This document summarizes a study on optimizing virtual machine placement in data centers. It discusses the motivations of energy management, resource usage optimization, and traffic engineering. It then reviews several approaches to virtual machine placement optimization, including stochastic integer programming, genetic algorithms, bin packing, constraint programming, subgraph isomorphism algorithms, and ant colony optimization heuristics. It also discusses considerations for the different approaches and outlines ideas for future work, such as mapping resource managers to placement algorithms and developing an objective/approach matrix.
Interoperability is a key requirement for the IoT but what does it really mean? Standard protocols for different vendor's devices to interact with each other? Connection between different languages and operating systems? Wireless technology choice? A way for devices to interact with the cloud? Does it include data syntax? Must we model semantics? Can security interoperate? RTI, the world's largest embedded middleware company, participates in about 15 different "interoperability" efforts, including FACE (avionics), GVA (European vehicle architecture), SGIP (smart grid) and ICE (medical systems). We are leaders in the Industrial IoT and its leading consortium, the Industrial Internet Consortium (IIC). This session will examine the depth of the interoperability problem and explore solutions.
Presented by Stan Schneider, RTI CEO at IoTDevCon 2015
Presentation detailed about SDN (Software Defined Network) overview . It covers from basics like different controllers and touches upon some technical details.
Covers Terminologies used, OpenFlow, Controllers, Open Day light, Cisco ONE, Google B4, NFV,etc
For my thesis, I developed and compared a sequential CPU and parallel GPU implementation of a ray tracer written in C++ and CUDA respectively. Here are the presentation slides from my thesis defense.
Part of Lecture series on EE646, Fuzzy Theory & Applications delivered by me during First Semester of M.Tech. Instrumentation & Control, 2012
Z H College of Engg. & Technology, Aligarh Muslim University, Aligarh
Reference Books:
1. T. J. Ross, "Fuzzy Logic with Engineering Applications", 2/e, John Wiley & Sons,England, 2004.
2. Lee, K. H., "First Course on Fuzzy Theory & Applications", Springer-Verlag,Berlin, Heidelberg, 2005.
3. D. Driankov, H. Hellendoorn, M. Reinfrank, "An Introduction to Fuzzy Control", Narosa, 2012.
Please comment and feel free to ask anything related. Thanks!
Interoperability is a key requirement for the IoT but what does it really mean? Standard protocols for different vendor's devices to interact with each other? Connection between different languages and operating systems? Wireless technology choice? A way for devices to interact with the cloud? Does it include data syntax? Must we model semantics? Can security interoperate? RTI, the world's largest embedded middleware company, participates in about 15 different "interoperability" efforts, including FACE (avionics), GVA (European vehicle architecture), SGIP (smart grid) and ICE (medical systems). We are leaders in the Industrial IoT and its leading consortium, the Industrial Internet Consortium (IIC). This session will examine the depth of the interoperability problem and explore solutions.
Presented by Stan Schneider, RTI CEO at IoTDevCon 2015
Presentation detailed about SDN (Software Defined Network) overview . It covers from basics like different controllers and touches upon some technical details.
Covers Terminologies used, OpenFlow, Controllers, Open Day light, Cisco ONE, Google B4, NFV,etc
For my thesis, I developed and compared a sequential CPU and parallel GPU implementation of a ray tracer written in C++ and CUDA respectively. Here are the presentation slides from my thesis defense.
Part of Lecture series on EE646, Fuzzy Theory & Applications delivered by me during First Semester of M.Tech. Instrumentation & Control, 2012
Z H College of Engg. & Technology, Aligarh Muslim University, Aligarh
Reference Books:
1. T. J. Ross, "Fuzzy Logic with Engineering Applications", 2/e, John Wiley & Sons,England, 2004.
2. Lee, K. H., "First Course on Fuzzy Theory & Applications", Springer-Verlag,Berlin, Heidelberg, 2005.
3. D. Driankov, H. Hellendoorn, M. Reinfrank, "An Introduction to Fuzzy Control", Narosa, 2012.
Please comment and feel free to ask anything related. Thanks!
the fuzzy logic is very important for any automated or intelligent system.we are training the system with the help of fuzzy logic and fuzzy system so that it can behave and think like human beings. so, in this slide fuzzy inference system has been explained with some numerial problem.
