This document provides an overview of distributed systems, including definitions, important aspects, examples, characteristics, goals, architectures, and techniques for scaling distributed systems. A distributed system is defined as a collection of independent computers that appears as a single coherent system to users. Key goals of distributed systems are making resources accessible, hiding the distribution of resources from users, being open through standard interfaces, and being scalable to additional users and resources.
A distributed system is a collection of independent computers that appears to its users as a single coherent system. Key characteristics include no shared memory, each computer runs its own local OS, and heterogeneity. Distributed systems aim to present a single-system image to hide the underlying hardware complexity and provide transparency. Middleware plays an important role in enabling communication and resource sharing across networked computers in a distributed system.
Distributed computing involves a collection of independent computers that appear as a single coherent system to users. It allows for pooling of resources and increased reliability through replication. Key aspects of distributed systems include hiding the distribution from users, providing a consistent interface, scalability, and fault tolerance. Common examples are web search, online games, and financial trading systems. Distributed computing is used for tasks like high-performance computing through cluster and grid computing.
Distributed computing is a system where the components of a computer program are distributed and run across multiple computers that communicate over a network. It involves splitting tasks between participants to solve problems faster. Key advantages include reliability through redundancy, scalability by adding more systems, and faster computation through parallel processing. However, distributed systems also present challenges like more difficult troubleshooting, less software support, and higher costs for network infrastructure.
distributed system chapter one introduction to distribued system.pdflematadese670
distributed system chapter one introduction to distribued system
Your score increases as you pick a category, fill out a long description and add more tags distributed system chapter one introduction to distribued system distributed system chapter one introduction to distribued system distributed system chapter one introduction to distribued system
chapter 1- introduction to distributed system.pptAschalewAyele2
This document provides an introduction to distributed systems. It defines a distributed system as a collection of independent computers that appears as a single coherent system to users. The goals of distributed systems are discussed, including resource accessibility, distribution transparency, openness, and scalability. Various types of distributed systems are also outlined, such as distributed computing systems like clusters, grids and clouds, distributed information systems like transaction processing and enterprise application integration, and distributed embedded systems like home, healthcare and sensor networks. Key techniques for improving scalability like hiding communication delays, distribution, and replication are also summarized.
This document provides an overview of distributed computing. It discusses key concepts like distributed systems having computers with separate memories that communicate over a network. Distributed computing involves splitting a program into parts that run simultaneously on multiple computers. The document also covers the history of distributed computing, examples like grid and cloud computing, motivations like performance and fault tolerance, and challenges around complexity and security.
This document provides an overview of distributed computing. It discusses the history and introduction of distributed computing. It describes the working of distributed systems and common types like grid computing, cluster computing and cloud computing. It covers the motivations, goals, characteristics, architectures, security challenges and examples of distributed computing. Advantages include improved performance and fault tolerance, while disadvantages are security issues and lost messages.
The document provides an introduction to distributed systems, including definitions, goals, and characteristics. It discusses key problems in distributed systems like concurrency, security, and partial failures. Some techniques for achieving scalability are also covered, such as hiding communication latencies, offloading work to clients, distributing data and computations, and replicating/caching data across multiple machines. The overall goals of distributed systems are to share resources, provide distribution transparency, support openness, and achieve scalability.
A distributed system is a collection of independent computers that appears to its users as a single coherent system. Key characteristics include no shared memory, each computer runs its own local OS, and heterogeneity. Distributed systems aim to present a single-system image to hide the underlying hardware complexity and provide transparency. Middleware plays an important role in enabling communication and resource sharing across networked computers in a distributed system.
Distributed computing involves a collection of independent computers that appear as a single coherent system to users. It allows for pooling of resources and increased reliability through replication. Key aspects of distributed systems include hiding the distribution from users, providing a consistent interface, scalability, and fault tolerance. Common examples are web search, online games, and financial trading systems. Distributed computing is used for tasks like high-performance computing through cluster and grid computing.
