years, and have a positive investment return within 12 years. Energy modeling also showed potential benefits from implementing a green roof, including reduced energy consumption and emissions.

Analysis of Dynamic Latched Comparator with Reduced Delay and Energy for High...

Association of Scientists, Developers and Faculties

Design and analysis of Low-power, area efficient and high speed analogue-to-digital converters are pushing toward the use of dynamic comparators, which is used to maximize speed and power efficiency. In the existing design, an analysis on the delay of the dynamic comparators will be presented and analytical expressions are derived. From the analytical expressions, designers can obtain an intuition about the main contributors to the comparator delay and fully explore the trade-offs in dynamic comparator design. Based on the presented analysis, a new dynamic comparator is proposed; where the circuit of a conventional double tail comparator is modified for low-power and fast operation where supply voltages down to 1.2V. Without complicating the design and by adding few transistors, the positive feedback during the regeneration is strengthened, which results in remarkably reduced delay time. This paper presents a CMOS comparator that reduces the overall propagation delay and hence provides higher speed. The design is simulated in 0.18μm CMOS Technology using Tanner EDA Tools. CMOS Comparator shows that the overall propagation delay of the comparator, TPD, is 1.4872e-9 seconds, with a 1.0 V supply voltage.

Présentation PowerPoint - Diapositive 1

butest

The document summarizes research on ranking the susceptibility of electrical feeders in the New York City power grid to failure using machine learning techniques. It finds that Rankboost and SVM performed comparably well in predicting failures over 7-day periods, while MartiRank performed slightly worse. All algorithms detected periods of concept drift where less historical data was optimal for prediction. Future work could integrate analysis of feeder subcomponents, better handle concept drift, and estimate time to failure.

Energy Wasting Rate as a Metrics for Green Computing and Static Analysis

Jérôme Rocheteau

This slides aims at defining a Green Computing metrics called Energy Wasting Rate that consists in the normalized sum of the energy consumption differences between sub-components of a given component and components, behaviorally equivalent but energetically more efficient. I detail how to realize such metrics then we sketch how these metrics can be useful and relevant for static analysis focused on software energy consumption.

Rhushikesh Ghotkar Mechanical Engineer

Rhushikesh Ghotkar

The document is a resume for Rhushikesh Ghotkar seeking a position as a Mechanical Engineer. It summarizes his education, including a Master's degree from Northwestern University and Bachelor's from National Institute of Technology in India. For experience, it describes two research assistant roles where he developed a new medical device to reduce HIV testing time and designed an optomechanical energy harvester. It also lists relevant coursework, software skills, publications, and leadership experience as captain of a renewable energy vehicle team.

Design of Power and Area Efficient Approximate Multipliers

JAYAPRAKASH JPINFOTECH

This document presents a new design approach for approximate multipliers that introduces varying probability terms to alter the partial products of the multiplier. Two variants of 16-bit approximate multipliers are proposed that achieve power savings of 72% and 38% compared to an exact multiplier while maintaining better precision than existing approximate multipliers, with mean relative error as low as 7.6% and 0.02%. The performance of the proposed multipliers is evaluated using an image processing application, where one variant achieves the highest peak signal to noise ratio.

The Influence of the Java Collection Framework on Overall Energy Consumption

GreenLabAtDI

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.

GreenDroid: A Tool for Analysing Power Consumption in the Android Ecosystem

GreenLabAtDI

GreenDroid is a tool that analyzes power consumption in Android applications. It works by instrumenting app source code and tests to collect execution traces and measure power usage. GreenDroid uses a power consumption model that assigns estimated power levels to different hardware component states. It analyzes traced methods to detect potential power issues and provides visualizations of power usage over time. The goal of GreenDroid is to help optimize apps to reduce energy consumption.

Green Computing as an Engineering Discipline

GreenLabAtDI

The document discusses green computing and software optimization to reduce energy consumption. It notes that up to 50% of an organization's energy costs can be attributed to IT departments, and up to 90% of ICT hardware energy is due to software. The author describes analyzing and improving software energy efficiency through techniques like SPELL, which uses spectrum-based fault localization to identify probable energy hot spots in source code. Initial studies found SPELL helped developers reduce energy consumption by 26% on average while spending 43% less time optimizing. The author concludes by describing the Green Software Lab collaboration between several universities.