Wireless Networked Control Systems (WNCSs) are spatially distributed systems in which sensors, actuators, and controllers connect through a wireless network instead of traditional point-to-point links. WNCSs have a tremendous potential to improve the efficiency of many large-scale distributed systems in industrial automation, building automation, automated highway, air transportation, and smart grid. Transmitting sensor measurements and control commands over wireless links provide many benefits such as the ease of installation and maintenance, low complexity and cost, and large flexibility to accommodate the modification and upgrade of the components in many control applications. Several industrial organizations, such as International Society of Automation (ISA), Highway Addressable Remote Transducer (HART), and Wireless In- dustrial Networking Alliance (WINA), have been actively pushing the application of wireless technologies in the control applications. Building a WNCS is very challenging since control systems often have stringent requirements on timing and reliability, which are difficult to attain by wireless sensor networks due to the adverse properties of the wireless communication and limited battery resources of the nodes. We provide a framework for the joint optimization of controller and communication systems encompassing efficient abstractions of both systems.
We provide platforms and frameworks for rapid development to unify Everything on the Internet for innovations to be created. Our customers has chosen our technology to be on the edge into the future of Internet. The future of Internet is not about a single technology or protocol, but to make them coexist.
Voice User Interface Design - Big Design 2017Crispin Reedy
Amazon Skills for Alexa, Google Actions for Home – Should your company build a conversational voice interface for one of these systems, and if so, how? What are the differences between a voice user interface and other types of UIs? What types of skills does a VUI designer need? What are some best practices for these VUIs? This session will explore all these questions and more. You’ll walk away with answers to the questions “If, Why, and How” you might choose to explore this interesting new area of design.
High Performance Computing Presentationomar altayyan
The Presentation Delivered on 3-6-2018 in the Data Mining Course, AI Specialization, at the Faculty of Information Technology Engineering Damascus University
Paper Link:
https://shamra.sy/academia/show/5b0c790de9fc6
Designing Swarms of Cyber-Physical Systems: The H2020 CPSwarm ProjectAlessandra Bagnato
CF 2017 - ACM International Conference on Computing Frontiers 2017
Alessandra Bagnato, Regina Krisztina Bíró, Dario Bonino, Claudio Pastrone, Wilfried Elmenreich, René Reiners, Melanie Schranz, Edin Arnautovic
Invite Paper
Cyber-Physical Systems (CPS) nd applications in a number of
large-scale, safety-critical domains e.g. transportation, smart cities,etc. As a matter of fact, the increasing interactions amongst dierent CPS are starting to generate unpredictable behaviors and emerging properties, often leading to unforeseen and/or undesired results.
Rather than being an unwanted byproduct, these interactions could, however, become an advantage if they were explicitly managed, and accounted, since the early design stages. The CPSwarm project, presented in this paper, aims at tackling these kinds of challenges by easing development and integration of complex herds of heterogeneous CPS. Thanks to CPSwarm, systems designed through a combination of existing and emerging tools, will collaborate on the basis of local policies and exhibit a collective behavior capable of solving complex, real-world, problems. Three real-world use cases
will demonstrate the validity of foundational assumptions of the
presented approach as well as the viability of the developed tools and methodologies.
Modelling a CPS Swarm System: A Simple Case Study
The CPSwarm workbench is a toolchain that facilitates the entire design process of swarms of CPS including
modelling, design, optimization, simulation and deployment. This paper highlights part of the work of the
CPSwarm workbench in the context of the CPSwarm H2020 project. In particular, the CPSwarm workbench
allows to create a generic swarm library that can be customized by developers to design new swarm environments,
new swarm members and new swarm goals. This paper shows an application of the initial CPSwarm
workbench by the example of a reference problem called EmergencyExit. In this example a swarm of robots
needs to find an exit in an unmapped environment and leave this room through the exit as soon as possible.
The example problem is further used to show the integration of Modelio, a UML/SysML modelling tool, and
FREVO, an optimization tool in the CPSwarm workbench
the fuzzy logic is very important for any automated or intelligent system.we are training the system with the help of fuzzy logic and fuzzy system so that it can behave and think like human beings. so, in this slide fuzzy inference system has been explained with some numerial problem.