Distributed computing is a system where the components of a computer program are distributed and run across multiple computers that communicate over a network. It involves splitting tasks between participants to solve problems faster. Key advantages include reliability through redundancy, scalability by adding more systems, and faster computation through parallel processing. However, distributed systems also present challenges like more difficult troubleshooting, less software support, and higher costs for network infrastructure.
distributed system chapter one introduction to distribued system.pdflematadese670
distributed system chapter one introduction to distribued system
Your score increases as you pick a category, fill out a long description and add more tags distributed system chapter one introduction to distribued system distributed system chapter one introduction to distribued system distributed system chapter one introduction to distribued system
chapter 1- introduction to distributed system.pptAschalewAyele2
This document provides an introduction to distributed systems. It defines a distributed system as a collection of independent computers that appears as a single coherent system to users. The goals of distributed systems are discussed, including resource accessibility, distribution transparency, openness, and scalability. Various types of distributed systems are also outlined, such as distributed computing systems like clusters, grids and clouds, distributed information systems like transaction processing and enterprise application integration, and distributed embedded systems like home, healthcare and sensor networks. Key techniques for improving scalability like hiding communication delays, distribution, and replication are also summarized.
This document provides an overview of distributed computing. It discusses key concepts like distributed systems having computers with separate memories that communicate over a network. Distributed computing involves splitting a program into parts that run simultaneously on multiple computers. The document also covers the history of distributed computing, examples like grid and cloud computing, motivations like performance and fault tolerance, and challenges around complexity and security.
This document provides an overview of distributed computing. It discusses the history and introduction of distributed computing. It describes the working of distributed systems and common types like grid computing, cluster computing and cloud computing. It covers the motivations, goals, characteristics, architectures, security challenges and examples of distributed computing. Advantages include improved performance and fault tolerance, while disadvantages are security issues and lost messages.
The document provides an introduction to distributed systems, including definitions, goals, and characteristics. It discusses key problems in distributed systems like concurrency, security, and partial failures. Some techniques for achieving scalability are also covered, such as hiding communication latencies, offloading work to clients, distributing data and computations, and replicating/caching data across multiple machines. The overall goals of distributed systems are to share resources, provide distribution transparency, support openness, and achieve scalability.
This document provides an overview of cloud computing and related topics such as distributed systems, cluster computing, and mobile computing. It defines cloud computing as a technology that allows for network-based computing over the Internet, providing hardware, software, and networking services to clients. Key aspects include on-demand services that are scalable and available anywhere via simple interfaces. The document contrasts cloud computing with cluster computing, noting that clusters have tightly coupled nodes within a local network, while clouds have loosely coupled nodes that can span wide geographic areas. Examples of cloud computing applications in areas like healthcare, engineering, education, and media are also provided.
A distributed system is a collection of independent computers that appears as a single coherent system to users. It provides advantages like cost-effectiveness, reliability, scalability, and flexibility but introduces challenges in achieving transparency, dependability, performance, and flexibility due to its distributed nature. A true distributed system that solves all these challenges perfectly is difficult to achieve due to limitations like network complexity and security issues.
Lect 2 Types of Distributed Systems.pptxPardonSamson
This document discusses different types of distributed systems including distributed computing systems and distributed information systems. Distributed computing systems are used for high-performance computing tasks and include cluster computing, where similar computers are connected by a network, and grid computing, where heterogeneous systems from different domains are connected. Distributed information systems allow data sharing across networked computers. The document also covers advantages and disadvantages as well as design issues of distributed systems such as transparency, reliability, performance, and security.
This document provides an overview of distributed systems. It defines a distributed system as a collection of independent computers that appears as a single coherent system to users. Key characteristics include hiding differences between computers and providing consistent, uniform interaction regardless of location or time. The main goals of distributed systems are making resources accessible, achieving distribution transparency, being open and scalable. Techniques for improving scalability include hiding communication latencies, distribution, and replication. Challenges include lack of global state information and handling slow/failed nodes.
introduction to cloud computing for college.pdfsnehan789
The document provides an overview of cloud computing by outlining its module which includes fundamental concepts of distributed systems, cluster computing, grid computing, cloud computing, and mobile computing. It then defines computing and distributed systems, explaining that a distributed system is a system with multiple components located on different machines that communicate and coordinate actions to appear as a single system. Key characteristics of distributed systems include presenting a single system image, expandability, continuous availability, and being supported by middleware.
