This document describes a study on the energy consumption of databases and cloud patterns. It aims to evaluate the impact of three databases (MySQL, PostgreSQL, MongoDB) and three cloud patterns (Local Database Proxy, Local Sharding-Based Router, Priority Message Queue) on the energy consumption and response time of cloud applications. The methodology section outlines the experimental setup, including the applications tested, databases used, cloud patterns implemented, and tools for measuring energy consumption and response time. Preliminary results are presented comparing the energy use and performance of the databases without any cloud patterns, and then with each of the three cloud patterns.
This is the presentation for his admission to the third year of his Ph.D.. It talks about the several direction his work had taken and look forward to the conclusion of some task in form of code release and published papers.
Presentation from the New Mexico Regional Energy Storage & Grid Integration Workshop: Optimized Integration of PV with Battery Storage: A Real World Success Story, presented by Jon Hawkins, PNM
Carles Bo, d'ICIQ, presenta IoChem-BD, un repositori de dades en química computacional. L'objectiu és elaborar una base de dades de forma normalitzada, definint processos, què es guarda i com es fa.
Aquesta presentació ha tingut lloc a la TSIUC'14, celebrada a la Universitat Autònoma de Barcelona el passat 2 de desembre de 2014, sota el títol "Reptes en Big Data a la universitat i la Recerca".
Dr. Fariba Fahroo presents an overview of her program, Computational Mathematics, at the AFOSR 2013 Spring Review. At this review, Program Officers from AFOSR Technical Divisions will present briefings that highlight basic research programs beneficial to the Air Force.
e-Research & the art of linking Astrophysics to DeforestationDavid Wallom
Keynote at HPCS 2016 on e-Research, talking about the e-Research methodology linking work on Astrophysics with finally Deforestation via Smartening Energy Systems and Detecting Energy Theft
This is the presentation for his admission to the third year of his Ph.D.. It talks about the several direction his work had taken and look forward to the conclusion of some task in form of code release and published papers.
Presentation from the New Mexico Regional Energy Storage & Grid Integration Workshop: Optimized Integration of PV with Battery Storage: A Real World Success Story, presented by Jon Hawkins, PNM
Carles Bo, d'ICIQ, presenta IoChem-BD, un repositori de dades en química computacional. L'objectiu és elaborar una base de dades de forma normalitzada, definint processos, què es guarda i com es fa.
Aquesta presentació ha tingut lloc a la TSIUC'14, celebrada a la Universitat Autònoma de Barcelona el passat 2 de desembre de 2014, sota el títol "Reptes en Big Data a la universitat i la Recerca".
Dr. Fariba Fahroo presents an overview of her program, Computational Mathematics, at the AFOSR 2013 Spring Review. At this review, Program Officers from AFOSR Technical Divisions will present briefings that highlight basic research programs beneficial to the Air Force.
e-Research & the art of linking Astrophysics to DeforestationDavid Wallom
Keynote at HPCS 2016 on e-Research, talking about the e-Research methodology linking work on Astrophysics with finally Deforestation via Smartening Energy Systems and Detecting Energy Theft
This is a presentation by Prof. Anne Elster at the International Workshop on Open Source Supercomputing held in conjunction with the 2017 ISC High Performance Computing Conference.
An update of the activities of the ProteomeXchange Consortium of proteomics resources given at HUPO 2016 (Taipei). Some slides at the end of the presentation are from Nuno Bandeira.
Selecting Ontologies and Publishing Data of Electrical Appliances: A Refrige...Anna Fensel
Application scenarios for the data generated from the Internet of Things are on the rise. For example, given the appliances’ energy consumption data, energy measurement tools now make it possible to save energy whilst efficiently controlling the consumption of different household devices. Yet, when the precise structured data describing appliance models is missing, it is difficult for such application scenarios to be realized. The developed OpenFridge ontology defines a basic vocabulary for the domain of measuring a refrigerator’s energy consumption, showing that the needed ontology schemata are already in place, but need to be identified and skillfully applied. Further, the ontology has been populated from the Web using data scraping, and the created dataset semantically describing the specifics of 1032 refrigerator models with 18665 triples, make these valuable assets for the development of further applications.
Show and Tell - Data and Digitalisation, Digital Twins.pdfSIFOfgem
This is the third in a series of 'Show and Tell' webinars from the Ofgem Strategic Innovation Fund Discovery phase, covering the Digital Twin projects.
As the move towards a net zero energy system accelerates, network customers and consumers will require simplified and accessible digital products, processes and services that can improve their user experience. Data and digital initiatives are already beginning to show the potential to improve the efficiency of energy networks whilst making it easier for third parties to interact with and innovate for the energy system. Digitalisation of energy network activities will contribute to better coordination, planning and network optimisation.
You will hear from SIF projects which are investigating new digital products and services such as digital twins.
The Strategic Innovation Fund (SIF) is an Ofgem programme managed in partnership with Innovate UK, part of UKRI. The SIF aims to fund network innovation that will contribute to achieving Net Zero rapidly and at lowest cost to consumers, and help transform the UK into the ‘Silicon Valley’ of energy, making it the best place for high-potential businesses to grow and scale in the energy market.