Wireless Networked Control Systems (WNCSs) are spatially distributed systems in which sensors, actuators, and controllers connect through a wireless network instead of traditional point-to-point links. WNCSs have a tremendous potential to improve the efficiency of many large-scale distributed systems in industrial automation, building automation, automated highway, air transportation, and smart grid. Transmitting sensor measurements and control commands over wireless links provide many benefits such as the ease of installation and maintenance, low complexity and cost, and large flexibility to accommodate the modification and upgrade of the components in many control applications. Several industrial organizations, such as International Society of Automation (ISA), Highway Addressable Remote Transducer (HART), and Wireless In- dustrial Networking Alliance (WINA), have been actively pushing the application of wireless technologies in the control applications. Building a WNCS is very challenging since control systems often have stringent requirements on timing and reliability, which are difficult to attain by wireless sensor networks due to the adverse properties of the wireless communication and limited battery resources of the nodes. We provide a framework for the joint optimization of controller and communication systems encompassing efficient abstractions of both systems.
We provide platforms and frameworks for rapid development to unify Everything on the Internet for innovations to be created. Our customers has chosen our technology to be on the edge into the future of Internet. The future of Internet is not about a single technology or protocol, but to make them coexist.
Voice User Interface Design - Big Design 2017Crispin Reedy
Amazon Skills for Alexa, Google Actions for Home – Should your company build a conversational voice interface for one of these systems, and if so, how? What are the differences between a voice user interface and other types of UIs? What types of skills does a VUI designer need? What are some best practices for these VUIs? This session will explore all these questions and more. You’ll walk away with answers to the questions “If, Why, and How” you might choose to explore this interesting new area of design.
High Performance Computing Presentationomar altayyan
The Presentation Delivered on 3-6-2018 in the Data Mining Course, AI Specialization, at the Faculty of Information Technology Engineering Damascus University
Paper Link:
https://shamra.sy/academia/show/5b0c790de9fc6
Designing Swarms of Cyber-Physical Systems: The H2020 CPSwarm ProjectAlessandra Bagnato
CF 2017 - ACM International Conference on Computing Frontiers 2017
Alessandra Bagnato, Regina Krisztina Bíró, Dario Bonino, Claudio Pastrone, Wilfried Elmenreich, René Reiners, Melanie Schranz, Edin Arnautovic
Invite Paper
Cyber-Physical Systems (CPS) nd applications in a number of
large-scale, safety-critical domains e.g. transportation, smart cities,etc. As a matter of fact, the increasing interactions amongst dierent CPS are starting to generate unpredictable behaviors and emerging properties, often leading to unforeseen and/or undesired results.
Rather than being an unwanted byproduct, these interactions could, however, become an advantage if they were explicitly managed, and accounted, since the early design stages. The CPSwarm project, presented in this paper, aims at tackling these kinds of challenges by easing development and integration of complex herds of heterogeneous CPS. Thanks to CPSwarm, systems designed through a combination of existing and emerging tools, will collaborate on the basis of local policies and exhibit a collective behavior capable of solving complex, real-world, problems. Three real-world use cases
will demonstrate the validity of foundational assumptions of the
presented approach as well as the viability of the developed tools and methodologies.
Modelling a CPS Swarm System: A Simple Case Study
The CPSwarm workbench is a toolchain that facilitates the entire design process of swarms of CPS including
modelling, design, optimization, simulation and deployment. This paper highlights part of the work of the
CPSwarm workbench in the context of the CPSwarm H2020 project. In particular, the CPSwarm workbench
allows to create a generic swarm library that can be customized by developers to design new swarm environments,
new swarm members and new swarm goals. This paper shows an application of the initial CPSwarm
workbench by the example of a reference problem called EmergencyExit. In this example a swarm of robots
needs to find an exit in an unmapped environment and leave this room through the exit as soon as possible.
The example problem is further used to show the integration of Modelio, a UML/SysML modelling tool, and
FREVO, an optimization tool in the CPSwarm workbench
Keynote address: Latest developments in Time Sensitive Networking (TSN), PROFINET in Process applications, Advanced Physical Layer (APL), OPC-UA and Industry 4.0
BDE-BDVA Webinar: BigDataEurope Overview & Synergies with BDVABigData_Europe
Short outline of the project's mission and current status & summary of the identified synergies between BDVA and the project, included those at a technical level.