Introduction to Cloud Computing
Cloud computing is a transformative technology that allows businesses and individuals to access computing resources over the internet. Instead of owning and maintaining physical hardware and software, users can leverage cloud services provided by companies like Amazon Web Services (AWS), Microsoft Azure, Google Cloud Platform (GCP), and others. This shift has revolutionized how we think about IT infrastructure, software development, data storage, and more.
Key Concepts of Cloud Computing
On-Demand Self-Service:
Users can provision computing resources as needed without human intervention from the service provider. This includes servers, storage, and applications.
Broad Network Access:
Cloud services are available over the network and accessed through standard mechanisms, enabling use from a variety of devices like laptops, smartphones, and tablets.
Resource Pooling:
Providers use a multi-tenant model to serve multiple customers with dynamically assigned resources. This model allows for economies of scale and efficient resource utilization.
Rapid Elasticity:
Resources can be elastically provisioned and released, sometimes automatically, to scale rapidly outward and inward commensurate with demand.
Measured Service:
Cloud systems automatically control and optimize resource use by leveraging a metering capability, allowing for pay-as-you-go pricing models.
Types of Cloud Computing Services
Infrastructure as a Service (IaaS):
Provides virtualized computing resources over the internet. Examples include AWS EC2, Google Compute Engine, and Azure Virtual Machines.
Platform as a Service (PaaS):
Offers hardware and software tools over the internet, typically used for application development. Examples include Google App Engine, AWS Elastic Beanstalk, and Azure App Services.
Software as a Service (SaaS):
Delivers software applications over the internet, on a subscription basis. Examples include Google Workspace, Microsoft Office 365, and Salesforce.
Deployment Models
Public Cloud:
Services are delivered over the public internet and shared across multiple organizations. It offers cost savings but might pose concerns regarding data security and privacy.
Private Cloud:
Dedicated to a single organization, offering enhanced security and control over data and infrastructure. It's more expensive than public cloud but can be tailored to specific business needs.
Hybrid Cloud:
Combines public and private clouds, allowing data and applications to be shared between them. This model offers greater flexibility and optimization of existing infrastructure, security, and compliance.
Community Cloud:
Shared between organizations with common concerns (e.g., security, compliance, jurisdiction). It can be managed internally or by a third-party.
Advantages of Cloud Computing
Cost Efficiency: Reduces the need for significant capital expenditure on hardware and software.
Scalability and Flexibility: Easily scales up or down based on
This document provides an overview of distributed systems. It discusses different types of distributed systems like client-server, n-tier, and peer-to-peer systems. It outlines advantages like performance, reliability, and scalability. Challenges include networking and security issues. Key features are discussed such as concurrency, lack of a global clock, and independent failures. Characteristics of distributed systems include heterogeneity, openness, security, scalability, failure handling, concurrency, and transparency. Examples provided are the Internet, intranets, and mobile/ubiquitous computing systems.
The document outlines the objectives and units of a course on distributed systems. The objectives are to learn about distributed environments, processes and synchronization, peer-to-peer networks, fault tolerance, network filesystems and middleware technologies. Unit 1 introduces distributed systems and covers resource sharing challenges, API protocols, data representation, marshaling, multicast communication and remote procedure calls.
This document discusses distributed operating systems. A distributed OS connects multiple computers via communication channels to distribute processing jobs between central processors. It consists of nodes joined by networks that enable sharing of computing resources and files while providing users with virtual machines. The document describes types of distributed OS like client-server and peer-to-peer, and features like openness, resource sharing, transparency, and flexibility. Examples of distributed OS mentioned are Solaris, OSF/1, Micros, and DYNIX.
01Introduction to Cloud Computing .pptxssuser586772
The document provides an introduction to cloud computing, including definitions and concepts. It discusses:
- What cloud computing is and the underlying principles of parallel and distributed computing systems.
- Key cloud characteristics like elasticity, scalability, and on-demand provisioning which allow resources to be provisioned as needed.
- Benefits of cloud computing like flexibility and cost savings but also challenges of complex management across cloud platforms.