For more information on the SIF visit: www.ofgem.gov.uk/sif
Or sign-up for our newsletter here: https://ukri.innovateuk.org/ofgem-sif-subscription-sign-up
An innovative approach to design cogeneration systems based on big data analy...Giulio Vialetto
In recent years, collecting energy consumption data is becoming easier and easier thanks to decreasing of cost of smart sensors. Moreover, capacity of analysis data using big data methods like machine learning and artificial intelligence is increasing. Such methods are expected to be useful to increase efficiency of energy systems.
In this paper an innovative approach to design cogeneration systems based on big data analysis is developed. More specifically, a study on how cluster analysis could be applied to analyse energy consumption data is depicted. The aim of the method is to design cogeneration systems that suit more efficiently energy demand profiles, choosing the correct type of cogeneration technology, operation strategy and, if they are necessary, energy storages. In the first part of the paper, the methodology based on clustering to perform the analysis of the dataset is described. In the second part, a case study with cogenerators (a wood industry that requires low temperature heat to dry wood into steam-powered kilns) is analysed. An alternative cogeneration system is designed and proposed. Thermodynamics benchmarks are defined to evaluate differences between as-is and alternative scenarios.
Results show that the proposed innovative method allows to choose a more suitable cogeneration technology compared to the adopted one, giving suggestions on the operation strategy in order to decrease energy losses and, consequently, primary energy consumption.
Context-oriented Knowledge Management in Production Networks @Gsom Emerging m...CaaS EU FP7 Project
Context-oriented Knowledge Management in Production Networks
By Kurt Sandkuhl
Invited lecture on October 8 at the GSOM Emerging Markets conference in St. Petersburg
Querying and reasoning over large scale building datasets: an outline of a pe...Ana Roxin
Presented at the International Workshop on Semantic Big Data (SBD 2016), held in conjunction with the 2016 ACM SIGMOD Conference
July 1st, 2016, San Francisco, USA
October 24, 2014: Joseph DeCarolis, Assistant Professor at North Carolina State University, will present The Importance of Open Data and Models for Energy Systems Analysis.
Energy system models represent a critical planning tool that can be used to deliver policy-relevant insights at scales ranging from local to global. When such models are used to inform public policy, the associated data and source code should be open in order to enable third party replication of results, expose hidden assumptions, and identify key model sensitivities. In this talk, I describe my own effort to push open data and models within the international energy modeling community.
Big Data HPC Convergence and a bunch of other thingsGeoffrey Fox
This talk supports the Ph.D. in Computational & Data Enabled Science & Engineering at Jackson State University. It describes related educational activities at Indiana University, the Big Data phenomena, jobs and HPC and Big Data computations. It then describes how HPC and Big Data can be converged into a single theme.
This is a presentation by Prof. Anne Elster at the International Workshop on Open Source Supercomputing held in conjunction with the 2017 ISC High Performance Computing Conference.
An update of the activities of the ProteomeXchange Consortium of proteomics resources given at HUPO 2016 (Taipei). Some slides at the end of the presentation are from Nuno Bandeira.
Selecting Ontologies and Publishing Data of Electrical Appliances: A Refrige...Anna Fensel
Application scenarios for the data generated from the Internet of Things are on the rise. For example, given the appliances’ energy consumption data, energy measurement tools now make it possible to save energy whilst efficiently controlling the consumption of different household devices. Yet, when the precise structured data describing appliance models is missing, it is difficult for such application scenarios to be realized. The developed OpenFridge ontology defines a basic vocabulary for the domain of measuring a refrigerator’s energy consumption, showing that the needed ontology schemata are already in place, but need to be identified and skillfully applied. Further, the ontology has been populated from the Web using data scraping, and the created dataset semantically describing the specifics of 1032 refrigerator models with 18665 triples, make these valuable assets for the development of further applications.
Show and Tell - Data and Digitalisation, Digital Twins.pdfSIFOfgem
This is the third in a series of 'Show and Tell' webinars from the Ofgem Strategic Innovation Fund Discovery phase, covering the Digital Twin projects.
As the move towards a net zero energy system accelerates, network customers and consumers will require simplified and accessible digital products, processes and services that can improve their user experience. Data and digital initiatives are already beginning to show the potential to improve the efficiency of energy networks whilst making it easier for third parties to interact with and innovate for the energy system. Digitalisation of energy network activities will contribute to better coordination, planning and network optimisation.
You will hear from SIF projects which are investigating new digital products and services such as digital twins.
The Strategic Innovation Fund (SIF) is an Ofgem programme managed in partnership with Innovate UK, part of UKRI. The SIF aims to fund network innovation that will contribute to achieving Net Zero rapidly and at lowest cost to consumers, and help transform the UK into the ‘Silicon Valley’ of energy, making it the best place for high-potential businesses to grow and scale in the energy market.
For more information on the SIF visit: www.ofgem.gov.uk/sif
Or sign-up for our newsletter here: https://ukri.innovateuk.org/ofgem-sif-subscription-sign-up
An innovative approach to design cogeneration systems based on big data analy...Giulio Vialetto
In recent years, collecting energy consumption data is becoming easier and easier thanks to decreasing of cost of smart sensors. Moreover, capacity of analysis data using big data methods like machine learning and artificial intelligence is increasing. Such methods are expected to be useful to increase efficiency of energy systems.