In this video from the HPC User Forum in Santa Fe, Leonardo Flores from the European Commission presents: EuroHPC - The EU Strategy in HPC.
"EuroHPC is a joint collaboration between European countries and the European Union about developing and supporting exascale supercomputing by 2022/2023. EuroHPC will permit the EU and participating countries to coordinate their efforts and share resources with the objective of deploying in Europe a world-class supercomputing infrastructure and a competitive innovation ecosystem in supercomputing technologies, applications and skills."
Watch the video: https://wp.me/p3RLHQ-k45
Learn more: https://eurohpc-ju.europa.eu/
and
http://hpcuserforum.com
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
Towards Deep Programmable Slicing. IEEE Netsoft'19 Distinguished Expert Panel Theme: Barriers and Frontiers of Softwarization for the Network of 2030, Paris, 2019. https://netsoft2019.ieee-netsoft.org/program/distinguished-expert-panel/
Many HPC applications are massively parallel and can benefit from the spatial parallelism offered by reconfigurable logic. While modern memory technologies can offer high bandwidth, designers must craft advanced communication and memory architectures for efficient data movement and on-chip storage. Addressing these challenges requires to combine compiler optimizations, high-level synthesis, and hardware design.
In this talk, I will present challenges, solutions, and trends for generating massively parallel accelerators on FPGA for high-performance computing. These architectures can provide performance comparable to software implementations on high-end processors, and much higher energy efficiency thanks to logic customization.
FITCE Congress 2017, Madrid - Raf Meersman (CEO, Comsof)Comsof
Automated and Optimised FTTx Planning
How increased availability of GIS data and PC Calculation power allows to save millions of Euro's in an FTTx network Rollout .
Presentation from FITCE Congress 2017 in Madrid
More info: http://www.fiberplanit.com
Google Cloud Platform (GCP) is one of the leaders among cloud APIs. It has gained notable expansion due to its suite of public cloud services that it based on a huge, solid infrastructure. GCP allows developers to use these services by accessing GCP RESTful API that is described through HTML pages on its website. However, the documentation of GCP API is written in natural language (English prose) and therefore shows several drawbacks, such as Informal Heterogeneous Documentation, Imprecise Types, Implicit Attribute Metadata, Hidden Links, Redundancy and Lack of Visual Support. To avoid confusion and misunderstandings, the cloud developers obviously need a precise specification of the knowledge and activities in GCP. Therefore, this paper introduces GCP MODEL, an inferred formal model-driven specification of GCP which describes without ambiguity the resources offered by GCP.
With the advent of cloud computing, different cloud providers with heterogeneous cloud services and Application Programming Interfaces (APIs) have emerged. This heterogeneity complicates the implementation of an interoperable multi-cloud system. Among the multi-cloud interoperability solutions, Model-Driven Engineering (MDE) has proven to be quite advantageous and is the mostly adopted methodology to rise in abstraction and mask the heterogeneity of the cloud. However, most of the existing MDE solutions for the cloud are not representative of the cloud APIs and lack of formalization. To address these shortcomings, I present in this thesis an approach based on Open Cloud Computing Interface (OCCI) standard, MDE, and formal methods. I provide two major contributions implemented in the context of the OCCIware project. First, I propose an approach based on reverse-engineering to extract knowledge from the ambiguous textual documentation of cloud APIs and to enhance its representation using MDE techniques. This approach is applied to Google Cloud Platform (GCP), where I provide GCP Model, a precise model-driven specification for GCP that is automatically inferred from GCP textual documentation. Second, I propose the fclouds framework to achieve semantic interoperability in multi-clouds, i.e., to identify the common concepts between cloud APIs and to reason over them. The fclouds language is a formalization of OCCI concepts and operational semantics in Alloy formal specification language. To demonstrate the effectiveness of the fclouds language, I formally specify thirteen case studies and verify their properties.