Automation Enhanced Green Campus Initiative

IRJET Journal

This document discusses automating a green campus initiative using Internet of Things technologies. It proposes using sensors placed around campus to monitor things like waste levels in trash cans. The sensors would send data to administrators, allowing issues to be addressed quickly without constant human monitoring. This reduces human error and energy usage from the sensor network overall. The document also reviews several related works that have implemented smart campus technologies, including using RFID tags, IoT devices, and computer vision algorithms to automate lighting, security, and energy usage optimization on campus.

Application of-computational-intelligence-techniques-for-economic-load-dispatch

Cemal Ardil

This document summarizes the application of computational intelligence techniques like genetic algorithms and particle swarm optimization for solving economic load dispatch problems. It first applies a real-coded genetic algorithm to minimize generation costs for a 6-generator test system with continuous fuel cost equations, showing superiority over quadratic programming. It then uses particle swarm optimization to minimize costs for a 10-generator system with each generator having discontinuous fuel options, showing better results than other published methods. The document provides background on economic load dispatch problems and optimization techniques like quadratic programming, genetic algorithms, and particle swarm optimization.

Green it

Rohana K Amarakoon

The document discusses green IT, which aims to minimize the negative environmental impacts of IT and use IT to address environmental issues. It describes green IT concepts like reducing waste, improving energy efficiency through practices like power management, and green IT purchasing. Various practical applications are outlined, such as product longevity, virtualization, and data center optimization. The advantages of green IT include reducing carbon emissions and energy costs, increasing data center cooling efficiency, and reducing server space needs through virtualization.

What's hot

Steve- Fall 2015 Research Poster revision4

Jeehwan Steve Lee

Electrical Audit of Computer Labs on Campus

Michael Pérez

Implementing Workload Postponing In Cloudsim to Maximize Renewable Energy Uti...

IJERA Editor

Scheduling techniques for reducing processor energy use in MacOS

bane5isp

Can the Cloud Be Green?

Peter May-Ostendorp

ENERGY-EFFICIENT SCHEDULING OF VIRTUAL MACHINES USING ANT COLONY OPTIMIZATIO...

Abhishek Panda

solar air heater Using ANN

RAJBALA PURNIMA PRIYA

environmental scivis via dynamic and thematc mapping

Neale Misquitta

Towards a Green Ranking for Programming Languages

GreenLabAtDI

Distributed system

MD Redaan

Poster_Final

Nikos Anastasiadis

Analysis of Dynamic Latched Comparator with Reduced Delay and Energy for High...

Association of Scientists, Developers and Faculties

Présentation PowerPoint - Diapositive 1

butest

Energy Wasting Rate as a Metrics for Green Computing and Static Analysis

Jérôme Rocheteau

Rhushikesh Ghotkar Mechanical Engineer

Rhushikesh Ghotkar

Design of Power and Area Efficient Approximate Multipliers

JAYAPRAKASH JPINFOTECH

What's hot (16)

Steve- Fall 2015 Research Poster revision4

Electrical Audit of Computer Labs on Campus

Implementing Workload Postponing In Cloudsim to Maximize Renewable Energy Uti...

Scheduling techniques for reducing processor energy use in MacOS

Can the Cloud Be Green?

ENERGY-EFFICIENT SCHEDULING OF VIRTUAL MACHINES USING ANT COLONY OPTIMIZATIO...

solar air heater Using ANN

environmental scivis via dynamic and thematc mapping

Towards a Green Ranking for Programming Languages

Distributed system

Poster_Final

Analysis of Dynamic Latched Comparator with Reduced Delay and Energy for High...

Présentation PowerPoint - Diapositive 1

Energy Wasting Rate as a Metrics for Green Computing and Static Analysis

Rhushikesh Ghotkar Mechanical Engineer

Design of Power and Area Efficient Approximate Multipliers

Similar to jStanley: Placing a Green Thumb on Java Collections

The Influence of the Java Collection Framework on Overall Energy Consumption

GreenLabAtDI

GreenDroid: A Tool for Analysing Power Consumption in the Android Ecosystem

GreenLabAtDI

Green Computing as an Engineering Discipline

GreenLabAtDI

Automation Enhanced Green Campus Initiative

IRJET Journal

Application of-computational-intelligence-techniques-for-economic-load-dispatch

Cemal Ardil

Green it

Rohana K Amarakoon

Green Computing Seminar

Nikunj_Agrawal

This document is a seminar report submitted by Mr. Nikunj P. Agrawal on the topic of "Green Computing" under the guidance of Prof. V. S. Gulhane in 2010-2011. The report begins by defining green computing as the environmentally sustainable practice of computing with minimal environmental impact. It discusses various approaches to green computing like virtualization, power management, recycling, and more efficient algorithms. It provides examples of green computing implementations and discusses how individuals and organizations can work to reduce the environmental impact of computing.