In this paper an innovative approach to design cogeneration systems based on big data analysis is developed. More specifically, a study on how cluster analysis could be applied to analyse energy consumption data is depicted. The aim of the method is to design cogeneration systems that suit more efficiently energy demand profiles, choosing the correct type of cogeneration technology, operation strategy and, if they are necessary, energy storages. In the first part of the paper, the methodology based on clustering to perform the analysis of the dataset is described. In the second part, a case study with cogenerators (a wood industry that requires low temperature heat to dry wood into steam-powered kilns) is analysed. An alternative cogeneration system is designed and proposed. Thermodynamics benchmarks are defined to evaluate differences between as-is and alternative scenarios.
Results show that the proposed innovative method allows to choose a more suitable cogeneration technology compared to the adopted one, giving suggestions on the operation strategy in order to decrease energy losses and, consequently, primary energy consumption.
Context-oriented Knowledge Management in Production Networks @Gsom Emerging m...CaaS EU FP7 Project
Context-oriented Knowledge Management in Production Networks
By Kurt Sandkuhl
Invited lecture on October 8 at the GSOM Emerging Markets conference in St. Petersburg
Querying and reasoning over large scale building datasets: an outline of a pe...Ana Roxin
Presented at the International Workshop on Semantic Big Data (SBD 2016), held in conjunction with the 2016 ACM SIGMOD Conference
July 1st, 2016, San Francisco, USA
October 24, 2014: Joseph DeCarolis, Assistant Professor at North Carolina State University, will present The Importance of Open Data and Models for Energy Systems Analysis.
Energy system models represent a critical planning tool that can be used to deliver policy-relevant insights at scales ranging from local to global. When such models are used to inform public policy, the associated data and source code should be open in order to enable third party replication of results, expose hidden assumptions, and identify key model sensitivities. In this talk, I describe my own effort to push open data and models within the international energy modeling community.
Big Data HPC Convergence and a bunch of other thingsGeoffrey Fox
This talk supports the Ph.D. in Computational & Data Enabled Science & Engineering at Jackson State University. It describes related educational activities at Indiana University, the Big Data phenomena, jobs and HPC and Big Data computations. It then describes how HPC and Big Data can be converged into a single theme.
OpenMetadata Community Meeting - 5th June 2024OpenMetadata
The OpenMetadata Community Meeting was held on June 5th, 2024. In this meeting, we discussed about the data quality capabilities that are integrated with the Incident Manager, providing a complete solution to handle your data observability needs. Watch the end-to-end demo of the data quality features.
* How to run your own data quality framework
* What is the performance impact of running data quality frameworks
* How to run the test cases in your own ETL pipelines
* How the Incident Manager is integrated
* Get notified with alerts when test cases fail
Watch the meeting recording here - https://www.youtube.com/watch?v=UbNOje0kf6E
Utilocate offers a comprehensive solution for locate ticket management by automating and streamlining the entire process. By integrating with Geospatial Information Systems (GIS), it provides accurate mapping and visualization of utility locations, enhancing decision-making and reducing the risk of errors. The system's advanced data analytics tools help identify trends, predict potential issues, and optimize resource allocation, making the locate ticket management process smarter and more efficient. Additionally, automated ticket management ensures consistency and reduces human error, while real-time notifications keep all relevant personnel informed and ready to respond promptly.
The system's ability to streamline workflows and automate ticket routing significantly reduces the time taken to process each ticket, making the process faster and more efficient. Mobile access allows field technicians to update ticket information on the go, ensuring that the latest information is always available and accelerating the locate process. Overall, Utilocate not only enhances the efficiency and accuracy of locate ticket management but also improves safety by minimizing the risk of utility damage through precise and timely locates.
Essentials of Automations: The Art of Triggers and Actions in FMESafe Software
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
AI Pilot Review: The World’s First Virtual Assistant Marketing SuiteGoogle
AI Pilot Review: The World’s First Virtual Assistant Marketing Suite
👉👉 Click Here To Get More Info 👇👇
https://sumonreview.com/ai-pilot-review/
AI Pilot Review: Key Features
✅Deploy AI expert bots in Any Niche With Just A Click
✅With one keyword, generate complete funnels, websites, landing pages, and more.
✅More than 85 AI features are included in the AI pilot.
✅No setup or configuration; use your voice (like Siri) to do whatever you want.
✅You Can Use AI Pilot To Create your version of AI Pilot And Charge People For It…
✅ZERO Manual Work With AI Pilot. Never write, Design, Or Code Again.
✅ZERO Limits On Features Or Usages
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✅No Complicated Setup: Get Up And Running In 2 Minutes
✅99.99% Up-Time Guaranteed
✅30 Days Money-Back Guarantee
✅ZERO Upfront Cost
See My Other Reviews Article:
(1) TubeTrivia AI Review: https://sumonreview.com/tubetrivia-ai-review
(2) SocioWave Review: https://sumonreview.com/sociowave-review
(3) AI Partner & Profit Review: https://sumonreview.com/ai-partner-profit-review
(4) AI Ebook Suite Review: https://sumonreview.com/ai-ebook-suite-review
Mobile App Development Company In Noida | Drona InfotechDrona Infotech
Looking for a reliable mobile app development company in Noida? Look no further than Drona Infotech. We specialize in creating customized apps for your business needs.
Visit Us For : https://www.dronainfotech.com/mobile-application-development/
Launch Your Streaming Platforms in MinutesRoshan Dwivedi
The claim of launching a streaming platform in minutes might be a bit of an exaggeration, but there are services that can significantly streamline the process. Here's a breakdown:
Pros of Speedy Streaming Platform Launch Services:
No coding required: These services often use drag-and-drop interfaces or pre-built templates, eliminating the need for programming knowledge.