MoDMaCAO: Model-Driven Configuration Management of Cloud Applications with OC...Stéphanie Challita
To tackle the cloud-provider lock-in, the Open Grid Forum (OGF) is developing the Open Cloud Computing Interface (OCCI), a standardized interface for managing any kind of cloud resources. Besides the OCCI Core model, which defines the basic modeling elements for cloud resources, the OGF also defines extensions that reflect the requirements of different cloud service levels, such as IaaS and PaaS. However, so far the OCCI PaaS extension is very coarse grained and lacks of supporting use cases and implementations. Especially, it does not define how the components of the application itself can be managed. In this paper, we present a model-driven framework that extends the OCCI PaaS extension and is able to use different configuration management tools to manage the whole lifecycle of cloud applications. We demonstrate the feasibility of the approach by presenting four different use cases and prototypical implementations for three different configuration management tools.
Specifying Semantic Interoperability between Heterogeneous Cloud Resources wi...Stéphanie Challita
With the advent of cloud computing, different cloud providers with heterogeneous services and Application Programming Interfaces (APIs) have emerged. Hence, building an interoperable multi-cloud system becomes a complex task. Our idea is to design fclouds framework to achieve semantic interoperability in multi-clouds, i.e., to identify the common concepts between cloud APIs and to reason over them. In this paper, we propose to take advantage of the Open Cloud Computing Interface (OCCI) standard and the Alloy formal specification language to define the fclouds language, which is a formal language for specifying heterogeneous cloud APIs. To do so, we formalize OCCI concepts and operational semantics, then we identify and validate five properties (consistency, sequentiality, reversibility, idempotence and safety) that denote their characteristics. To demonstrate the effectiveness of our cloud formal language, we present thirteen case studies where we formally specify infrastructure, platform, Internet of Things (IoT) and transverse cloud concerns. Thanks to the Alloy analyzer, we verify that these heterogeneous APIs uphold the properties of fclouds and also validate their own specific properties. Then, thanks to formal transformation rules and equivalence properties, we draw a precise alignment between our case studies, which promotes semantic interoperability in a multi-cloud system.
Today, Google Cloud Platform (GCP) is one of the leaders among cloud APIs. Although it was established only five years ago, GCP has gained notable expansion due to its suite of public cloud services that it based on a huge, solid infrastructure. GCP allows developers to use these services by accessing GCP RESTful API that is described through HTML pages on its website. However, the documentation of GCP API is written in natural language (English prose) and therefore shows several drawbacks, such as Informal Heterogeneous Documentation, Imprecise Types, Implicit Attribute Metadata, Hidden Links, Redundancy and Lack of Visual Support. To avoid confusion and misunderstandings, the cloud developers obviously need a precise specification of the knowledge and activities in GCP. Therefore, this paper introduces GCP MODEL, an inferred formal model-driven specification of GCP which describes without ambiguity the resources offered by GCP. GCP MODEL conforms to the Open Cloud Computing Interface (OCCI) metamodel and is implemented based on the open source model-driven Eclipse-based OCCIWARE tool chain. Thanks to our GCP MODEL, we offer corrections to the drawbacks we identified.
Towards Formal-based Semantic Interoperability in Multi-Clouds (CLOUD'2017)Stéphanie Challita
Multi-cloud computing has been proposed as a way to reduce vendor lock-in, to improve resiliency during outages and geo-presence, to boost performance and to lower costs.
However, semantic differences between cloud providers, as well as their heterogeneous management interfaces, make changing from one provider to another very complex and costly. This is quite challenging for the implementation of multi-cloud systems. In this paper, we aim to take advantage of formal methods to define a precise semantics for multi-clouds. We propose FCLOUDS, a formal-based framework for semantic interoperability in multi-clouds. This framework contains a catalogue of formal models that mathematically describe cloud APIs and reason over them. A precise alignment can be described between their concepts, which promotes semantic interoperability.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
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A Study of Virtual Machine Placement Optimization in Data Centers (CLOSER'2017)
1. Stéphanie Challita | Fawaz Paraiso | Philippe Merle
Inria Lille – Nord Europe & University of Lille (France)
7th International Conference on Cloud Computing and Services Science
(CLOSER 2017)
A Study of Virtual Machine
Placement Optimization in
Data Centers
2. VMVMVM
VM
24 – 26 April, 2017 Porto, Portugal2/19
Virtualization
“Virtualization is the key concept of Cloud Computing”
How to select the most suitable host for each virtual
machine?