The document discusses different types of computing including sequential, parallel, distributed, cluster, grid, and cloud computing. Sequential computing involves breaking a task into sequential steps executed on a single processor. Parallel computing breaks a task into independent sub-tasks that can be processed simultaneously on multiple processors. Distributed computing aggregates resources from geographically distributed systems to complete tasks. Cluster computing uses tightly coupled computers working as a single system. Grid computing virtualizes distributed resources to create a single system. Cloud computing provides on-demand access to configurable computing resources over the internet.
- Introduction - Distributed - System -ssuser7c150a
The document provides an introduction to distributed systems, including defining their key characteristics and challenges. It discusses how distributed systems allow independent computers to coordinate activities and share resources over a network. Examples of distributed systems include the internet, intranets, cloud computing systems, and wireless networks. The main goals of distributed systems are transparency, openness, and scalability, while the key challenges are heterogeneity, distribution transparency, fault tolerance, and security.
This document provides an introduction to distributed computing, including definitions, history, goals, characteristics, examples of applications, and scenarios. It discusses advantages like improved performance and reliability, as well as challenges like complexity, network problems, security, and heterogeneity. Key issues addressed are transparency, openness, scalability, and the need to handle differences across hardware, software, and developers when designing distributed systems.
UNIT 4 - UNDERSTANDING THE NETWORK ARCHITECTURE.pptxLeahRachael
This document provides an overview of network architecture and protocols. It discusses Ethernet, including its history, components, addressing, frames, and media access control. It also covers token ring, AppleTalk, ARCNET, the layered network protocol model (with HTTP as an example), and network operating system architectures like peer-to-peer and client-server. Segmentation strategies like firewalls and software-defined networking are introduced to improve performance and security.
1. The document discusses various models for distributed systems including physical, architectural, and fundamental models.
2. Physical models describe the hardware components and network connections. Architectural models capture the computational elements and communication tasks. Fundamental models take an abstract view of key aspects like interaction and failure.
3. Key architectural elements include communicating entities, communication paradigms, roles and responsibilities, and placement strategies. Common patterns are also described like layering, tiered architectures, and proxies.
Distributed computing utilizes a network of interconnected computers to accomplish tasks more quickly than a single computer. Each computer has its own memory and performs a portion of the overall task, communicating via message passing. Key benefits include scalability, redundancy, and sharing of resources. Common architectures include client-server, n-tier, and peer-to-peer networks. While complex, distributed systems can provide increased speed, reliability, and ability to incrementally expand processing power over centralized systems.
The document defines a distributed system and provides examples. It outlines the challenges in designing distributed systems, including heterogeneity, openness, security, scalability, failure handling, concurrency, and transparency. Distributed systems divide tasks across networked computers and aim to appear as a single computer to users.
1. Models can describe aspects of distributed systems in an abstract way, simplifying their complexity. Architectural models define how responsibilities are distributed among components, while interaction models deal with time handling.
2. Three architectural models were discussed: client-server, peer-to-peer, and variations including proxy servers, mobile code, agents, thin clients, and mobile devices.
3. Two interaction models - synchronous and asynchronous distributed systems - differ in whether bounds can be placed on timing.
4. Fault models specify what faults may occur and their effects, including omission, arbitrary, and timing faults impacting processes and communication.
This presentation introduces several types of distributed computing technologies including distributed computing, grid computing, cluster computing, utility computing, and cloud computing. It provides details on each type such as definitions, examples, and characteristics. Cloud computing allows for sharing of computing resources over the internet rather than using local servers. It can be public, private, hybrid or community-based and offers platform, software or infrastructure as services to users.
Infrastructure Challenges in Scaling RAG with Custom AI modelsZilliz
Building Retrieval-Augmented Generation (RAG) systems with open-source and custom AI models is a complex task. This talk explores the challenges in productionizing RAG systems, including retrieval performance, response synthesis, and evaluation. We’ll discuss how to leverage open-source models like text embeddings, language models, and custom fine-tuned models to enhance RAG performance. Additionally, we’ll cover how BentoML can help orchestrate and scale these AI components efficiently, ensuring seamless deployment and management of RAG systems in the cloud.
This document provides an overview of cloud computing and related topics such as distributed systems, cluster computing, and mobile computing. It defines cloud computing as a technology that allows for network-based computing over the Internet, providing hardware, software, and networking services to clients. Key aspects include on-demand services that are scalable and available anywhere via simple interfaces. The document contrasts cloud computing with cluster computing, noting that clusters have tightly coupled nodes within a local network, while clouds have loosely coupled nodes that can span wide geographic areas. Examples of cloud computing applications in areas like healthcare, engineering, education, and media are also provided.