Faster setup: Compared to building from scratch, these platforms can get you up and running much quicker.
All-in-one solutions: Many services offer features like content management systems (CMS), video players, and monetization tools, reducing the need for multiple integrations.
Things to Consider:
Limited customization: These platforms may offer less flexibility in design and functionality compared to custom-built solutions.
Scalability: As your audience grows, you might need to upgrade to a more robust platform or encounter limitations with the "quick launch" option.
Features: Carefully evaluate which features are included and if they meet your specific needs (e.g., live streaming, subscription options).
Examples of Services for Launching Streaming Platforms:
Muvi [muvi com]
Uscreen [usencreen tv]
Alternatives to Consider:
Existing Streaming platforms: Platforms like YouTube or Twitch might be suitable for basic streaming needs, though monetization options might be limited.
Custom Development: While more time-consuming, custom development offers the most control and flexibility for your platform.
Overall, launching a streaming platform in minutes might not be entirely realistic, but these services can significantly speed up the process compared to building from scratch. Carefully consider your needs and budget when choosing the best option for you.
May Marketo Masterclass, London MUG May 22 2024.pdfAdele Miller
Can't make Adobe Summit in Vegas? No sweat because the EMEA Marketo Engage Champions are coming to London to share their Summit sessions, insights and more!
This is a MUG with a twist you don't want to miss.
A Study of Variable-Role-based Feature Enrichment in Neural Models of CodeAftab Hussain
Understanding variable roles in code has been found to be helpful by students
in learning programming -- could variable roles help deep neural models in
performing coding tasks? We do an exploratory study.
- These are slides of the talk given at InteNSE'23: The 1st International Workshop on Interpretability and Robustness in Neural Software Engineering, co-located with the 45th International Conference on Software Engineering, ICSE 2023, Melbourne Australia
Graspan: A Big Data System for Big Code AnalysisAftab Hussain
We built a disk-based parallel graph system, Graspan, that uses a novel edge-pair centric computation model to compute dynamic transitive closures on very large program graphs.
We implement context-sensitive pointer/alias and dataflow analyses on Graspan. An evaluation of these analyses on large codebases such as Linux shows that their Graspan implementations scale to millions of lines of code and are much simpler than their original implementations.
These analyses were used to augment the existing checkers; these augmented checkers found 132 new NULL pointer bugs and 1308 unnecessary NULL tests in Linux 4.4.0-rc5, PostgreSQL 8.3.9, and Apache httpd 2.2.18.
- Accepted in ASPLOS ‘17, Xi’an, China.
- Featured in the tutorial, Systemized Program Analyses: A Big Data Perspective on Static Analysis Scalability, ASPLOS ‘17.
- Invited for presentation at SoCal PLS ‘16.
- Invited for poster presentation at PLDI SRC ‘16.
Need for Speed: Removing speed bumps from your Symfony projects ⚡️Łukasz Chruściel
No one wants their application to drag like a car stuck in the slow lane! Yet it’s all too common to encounter bumpy, pothole-filled solutions that slow the speed of any application. Symfony apps are not an exception.
In this talk, I will take you for a spin around the performance racetrack. We’ll explore common pitfalls - those hidden potholes on your application that can cause unexpected slowdowns. Learn how to spot these performance bumps early, and more importantly, how to navigate around them to keep your application running at top speed.
We will focus in particular on tuning your engine at the application level, making the right adjustments to ensure that your system responds like a well-oiled, high-performance race car.
E-commerce Application Development Company.pdfHornet Dynamics
Your business can reach new heights with our assistance as we design solutions that are specifically appropriate for your goals and vision. Our eCommerce application solutions can digitally coordinate all retail operations processes to meet the demands of the marketplace while maintaining business continuity.
AI Fusion Buddy Review: Brand New, Groundbreaking Gemini-Powered AI AppGoogle
AI Fusion Buddy Review: Brand New, Groundbreaking Gemini-Powered AI App
👉👉 Click Here To Get More Info 👇👇
https://sumonreview.com/ai-fusion-buddy-review
AI Fusion Buddy Review: Key Features
✅Create Stunning AI App Suite Fully Powered By Google's Latest AI technology, Gemini
✅Use Gemini to Build high-converting Converting Sales Video Scripts, ad copies, Trending Articles, blogs, etc.100% unique!
✅Create Ultra-HD graphics with a single keyword or phrase that commands 10x eyeballs!
✅Fully automated AI articles bulk generation!
✅Auto-post or schedule stunning AI content across all your accounts at once—WordPress, Facebook, LinkedIn, Blogger, and more.
✅With one keyword or URL, generate complete websites, landing pages, and more…
✅Automatically create & sell AI content, graphics, websites, landing pages, & all that gets you paid non-stop 24*7.
✅Pre-built High-Converting 100+ website Templates and 2000+ graphic templates logos, banners, and thumbnail images in Trending Niches.
✅Say goodbye to wasting time logging into multiple Chat GPT & AI Apps once & for all!