Hypervisor Hypervisor Hypervisor
4. 24 – 26 April, 2017 Porto, Portugal4/19
Motivation 1
Energy Management
Minimizes the cost of powering at the hardware level
Server consolidation
Green Data Centers are a must to fight against the
huge power consumption and bills caused by
inappropriate virtualization
5. 24 – 26 April, 2017 Porto, Portugal5/19
Motivation 2
Resource Usage Optimization
Resources should be:
Available to applications only as needed
Not allocated statically based on the peak workload
demand
This is known by the “Elasticity of the Cloud”
6. 24 – 26 April, 2017 Porto, Portugal6/19
Motivation 3
Traffic Engineering
To maintain data center applications efficiency
accurate planning of the network architecture
VL2N-Tree (source: (Fang et al., 2013))BCube (source: (Wang et al., 2014))
Fat-tree (source: (Fang et al., 2013))
VL2 (source: (Fang et al., 2013))
7. 24 – 26 April, 2017 Porto, Portugal7/19
Approaches
Stochastic
Integer
Programming
Genetic
Algorithm
Bin Packing
Constraint
Programming
Subgraph
isomorphism
algorithms
Greedy
heuristics
ACO
heuristics
Easily extendable
take additional constraints into account
Relatively long search times
8. 24 – 26 April, 2017 Porto, Portugal
Approaches
Stochastic
Integer
Programming
Genetic
Algorithm
Bin Packing
Constraint
Programming
Subgraph
isomorphism
algorithms
Greedy
heuristics
ACO
heuristics
The number of PMs used is reduced to the half
This approach might put two interfering VMs on one PM
8/19
10. 24 – 26 April, 2017 Porto, Portugal10/19
Approaches
2. Bin Packing
Ant Colony Optimization (ACO) heuristics:
Source: upload.wikimedia.org/wikipedia/commons/thumb/a/af/Aco_branches.svg/2000px-Aco_branches.svg.png
Ant System (AS)
Ant Colony System (ACS)
Min-Max Ant System (MMAS)
11. 24 – 26 April, 2017 Porto, Portugal11/19
Approaches
2. Bin Packing
Subgraph isomorphism algorithms:
f(S1) = VM1
f(S2) = VM2
f(S3) = VM3
f(S4) = VM4
f(S5) = VM5
VM1
VM4 VM3
VM2 VM5
VM graph
S1
S5
S4
S2
S3
Server graph
An isomorphism between
Servers and VMs
12. 24 – 26 April, 2017 Porto, Portugal12/19
Approaches
Stochastic
Integer
Programming
Genetic
Algorithm
Bin Packing
Constraint
Programming
Subgraph
isomorphism
algorithms
Greedy
heuristics
ACO
heuristics
Helpful in estimating the variation in demands and prices
frequent recomputations are not needed
Users might end up paying more if there is an estimation
error
13. 24 – 26 April, 2017 Porto, Portugal13/19
Approaches
Stochastic
Integer
Programming
Genetic
Algorithm
Bin Packing
Constraint
Programming
Subgraph
isomorphism
algorithms
Greedy
heuristics
ACO
heuristics
It solves the VM interference problem encountered in the
Bin Packing approach
It requires more computing time and higher computing
resources
14. 24 – 26 April, 2017 Porto, Portugal14/19
Approaches
Stochastic
Integer
Programming
Genetic
Algorithm
Bin Packing
Constraint
Programming
Subgraph
isomorphism
algorithms
Greedy
heuristics
ACO
heuristics
Population of
server capacities
Determine the
fitness of each server
Select next
generation
Perform reproduction
using crossover
Perform
mutation
Display results
Desired condition reached
Else
15. 24 – 26 April, 2017 Porto, Portugal15/19
Discussion
Constraint
Programming
Bin Packing Stochastic
Integer
Programming
Genetic
Algorithm
We know the
demands of VMs
we compute the
cost functions
The demand is
highly variable
Physical
machines have the
same amount of
memory and
processing
capabilities
We have
uncertain
parameters on
which the cost
depends
We need to
operate on groups
Objective
functions
dynamically change
17. 24 – 26 April, 2017 Porto, Portugal17/19
Discussion
Metrics for Future Empirical Studies
SLA violation percentage
Energy amount
Number of VM migrations
100%
100%
100%
Each VM placement algorithm works well under
specific conditions/objectives
Comparative analysis becomes quite tricky
18. 24 – 26 April, 2017 Porto, Portugal18/19
Future Work
Map between Resource Managers and Placement Algorithms
PA1
PA2
PA3
PA4
OpenStack
Vmotion
Containers add new efficiency to
Cloud Computing
VM
MESOS
Kubernetes
VM
Hypervisor
VM
19. 24 – 26 April, 2017 Porto, Portugal19/19
Future Work
Objective /Approach Matrix
Energy Resources Traffic
Constraint
Programming
Bin Packing
Stochastic Integer
Programming
Genetic Algorithm
Greedy heuristics
Subgraph Isomorphism
algorithms
ACO heuristics
What about an hybrid solution?