A distributed system is a collection of independent computers that appears as a single coherent system to users. It provides advantages like cost-effectiveness, reliability, scalability, and flexibility but introduces challenges in achieving transparency, dependability, performance, and flexibility due to its distributed nature. A true distributed system that solves all these challenges perfectly is difficult to achieve due to limitations like network complexity and security issues.
Lect 2 Types of Distributed Systems.pptxPardonSamson
This document discusses different types of distributed systems including distributed computing systems and distributed information systems. Distributed computing systems are used for high-performance computing tasks and include cluster computing, where similar computers are connected by a network, and grid computing, where heterogeneous systems from different domains are connected. Distributed information systems allow data sharing across networked computers. The document also covers advantages and disadvantages as well as design issues of distributed systems such as transparency, reliability, performance, and security.
This document provides an overview of distributed systems. It defines a distributed system as a collection of independent computers that appears as a single coherent system to users. Key characteristics include hiding differences between computers and providing consistent, uniform interaction regardless of location or time. The main goals of distributed systems are making resources accessible, achieving distribution transparency, being open and scalable. Techniques for improving scalability include hiding communication latencies, distribution, and replication. Challenges include lack of global state information and handling slow/failed nodes.
introduction to cloud computing for college.pdfsnehan789
The document provides an overview of cloud computing by outlining its module which includes fundamental concepts of distributed systems, cluster computing, grid computing, cloud computing, and mobile computing. It then defines computing and distributed systems, explaining that a distributed system is a system with multiple components located on different machines that communicate and coordinate actions to appear as a single system. Key characteristics of distributed systems include presenting a single system image, expandability, continuous availability, and being supported by middleware.
Introduction to Cloud Computing
Cloud computing is a transformative technology that allows businesses and individuals to access computing resources over the internet. Instead of owning and maintaining physical hardware and software, users can leverage cloud services provided by companies like Amazon Web Services (AWS), Microsoft Azure, Google Cloud Platform (GCP), and others. This shift has revolutionized how we think about IT infrastructure, software development, data storage, and more.
Key Concepts of Cloud Computing
On-Demand Self-Service:
Users can provision computing resources as needed without human intervention from the service provider. This includes servers, storage, and applications.
Broad Network Access:
Cloud services are available over the network and accessed through standard mechanisms, enabling use from a variety of devices like laptops, smartphones, and tablets.
Resource Pooling:
Providers use a multi-tenant model to serve multiple customers with dynamically assigned resources. This model allows for economies of scale and efficient resource utilization.
Rapid Elasticity:
Resources can be elastically provisioned and released, sometimes automatically, to scale rapidly outward and inward commensurate with demand.
Measured Service:
Cloud systems automatically control and optimize resource use by leveraging a metering capability, allowing for pay-as-you-go pricing models.
Types of Cloud Computing Services
Infrastructure as a Service (IaaS):
Provides virtualized computing resources over the internet. Examples include AWS EC2, Google Compute Engine, and Azure Virtual Machines.
Platform as a Service (PaaS):
Offers hardware and software tools over the internet, typically used for application development. Examples include Google App Engine, AWS Elastic Beanstalk, and Azure App Services.
Software as a Service (SaaS):
Delivers software applications over the internet, on a subscription basis. Examples include Google Workspace, Microsoft Office 365, and Salesforce.
Deployment Models
Public Cloud:
Services are delivered over the public internet and shared across multiple organizations. It offers cost savings but might pose concerns regarding data security and privacy.
Private Cloud:
Dedicated to a single organization, offering enhanced security and control over data and infrastructure. It's more expensive than public cloud but can be tailored to specific business needs.
Hybrid Cloud:
Combines public and private clouds, allowing data and applications to be shared between them. This model offers greater flexibility and optimization of existing infrastructure, security, and compliance.
Community Cloud:
Shared between organizations with common concerns (e.g., security, compliance, jurisdiction). It can be managed internally or by a third-party.