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See My Other Reviews Article:
(1) AI Genie Review: https://sumonreview.com/ai-genie-review
(2) SocioWave Review: https://sumonreview.com/sociowave-review
(3) AI Partner & Profit Review: https://sumonreview.com/ai-partner-profit-review
(4) AI Ebook Suite Review: https://sumonreview.com/ai-ebook-suite-review
#AIFusionBuddyReview,
#AIFusionBuddyFeatures,
#AIFusionBuddyPricing,
#AIFusionBuddyProsandCons,
#AIFusionBuddyTutorial,
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#HowDoesAIFusionBuddyWorks
Quarkus Hidden and Forbidden ExtensionsMax Andersen
Quarkus has a vast extension ecosystem and is known for its subsonic and subatomic feature set. Some of these features are not as well known, and some extensions are less talked about, but that does not make them less interesting - quite the opposite.
Come join this talk to see some tips and tricks for using Quarkus and some of the lesser known features, extensions and development techniques.
Introducing Crescat - Event Management Software for Venues, Festivals and Eve...Crescat
Crescat is industry-trusted event management software, built by event professionals for event professionals. Founded in 2017, we have three key products tailored for the live event industry.
Crescat Event for concert promoters and event agencies. Crescat Venue for music venues, conference centers, wedding venues, concert halls and more. And Crescat Festival for festivals, conferences and complex events.
With a wide range of popular features such as event scheduling, shift management, volunteer and crew coordination, artist booking and much more, Crescat is designed for customisation and ease-of-use.
Over 125,000 events have been planned in Crescat and with hundreds of customers of all shapes and sizes, from boutique event agencies through to international concert promoters, Crescat is rigged for success. What's more, we highly value feedback from our users and we are constantly improving our software with updates, new features and improvements.
If you plan events, run a venue or produce festivals and you're looking for ways to make your life easier, then we have a solution for you. Try our software for free or schedule a no-obligation demo with one of our product specialists today at crescat.io
Introducing Crescat - Event Management Software for Venues, Festivals and Eve...
Icsoc16b.ppt
1. A Study of the Energy Consumption of
Databases and Cloud Patterns
Naouel Moha,
B´echir Bani, Foutse Khomh and Yann-Ga¨el Gu´eh´eneuc
´Ecole Polytechnique de Montr´eal
Ptidej Team / SWAT Lab
2. POLYTECHNIQUE MONTR´EAL Introduction Literature Review Methodology Results Conclusion
Table of contents
1 Introduction
2 Literature Review
3 Methodology
4 Results
5 Conclusion
14th ICSOC 2016 – Naouel Moha, B´echir Bani, Foutse Khomh and Yann-Ga¨el Gu´eh´eneuc 2/31 – www.polymtl.ca
5. POLYTECHNIQUE MONTR´EAL Introduction Literature Review Methodology Results Conclusion
Motivations
• Little is still known about the energy footprint of these
applications and, in particular, of their databases
• Databases are the backbone of cloud-based applications
14th ICSOC 2016 – Naouel Moha, B´echir Bani, Foutse Khomh and Yann-Ga¨el Gu´eh´eneuc 5/31 – www.polymtl.ca
7. POLYTECHNIQUE MONTR´EAL Introduction Literature Review Methodology Results Conclusion
Motivations
• None of previous works investigated the combined impact of
databases and cloud patterns on the energy consumption of
cloud-based applications
• The benefits and trade-offs of different databases and
combinations of cloud patterns are mostly intuitive and not
validated
14th ICSOC 2016 – Naouel Moha, B´echir Bani, Foutse Khomh and Yann-Ga¨el Gu´eh´eneuc 7/31 – www.polymtl.ca
8. POLYTECHNIQUE MONTR´EAL Introduction Literature Review Methodology Results Conclusion
Objectives
1 Propose an approach to collect energy measures of
cloud-based applications implemented with cloud patterns in
conjunction with databases in a cloud environment
2 Evaluate the impact on energy consumption of three cloud
patterns with three databases
3 Highlight the contrast response time with energy efficiency of
databases
14th ICSOC 2016 – Naouel Moha, B´echir Bani, Foutse Khomh and Yann-Ga¨el Gu´eh´eneuc 8/31 – www.polymtl.ca
9. POLYTECHNIQUE MONTR´EAL Introduction Literature Review Methodology Results Conclusion
Energy Consumption and Applications Design
1 How Green Are Cloud Patterns? (Abtahizadeh et al.)
• Showed that cloud patterns can effectively reduce the energy
consumption of a cloud application
• Only considered MySQL database and a RESTful application
2 Investigating the impacts of web servers on web
application energy usage (Manotas et al.)
• Showed that the energy consumption of a Web application
depends on the Web server used to handle requests
14th ICSOC 2016 – Naouel Moha, B´echir Bani, Foutse Khomh and Yann-Ga¨el Gu´eh´eneuc 9/31 – www.polymtl.ca
10. POLYTECHNIQUE MONTR´EAL Introduction Literature Review Methodology Results Conclusion
Performance of Relational and NoSQL Databases
1 Comparison of NoSQL and SQL Databases in the Cloud
(Hammes et al.)
• Highlighted the performance of both PostgreSQL database and
MongoDB database
• Observed that PostgreSQL databases perform better than
MongoDB databases in cloud environments
14th ICSOC 2016 – Naouel Moha, B´echir Bani, Foutse Khomh and Yann-Ga¨el Gu´eh´eneuc 10/31 – www.polymtl.ca
11. POLYTECHNIQUE MONTR´EAL Introduction Literature Review Methodology Results Conclusion
Impact of Cloud Patterns on Applications Performance
1 An empirical Study of the impact of cloud patterns on
Quality of Service (QoS) (Hecht et al.)