An Improvement in Power Management in green Computing using Neural Networks

IOSR Journals

This document summarizes previous work on green computing and power management techniques using neural networks. It proposes a new technique using neural networks and dynamic clustering for energy conservation in green computing. Previous approaches focused on virtualization, power management, material recycling, and algorithms for efficient routing and clustering. The proposed technique would use a neural network's learning capabilities combined with dynamic clustering to improve energy efficiency. It was implemented in a simulation and results were presented graphically. The goal is to reduce resource consumption and electronic waste through more efficient power management.

FINAL

Yogitha R Malkireddy

Green computing refers to environmentally sustainable and efficient computing practices throughout a product's lifecycle. This includes green use through energy efficient computing, green disposal like recycling, green design of efficient components, and green manufacturing with low environmental impact. Approaches to green computing involve optimizing software and deployment, like virtualization and power management, as well as recycling materials to reduce waste. The goals are to minimize environmental impact and costs while maximizing performance and sustainability.

Greencomputing

Melaku Bayih Demessie

This document defines green computing and discusses its importance. Green computing aims to reduce the environmental impact of computing through more efficient use of resources and responsible disposal of electronic waste. It involves strategies like power management, using energy efficient hardware, recycling electronics, and reducing paper usage. While green computing requires initial costs, it provides long term benefits like financial savings, energy efficiency and environmental protection. The document emphasizes that adopting green computing practices is important for a sustainable future.

Technologies, Strategies And Algorithm In Green Computing – Solution To Energ...

IJERA Editor

A safe and non-polluted environment is the basic need of a living being. But today the situation is getting changed. Our environment is getting polluted day by day at a very high rate. The use of computing devices plays a vital role in harmfulness of environment. To reduce these harmful impacts the concept of Green Computing must be implemented. In this research paper, we includes some technologies, strategies and algorithm which are used for the implementation of green computing. The main reason is the awareness of a common user. If a common user is getting aware about the harmful impacts of use of computing devices over environment and takes some steps at own level to reduce electricity, the concept of green computing will be implemented.

Self-adaptation Approaches for Energy Efficiency

Patricia Lago

The increasing energy demands of software systems have set an essential software quality requirement: energy efficiency. At the same time, the many contextual changes faced by software systems during execution can hamper their functionality and overall quality. To address both problems, self-adaptation approaches can empower software systems, at both design-time and runtime, to adapt to dynamic conditions. In this way, software systems can be more resilient to failure, hence more trustful to satisfy the demands of modern digital society. In this paper, we perform a systematic literature review to study the state-of-the-art on existing self-adaptation approaches for energy efficiency. We analyze the identified approaches from three different perspectives, namely publication trends, application domains, and types of software systems. Our findings can help solution providers to make guided decisions to enable self-adaptability in designing and engineering software systems.

sustainable computing

saimashahab1

Sustainable computing aims to reduce the environmental impact of computing through more efficient use of resources and the use of renewable energy sources. It involves developing systems that minimize waste, optimize performance and lifespan, and reduce carbon emissions. Key challenges include reducing the use of harmful materials in devices, high energy consumption from data usage and storage, and growing electronic waste. Researchers are exploring ways to address these through modular design, powering infrastructure with clean energy, and reuse/recycling of components and materials. Data-driven approaches also have potential to optimize resource allocation and infrastructure management to support sustainable development goals.

5 6

INTERNATIONAL INDEXED,REFERRED,MULTILINGUAL,INTERDISCIPLINARY, MONTHLY RESEARCH JOURNAL

Green computing aims to design, build, and operate computer systems to be more energy efficient while also improving economic viability and system performance. It seeks to reduce the negative environmental impact of computing devices through their entire lifecycles from production to disposal. Current trends in green computing include efforts to reduce e-waste, increase energy efficiency in data centers and devices, optimize data center resources through consolidation and virtualization, promote eco-labeling of green IT products, and leverage the energy efficiency of cloud computing and terminal servers.

GurminderBharani_Masters_Thesis

bharanigurminder

This document is a thesis submitted by Gurminder Bharani to Symbiosis Institute of Geoinformatics in partial fulfillment of an M.Sc. degree. The thesis is titled "Automated Drought Analysis with Python and Machine Learning". It describes using Python and machine learning techniques to automate the analysis of drought conditions from satellite and other climate data sources. The thesis includes chapters on the literature review, study area, methodology, results, discussion, conclusion, and references.