Advantages of Cloud Computing
Cost Efficiency: Reduces the need for significant capital expenditure on hardware and software.
Scalability and Flexibility: Easily scales up or down based on
This document provides an overview of distributed systems. It discusses different types of distributed systems like client-server, n-tier, and peer-to-peer systems. It outlines advantages like performance, reliability, and scalability. Challenges include networking and security issues. Key features are discussed such as concurrency, lack of a global clock, and independent failures. Characteristics of distributed systems include heterogeneity, openness, security, scalability, failure handling, concurrency, and transparency. Examples provided are the Internet, intranets, and mobile/ubiquitous computing systems.
The document outlines the objectives and units of a course on distributed systems. The objectives are to learn about distributed environments, processes and synchronization, peer-to-peer networks, fault tolerance, network filesystems and middleware technologies. Unit 1 introduces distributed systems and covers resource sharing challenges, API protocols, data representation, marshaling, multicast communication and remote procedure calls.
This document discusses distributed operating systems. A distributed OS connects multiple computers via communication channels to distribute processing jobs between central processors. It consists of nodes joined by networks that enable sharing of computing resources and files while providing users with virtual machines. The document describes types of distributed OS like client-server and peer-to-peer, and features like openness, resource sharing, transparency, and flexibility. Examples of distributed OS mentioned are Solaris, OSF/1, Micros, and DYNIX.
01Introduction to Cloud Computing .pptxssuser586772
The document provides an introduction to cloud computing, including definitions and concepts. It discusses:
- What cloud computing is and the underlying principles of parallel and distributed computing systems.
- Key cloud characteristics like elasticity, scalability, and on-demand provisioning which allow resources to be provisioned as needed.
- Benefits of cloud computing like flexibility and cost savings but also challenges of complex management across cloud platforms.
The document discusses different types of computing including sequential, parallel, distributed, cluster, grid, and cloud computing. Sequential computing involves breaking a task into sequential steps executed on a single processor. Parallel computing breaks a task into independent sub-tasks that can be processed simultaneously on multiple processors. Distributed computing aggregates resources from geographically distributed systems to complete tasks. Cluster computing uses tightly coupled computers working as a single system. Grid computing virtualizes distributed resources to create a single system. Cloud computing provides on-demand access to configurable computing resources over the internet.
- Introduction - Distributed - System -ssuser7c150a
The document provides an introduction to distributed systems, including defining their key characteristics and challenges. It discusses how distributed systems allow independent computers to coordinate activities and share resources over a network. Examples of distributed systems include the internet, intranets, cloud computing systems, and wireless networks. The main goals of distributed systems are transparency, openness, and scalability, while the key challenges are heterogeneity, distribution transparency, fault tolerance, and security.
This document provides an introduction to distributed computing, including definitions, history, goals, characteristics, examples of applications, and scenarios. It discusses advantages like improved performance and reliability, as well as challenges like complexity, network problems, security, and heterogeneity. Key issues addressed are transparency, openness, scalability, and the need to handle differences across hardware, software, and developers when designing distributed systems.
UNIT 4 - UNDERSTANDING THE NETWORK ARCHITECTURE.pptxLeahRachael
This document provides an overview of network architecture and protocols. It discusses Ethernet, including its history, components, addressing, frames, and media access control. It also covers token ring, AppleTalk, ARCNET, the layered network protocol model (with HTTP as an example), and network operating system architectures like peer-to-peer and client-server. Segmentation strategies like firewalls and software-defined networking are introduced to improve performance and security.
1. The document discusses various models for distributed systems including physical, architectural, and fundamental models.
2. Physical models describe the hardware components and network connections. Architectural models capture the computational elements and communication tasks. Fundamental models take an abstract view of key aspects like interaction and failure.
3. Key architectural elements include communicating entities, communication paradigms, roles and responsibilities, and placement strategies. Common patterns are also described like layering, tiered architectures, and proxies.
Distributed computing utilizes a network of interconnected computers to accomplish tasks more quickly than a single computer. Each computer has its own memory and performs a portion of the overall task, communicating via message passing. Key benefits include scalability, redundancy, and sharing of resources. Common architectures include client-server, n-tier, and peer-to-peer networks. While complex, distributed systems can provide increased speed, reliability, and ability to incrementally expand processing power over centralized systems.