• Studied the impact of three cloud patterns on QoS
• Reported that the implementation of the Local Database
Proxy pattern can significantly impact the QoS
14th ICSOC 2016 – Naouel Moha, B´echir Bani, Foutse Khomh and Yann-Ga¨el Gu´eh´eneuc 11/31 – www.polymtl.ca
12. POLYTECHNIQUE MONTR´EAL Introduction Literature Review Methodology Results Conclusion
Objects and Design
1 RESTful multi-threaded application
• Use Sakila sample database (provided by MySQL)
• Adapt the schema of Sakila sample database to PostgreSQL
and MongoDB databases
2 DVD Store application
• Open source simulation of an e-commerce web site
• We refactor the code of DVD Store to allow it to connect to a
MongoDB database
3 JPetStore application
• Open source simulation of an e-commerce web application
• We refactor the code of JPetStore to allow it to connect to a
PostgreSQL and MongoDB databases
14th ICSOC 2016 – Naouel Moha, B´echir Bani, Foutse Khomh and Yann-Ga¨el Gu´eh´eneuc 12/31 – www.polymtl.ca
13. POLYTECHNIQUE MONTR´EAL Introduction Literature Review Methodology Results Conclusion
Objects and Design
Power-API Tool
• Provides power information (in watts converted to joules to
measure the energy) per PID for each system component
(CPU, memory, etc.)
• Uses sensors and analytical models for its energy estimation
• Allows to estimate the amount of power required by the CPU
to execute a process (at the corresponding PID)
14th ICSOC 2016 – Naouel Moha, B´echir Bani, Foutse Khomh and Yann-Ga¨el Gu´eh´eneuc 13/31 – www.polymtl.ca
14. POLYTECHNIQUE MONTR´EAL Introduction Literature Review Methodology Results Conclusion
Research Variables
1 Independent Variables
• Databases: MySQL, PostgreSQL, MongoDB
• Cloud Patterns: Local Database Proxy, Local Sharding-Based
Router, Priority Message Queue
2 Dependent Variables
• Response Time: Corresponding to Select and Insert requests
(milliseconds)
• Energy Consumption: Using Power-API profiler (Joules)
14th ICSOC 2016 – Naouel Moha, B´echir Bani, Foutse Khomh and Yann-Ga¨el Gu´eh´eneuc 14/31 – www.polymtl.ca
15. POLYTECHNIQUE MONTR´EAL Introduction Literature Review Methodology Results Conclusion
Data Extraction Process
Energy Consumption Data Extraction Process
14th ICSOC 2016 – Naouel Moha, B´echir Bani, Foutse Khomh and Yann-Ga¨el Gu´eh´eneuc 15/31 – www.polymtl.ca
16. POLYTECHNIQUE MONTR´EAL Introduction Literature Review Methodology Results Conclusion
Analysis Method
• Mann-Whitney U test
• A non-parametric statistical test whose relevance is reflected in
the assessment of two independent distributions
• The null hypothesis is rejected (there is a significant difference
between the the two distributions) when its p-value < 0.05
• Cliff’s δ effect size
• Represents the degree of interlock between two sample
distributions
• Its value ranges from -1 to +1: negligeable (δ < 0.147),
small (0.147< δ <0.33), medium (0.33< δ <0.474) and
large (δ > 0.474)
14th ICSOC 2016 – Naouel Moha, B´echir Bani, Foutse Khomh and Yann-Ga¨el Gu´eh´eneuc 16/31 – www.polymtl.ca
17. POLYTECHNIQUE MONTR´EAL Introduction Literature Review Methodology Results Conclusion
RQ1:
Does the choice of MySQL, PostgreSQL, and
MongoDB databases affect the energy consumption
of cloud applications (when no cloud patterns are
implemented)?
14th ICSOC 2016 – Naouel Moha, B´echir Bani, Foutse Khomh and Yann-Ga¨el Gu´eh´eneuc 17/31 – www.polymtl.ca
18. POLYTECHNIQUE MONTR´EAL Introduction Literature Review Methodology Results Conclusion
Without Applying Cloud Patterns
Energy Consumption p-value and Cliff’s δ
Pattern MySQL PostgreSQL p-value Cliff’s δ MySQL MongoDB p-value Cliff’s δ PostgreSQL MongoDB p-value Cliff’s δ
P0 262.5 568.2 0.01 medium 262.5 354.7 0.24 small 568.2 354.7 0.09 small
Response Time p-value and Cliff’s δ
Pattern MySQL PostgreSQL p-value Cliff’s δ MySQL MongoDB p-value Cliff’s δ PostgreSQL MongoDB p-value Cliff’s δ
P0 36018.6 28615.7 0.09 small 36018.6 4253.8 < 10e−6 large 28615.7 4253.8 < 10e−6 large
14th ICSOC 2016 – Naouel Moha, B´echir Bani, Foutse Khomh and Yann-Ga¨el Gu´eh´eneuc 18/31 – www.polymtl.ca
19. POLYTECHNIQUE MONTR´EAL Introduction Literature Review Methodology Results Conclusion
RQ2:
Does the implementation of Local Database Proxy,
Local Sharding Based Router, and Priority Message
Queue patterns affect the energy consumption of
cloud applications using MySQL, PostgreSQL, and
MongoDB Databases?