Hello everyone, I’m Stéphanie Challita, a PhD student in University of Lille, France and also a member of Inria research team.
I’m here to present for you my paper “A Study of VM Placement Optimization in Data Centers”.
In cloud computing domain, since provisioning Virtual Machines (VMs) is fundamental to provide infrastructure services, one can say that virtualization is the key concept of cloud computing.
However, VMs need to be adequately placed to fulfill performance goals, to optimize network flows, and to reduce CPU, storage and energy
costs. These are the motivations behind this work that I will detail in next slides.
So How to select the most suitable host for each virtual machine?
In order to answer this question, I present a survey of various approaches studying VM placement, highlighting their key concepts, as well as the state-of-the-art implementations.
As I said, the motivation behind VM placement optimization can be energy-aware, resource-aware, traffic-aware, or a combination of these.
First, Enhancing energy efficiency in data centers can be resolved by applying a suitable VM placement algorithm that minimizes the cost of powering at the hardware level.
Moreover, turning off unused machines, on the basis of server consolidation and energy-aware job scheduling, can also constitute a solution for the energy problem.
In this context, “Green Data Centers” are nowadays a must to fight against huge power consumption and bills caused by inappropriate virtualization.
Secondly, In order to maintain the application performance, isolation and security, each VM requires a certain amount of resources, such as CPU, memory and link bandwidth, etc.
In order to minimize their cost, these resources should be made available to applications only as needed and not allocated statically based on
the peak workload demand.
This is known as the “elasticity of the cloud”.
Third, measuring and optimizing data center traffic is important to maintain the efficiency of applications.
For information, a data center, which hosts thousands of devices like servers, switches and routers, needs an accurate planning of the network architecture.
One can distinguish several architectures such as Fat-tree, VL2 and BCube.
VM placement may depend of these architectures.
As shown in this Figure, our classification of VM placement algorithms is based on four main approaches:
1) Constraint Programming,
2) Bin Packing,
3) Stochastic Integer Programming,
and 4) Genetic Algorithm.
We start by detailing the first approach which is Constraint Programming.
Since this approach can always consider additional constraints, it can always be expandable.
However, in cases where we have several constraints to take into consideration, this approach may take too much time to find the most suitable VM placement.
Therefore, the main challenge consists in finding the optimal solution before any modification in terms of the constraint parameters.
The Bin Packing problem is an NP-hard problem that can be solved using Greedy heuristics, Ant Colony Optimization (ACO) heuristics or Subgraph isomorphism algorithms
Bin Packing can reduce to the half the number of PMs.
In order to do so, this approach may host two interfering VMs on one PM.
For solving the bin packing problem, we distinguish several greedy heuristics, such as FF.
FF places each VM into “the first bin in which it will fit”.
For example, we consider we have 3 VMS with different RAM capacities and 2 servers with 2GB RAM capacity each.
Since the first and the second VM can fit in the first server, we place them there. However, the third VM will be placed on the second server for lack of resources on the first.
FF is very quick but is not likely to lead to an optimal solution.
It is more efficient when first sorting the list of elements into a decreasing order. This is the First-Fit Decreasing.
The second heuristic for the bin packing problem is ACO, which is a probabilistic technique that can be reduced to finding good paths through graphs.
It is inspired from the collective behaviour of social insects.
When searching for food, ants tend to choose paths marked by strong pheromone concentrations.