HSO: A Hybrid Swarm Optimization Algorithm for Reducing Energy Consumption in...

TELKOMNIKA JOURNAL

Mobile Cloud Computing (MCC) is an emerging technology for the improvement of mobile service quality. MCC resources are dynamically allocated to the users who pay for the resources based on their needs. The drawback of this process is that it is prone to failure and demands a high energy input. Resource providers mainly focus on resource performance and utilization with more consideration on the constraints of service level agreement (SLA). Resource performance can be achieved through virtualization techniques which facilitates the sharing of resource providers’ information between different virtual machines. To address these issues, this study sets forth a novel algorithm (HSO) that optimized energy efficiency resource management in the cloud; the process of the proposed method involves the use of the developed cost and runtime-effective model to create a minimum energy configuration of the cloud compute nodes while guaranteeing the maintenance of all minimum performances. The cost functions will cover energy, performance and reliability concerns. With the proposed model, the performance of the Hybrid swarm algorithm was significantly increased, as observed by optimizing the number of tasks through simulation, (power consumption was reduced by 42%). The simulation studies also showed a reduction in the number of required calculations by about 20% by the inclusion of the presented algorithms compared to the traditional static approach. There was also a decrease in the node loss which allowed the optimization algorithm to achieve a minimal overhead on cloud compute resources while still saving energy significantly. Conclusively, an energy-aware optimization model which describes the required system constraints was presented in this study, and a further proposal for techniques to determine the best overall solution was also made.

Datacenter ISO50001 and CoC

Didier Monestes

CS8078-Green Computing Question Bank

Gobinath Subramaniam

Green computing aims to reduce the environmental impact of computing through more efficient use of computing resources and design of environmentally friendly computing technologies. Virtualization allows for server consolidation which reduces energy consumption by increasing hardware utilization. A green data center uses energy efficient technologies and design to minimize its environmental footprint.

European Green IT Webinar 2014 - Kaliterre (France)

GreenLabCenter

KaliTerre is a French company founded in 2010 that provides eco-design training, consulting, and software called Greenspector to help companies detect resource-intensive patterns in source code. Greenspector analyzes code based on 250 eco-design rules, proposes practices to correct issues, and provides metrics on estimated resource savings. It is available both as a cloud-based SaaS and a plugin for the Eclipse IDE. Customers range from small startups to large enterprises seeking to extend hardware lifetime and reduce IT costs through more efficient software.

Presentation

Amar Dhillon

This document discusses using genetic algorithms for job scheduling in cloud computing environments. It begins with an introduction to cloud computing and genetic algorithms. It then discusses the challenges of genetic scheduling, including reducing makespan time, uniform load balancing, and minimizing user cost. It reviews various genetic algorithm approaches that have been proposed to address these challenges, such as approaches aimed at reducing makespan time alone, reducing cost alone, or reducing both cost and makespan time simultaneously. The document concludes that no single algorithm solves all problems, and that combining algorithms can better satisfy complex constraints in job scheduling.

Similar to jStanley: Placing a Green Thumb on Java Collections (20)

The Influence of the Java Collection Framework on Overall Energy Consumption

GreenDroid: A Tool for Analysing Power Consumption in the Android Ecosystem

Green Computing as an Engineering Discipline

Automation Enhanced Green Campus Initiative

Application of-computational-intelligence-techniques-for-economic-load-dispatch

Green it

Green Computing Seminar

An Improvement in Power Management in green Computing using Neural Networks

FINAL

Greencomputing

Technologies, Strategies And Algorithm In Green Computing – Solution To Energ...

Self-adaptation Approaches for Energy Efficiency

sustainable computing

5 6

GurminderBharani_Masters_Thesis

HSO: A Hybrid Swarm Optimization Algorithm for Reducing Energy Consumption in...

Datacenter ISO50001 and CoC

CS8078-Green Computing Question Bank

European Green IT Webinar 2014 - Kaliterre (France)

Presentation

More from Jácome Cunha

Spreadsheet Engineering

jStanley: Placing a Green Thumb on Java Collections

Recommended

Recommended

More Related Content

What's hot

What's hot (16)

Similar to jStanley: Placing a Green Thumb on Java Collections

Similar to jStanley: Placing a Green Thumb on Java Collections (20)

More from Jácome Cunha

More from Jácome Cunha (20)

Recently uploaded

Recently uploaded (20)

jStanley: Placing a Green Thumb on Java Collections

Editor's Notes