14th ICSOC 2016 – Naouel Moha, B´echir Bani, Foutse Khomh and Yann-Ga¨el Gu´eh´eneuc 19/31 – www.polymtl.ca
20. POLYTECHNIQUE MONTR´EAL Introduction Literature Review Methodology Results Conclusion
Local Database Proxy Pattern
Energy Consumption p-value and Cliff’s δ
Pattern MySQL PostgreSQL p-value Cliff’s δ MySQL MongoDB p-value Cliff’s δ PostgreSQL MongoDB p-value Cliff’s δ
P1 490.2 1391.1 < 10e−6 large 490.2 890.0 < 10e−6 large 1391.1 890.0 0.09 small
P2 495.2 1529.9 < 10e−6 large 495.2 915.9 < 10e−6 large 1529.9 915.9 0.04 medium
P3 495.0 1476.5 < 10e−6 large 495.0 904.5 < 10e−6 large 1476.5 904.5 0.04 medium
Response Time p-value and Cliff’s δ
Pattern MySQL PostgreSQL p-value Cliff’s δ MySQL MongoDB p-value Cliff’s δ PostgreSQL MongoDB p-value Cliff’s δ
P1 30430.0 27867.8 0.23 small 30430.0 3639.8 < 10e−6 large 27867.8 3639.8 < 10e−6 large
P2 29504.1 27036.5 0.23 small 29504.1 3214.2 < 10e−6 large 27036.5 3214.2 < 10e−6 large
P3 29825.2 26129.6 0.23 small 29825.2 3275.0 < 10e−6 large 26129.6 3275.0 < 10e−6 large
14th ICSOC 2016 – Naouel Moha, B´echir Bani, Foutse Khomh and Yann-Ga¨el Gu´eh´eneuc 20/31 – www.polymtl.ca
21. POLYTECHNIQUE MONTR´EAL Introduction Literature Review Methodology Results Conclusion
Local Sharding-Based Router
Energy Consumption p-value and Cliff’s δ
Pattern MySQL PostgreSQL p-value Cliff’s δ MySQL MongoDB p-value Cliff’s δ PostgreSQL MongoDB p-value Cliff’s δ
P4 1331.9 6330.2 < 10e−6 large 1331.9 5826.4 < 10e−6 large 6330.2 5826.4 0.23 small
P5 611.6 4245.1 < 10e−6 large 611.6 3821.8 < 10e−6 large 4245.1 3821.8 0.23 small
P6 824.1 4929.4 < 10e−6 large 824.1 4194.4 < 10e−6 large 4929.4 4194.4 0.23 small
Response Time p-value and Cliff’s δ
Pattern MySQL PostgreSQL p-value Cliff’s δ MySQL MongoDB p-value Cliff’s δ PostgreSQL MongoDB p-value Cliff’s δ
P4 170693.1 138026.6 0.09 small 170693.1 26259.5 < 10e−6 large 138026.6 26259.5 < 10e−6 large
P5 165250.7 145382.6 0.09 small 165250.7 27897.8 < 10e−6 large 145382.6 27897.8 < 10e−6 large
P6 168786.5 130585.0 0.09 small 168786.5 24680.3 < 10e−6 large 130585.0 24680.3 < 10e−6 large
14th ICSOC 2016 – Naouel Moha, B´echir Bani, Foutse Khomh and Yann-Ga¨el Gu´eh´eneuc 21/31 – www.polymtl.ca
22. POLYTECHNIQUE MONTR´EAL Introduction Literature Review Methodology Results Conclusion
RQ3:
Do the interactions between Local Database Proxy,
Local Sharding Based Router, and Priority Message
Queue patterns affect the energy consumption of
cloud applications using MySQL, PostgreSQL, and
MongoDB databases?
14th ICSOC 2016 – Naouel Moha, B´echir Bani, Foutse Khomh and Yann-Ga¨el Gu´eh´eneuc 22/31 – www.polymtl.ca
23. POLYTECHNIQUE MONTR´EAL Introduction Literature Review Methodology Results Conclusion
Combination Proxy Pattern and Message Queue Pattern
Energy Consumption p-value and Cliff’s δ
Pattern MySQL PostgreSQL p-value Cliff’s δ MySQL MongoDB p-value Cliff’s δ PostgreSQL MongoDB p-value Cliff’s δ
P1+P7 442.7 1379.8 < 10e−6 large 442.7 814.3 < 10e−6 large 1379.8 814.3 0.03 medium
P2+P7 468.8 1482.5 < 10e−6 large 468.8 891.9 < 10e−6 large 1482.5 891.9 0.03 medium
P3+P7 490.2 1391.1 < 10e−6 large 490.2 890.0 < 10e−6 large 1391.1 890.0 0.09 small
Response Time p-value and Cliff’s δ
Pattern MySQL PostgreSQL p-value Cliff’s δ MySQL MongoDB p-value Cliff’s δ PostgreSQL MongoDB p-value Cliff’s δ
P1+P7 27826.2 22299.8 0.48 negligible 27826.2 3747.1 < 10e−6 large 22299.8 3747.1 < 10e−6 large
P2+P7 26703.4 25706.8 0.48 negligible 26703.4 3127.5 < 10e−6 large 25706.8 3127.5 < 10e−6 large
P3+P7 29339.7 23153.6 0.23 small 29339.7 4210.2 < 10e−6 large 23153.6 4210.2 < 10e−6 large
14th ICSOC 2016 – Naouel Moha, B´echir Bani, Foutse Khomh and Yann-Ga¨el Gu´eh´eneuc 23/31 – www.polymtl.ca
24. POLYTECHNIQUE MONTR´EAL Introduction Literature Review Methodology Results Conclusion
Combination Sharding and Message Queue Patterns
Energy Consumption p-value and Cliff’s δ
Pattern MySQL PostgreSQL p-value Cliff’s δ MySQL MongoDB p-value Cliff’s δ PostgreSQL MongoDB p-value Cliff’s δ
P4+P7 1255.5 5777.4 < 10e−6 large 1255.5 5622.9 < 10e−6 large 5777.4 5622.9 0.82 negligible
P5+P7 492.2 3884.5 < 10e−6 large 492.2 3386.6 < 10e−6 large 3884.5 3386.6 0.23 small