So as soon as an ant finds a food source, it studies the quantity and the quality of the food and takes some of it back to the nest.
During the return trip, the quantity of pheromones that an ant leaves on the ground may depend on the quantity and quality of the food.
The pheromone trails will guide other ants to the food source
And enables them to find the shortest paths between their nest and food sources
This behaviour, aiming for the shortest paths, can be used for the VM migration optimization between PMs.
Some extensions of ACO algorithms are presented in the literature such as Ant System (AS), Ant Colony System (ACS) and MAX-MIN Ant System (MMAS)
The third heuristic for solving the Bin Packing Problem is the subgraph isomorphism algorithms where two graphs are given as input, and one must determine whether the first graph contains a subgraph that is isomorphic to the second graph.
Recently, several algorithms have used subgraph isomorphism to formulate the problem of VM placement, i.e., to model data center topologies and VM clusters.
The two graphs shown below are isomorphic, despite their different looking drawings.
They the same number of nodes connected in the same way.
In graph theory, we can talk about bijection between the node sets of Server Graph and VM Graph
Stochastic Integer Programming is helpful in estimating the variation in demands and costs.
Thereby, frequent recomputations are not needed, but if there is an error in the estimation, unfortunately users might end up paying more.
Last but not least, we have Genetic Algorithm.
It considers additional constraints while optimizing the cost function, so it solves the VM interference problem encountered in the Bin Packing approach
But it requires more computing time and higher computing resources as compared to Bin Packing
This activity diagram explains the genetic algorithm. We start by choosing the population of server
For a better understanding of the four approaches, we provide this table that explains the optimal case for using each of these approaches.
An objective function is a function to maximize or minimize.
Similarity with the fitness in GA.
This figure summarizes the classification of 17 methods stated in this work.
In the paper, we have identified for each method the approach to which it belongs, the objective, network architecture if this information exists, evaluation type (simulation experiments, simulation in real environments, experiments with real workload…), as well as the competitor approaches.
We can state that Bin Packing is lately the most employed approach. It always generates a good solution in a correct amount of time.
It is crucial to choose a VM placement technique that suits the needs of both the cloud user and cloud provider.
However, due to the presence of several parameters, comparative analysis in a uniform fashion of such techniques becomes quite tricky.
In fact, each of the VM placement algorithm works well under certain specific conditions/objectives.
Thereby, in order to compare the efficiency of the previous algorithms, we propose that future empirical studies will be based on the three following metrics to measure and evaluate the algorithms performance.
Firstly, one should take into account the energy amount consumed by data center resources, due to the application workloads.
The second metric to be considered is the SLA violation percentage, which expresses the level by which performance requirements defined between the resource provider and consumers are violated.
(The SLA violation can happen when VMs sharing the same PM need a CPU performance that cannot be provided because of energy-aware resource management and consolidation. The provider pays a penalty to the client in case of SLA violation.)
The third metric is the number of VM migrations during the adaptation of the VM placement.
VM migrations consume time, energy and network bandwidth. Thus, it is important to minimize the number of VM migrations.
Many future directions and perspectives have not been explored yet and can be contemplated for the future.
For example, nowadays there are several resource managers that are mostly doing the placement of VMs, like vMotion, the commercial product of
VMware and OpenStack, the open-source cloud manager.
Other resource managers like Kubernetes, Swarm, and Mesos to cite a few, are responsible for the placement of containers.
Therefore, it will be interesting to conduct an exhaustive study of the existing resource managers, and to map between them and the placement algorithm(s) they use.
Finally, none of the identified approaches cover the three detailed motivations.
Designing an hybrid solution combining several approaches represents a future challenge.
I would like to mention that this work is supported by the French project OCCIware that aims at managing any kind of cloud resources by using the OCCI standard.
Thank you for your attention.
I will be happy to answer your questions.
The virtual machine cluster makes use of virtual machines as nodes. The main motive behind a virtual machine cluster is to install multiple functionalities on the same server. This works by enhancing the server utilization.
Virtual machine clusters work by protecting the physical machine from any hardware and software failures. When a physical node fails, the virtual machine can access another node, with no time lag. And thus, virtual machine clustering provides a dynamic backup processes. It is therefore widely used in organizations where data is of great value, all thanks to its easy disaster recovery capabilities.