P6+P7 775.9 4526.8 < 10e−6 large 775.9 4127.4 < 10e−6 large 4526.8 4127.4 0.23 small
Response Time p-value and Cliff’s δ
Pattern MySQL PostgreSQL p-value Cliff’s δ MySQL MongoDB p-value Cliff’s δ PostgreSQL MongoDB p-value Cliff’s δ
P4+P7 37584.7 29287.7 0.23 small 37584.7 2716.3 < 10e−6 large 29287.7 2716.3 < 10e−6 large
P5+P7 38153.7 26445.6 0.09 small 38153.7 2869.7 < 10e−6 large 26445.6 2869.7 < 10e−6 large
P6+P7 34183.0 27507.3 0.23 small 34183.0 20609.3 0.03 medium 27507.3 20609.3 0.09 small
14th ICSOC 2016 – Naouel Moha, B´echir Bani, Foutse Khomh and Yann-Ga¨el Gu´eh´eneuc 24/31 – www.polymtl.ca
25. POLYTECHNIQUE MONTR´EAL Introduction Literature Review Methodology Results Conclusion
Important Results
• MySQL database is the least energy consuming but is the
slowest among the three databases
• PostgreSQL database is the most energy consuming among
the three databases, but is faster than MySQL but slower
than MongoDB
• MongoDB database consumes more energy than MySQL
but less than PostgreSQL and is the fastest among the three
databases
14th ICSOC 2016 – Naouel Moha, B´echir Bani, Foutse Khomh and Yann-Ga¨el Gu´eh´eneuc 25/31 – www.polymtl.ca
26. POLYTECHNIQUE MONTR´EAL Introduction Literature Review Methodology Results Conclusion
Discussions
• PostgreSQL database generates multiple parallel processes to
run the requests sent by the RESTful cloud-based application
• MySQL and MongoDB generate only one process at a time to
handle requests sent by the cloud-based application
14th ICSOC 2016 – Naouel Moha, B´echir Bani, Foutse Khomh and Yann-Ga¨el Gu´eh´eneuc 26/31 – www.polymtl.ca
27. POLYTECHNIQUE MONTR´EAL Introduction Literature Review Methodology Results Conclusion
Discussions
• MySQL and PostgreSQL follow the ACID model
• MongoDB database follow the BASE model
⇒ MongoDB is faster than the other two relational
databases
⇒ requests processed by relational databases must be
executed one by one and cannot be executed in a
Simultaneous way
14th ICSOC 2016 – Naouel Moha, B´echir Bani, Foutse Khomh and Yann-Ga¨el Gu´eh´eneuc 27/31 – www.polymtl.ca
28. POLYTECHNIQUE MONTR´EAL Introduction Literature Review Methodology Results Conclusion
Summary
• We carried on a series of experiments on different versions of
three cloud applications
• We contrasted the performance of various combinations of
databases and cloud patterns in terms of energy consumption
and response time of the cloud-based applications
• Databases can reduce the energy consumption of cloud-based
applications
• Cloud patterns do not impact the behavior of the databases
14th ICSOC 2016 – Naouel Moha, B´echir Bani, Foutse Khomh and Yann-Ga¨el Gu´eh´eneuc 28/31 – www.polymtl.ca
29. POLYTECHNIQUE MONTR´EAL Introduction Literature Review Methodology Results Conclusion
Limitations of the proposed approach
• Energy measurements are subject to perturbations depending
of hardware and network
• More studies should be conducted with possibly more accurate
tools to verify our findings
• Our findings may still be specific to our studied applications,
which were designed specifically for the experiments
14th ICSOC 2016 – Naouel Moha, B´echir Bani, Foutse Khomh and Yann-Ga¨el Gu´eh´eneuc 29/31 – www.polymtl.ca
30. POLYTECHNIQUE MONTR´EAL Introduction Literature Review Methodology Results Conclusion
Future work
• Expand our study to different NoSQL databases
• Examine how a match/mismatch between the selected
database and the workload characteristic affects energy
efficiency
14th ICSOC 2016 – Naouel Moha, B´echir Bani, Foutse Khomh and Yann-Ga¨el Gu´eh´eneuc 30/31 – www.polymtl.ca
31. POLYTECHNIQUE MONTR´EAL Introduction Literature Review Methodology Results Conclusion
• We carried on a series of experiments on different versions of
three cloud applications
• We contrasted the performance of various combinations of
databases and cloud patterns in terms of energy consumption
and response time of the cloud-based applications
• Databases can reduce the energy consumption of cloud-based
applications
• Cloud patterns do not impact the behavior of the databases
14th ICSOC 2016 – Naouel Moha, B´echir Bani, Foutse Khomh and Yann-Ga¨el Gu´eh´eneuc 31/31 – www.polymtl.ca