The document defines a distributed system and provides examples. It outlines the challenges in designing distributed systems, including heterogeneity, openness, security, scalability, failure handling, concurrency, and transparency. Distributed systems divide tasks across networked computers and aim to appear as a single computer to users.
1. Models can describe aspects of distributed systems in an abstract way, simplifying their complexity. Architectural models define how responsibilities are distributed among components, while interaction models deal with time handling.
2. Three architectural models were discussed: client-server, peer-to-peer, and variations including proxy servers, mobile code, agents, thin clients, and mobile devices.
3. Two interaction models - synchronous and asynchronous distributed systems - differ in whether bounds can be placed on timing.
4. Fault models specify what faults may occur and their effects, including omission, arbitrary, and timing faults impacting processes and communication.
This presentation introduces several types of distributed computing technologies including distributed computing, grid computing, cluster computing, utility computing, and cloud computing. It provides details on each type such as definitions, examples, and characteristics. Cloud computing allows for sharing of computing resources over the internet rather than using local servers. It can be public, private, hybrid or community-based and offers platform, software or infrastructure as services to users.
Infrastructure Challenges in Scaling RAG with Custom AI modelsZilliz
Building Retrieval-Augmented Generation (RAG) systems with open-source and custom AI models is a complex task. This talk explores the challenges in productionizing RAG systems, including retrieval performance, response synthesis, and evaluation. We’ll discuss how to leverage open-source models like text embeddings, language models, and custom fine-tuned models to enhance RAG performance. Additionally, we’ll cover how BentoML can help orchestrate and scale these AI components efficiently, ensuring seamless deployment and management of RAG systems in the cloud.
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
GraphRAG for Life Science to increase LLM accuracyTomaz Bratanic
GraphRAG for life science domain, where you retriever information from biomedical knowledge graphs using LLMs to increase the accuracy and performance of generated answers
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfPaige Cruz
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
Maruthi Prithivirajan, Head of ASEAN & IN Solution Architecture, Neo4j
Get an inside look at the latest Neo4j innovations that enable relationship-driven intelligence at scale. Learn more about the newest cloud integrations and product enhancements that make Neo4j an essential choice for developers building apps with interconnected data and generative AI.
UiPath Test Automation using UiPath Test Suite series, part 6DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Driving Business Innovation: Latest Generative AI Advancements & Success StorySafe Software
Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
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2. DEFINITIONS
• A distributed system is a collection of independent
computers that appears to its users as a single
coherent system.
• A distributed system is a collection of autonomous
computers linked by a computer network that appear
to the users of the system as a single computer.
3. • A collection of autonomous computers linked by a
computer network and supported by software that
enables the collection to operate as an integrated
facility.
• You know you have one when the crash of a
computer you have never heard of stops you from
getting any work done. (Leslie Lamport).
4. IMPORTANT ASPECTS:
• A distributed system consists of components (i.e.,
computers) that are autonomous
• Users (people or programs) think they are dealing with
a single system
• Autonomous components need to collaborate
5. •Examples of distributed systems:
–Network of workstations
–Distributed manufacturing system (e.g, automated
assembly line)
–Network of branch office computers
–Automatic Teller Machine Network (ATMs)
–Local Area Network
–Database Management System
–World-Wide Web
6. CHARACTERISTICS OF DISTRIBUTED SYSTEMS:
Differences between various computers and the ways
in which they communicate are mostly hidden from
users.
Users and applications can interact with a distributed
system in a consistent and uniform way, regardless of
where and when interaction takes place.
Distributed systems should also be relatively easy to
expand or scale.
A distributed system will normally be continuously
available
7. Multiple autonomous components.
Heterogeneous.
Components are not shared by all users.
Resources may not be accessible.
Software runs in concurrent processes on different
processors.
Multiple Points of control.
Multiple Points of failure (but more fault tolerant!).
8. • Layers of software support heterogeneous computers
and networks while offering a single system view -
sometimes called middleware.
9.
10. Four networked computers and three applications:
1. Application B is distributed across computers 2 and
3.
2. Each application is offered the same interface
3. Distributed system provides the means for
components of a single distributed application to
communicate with each other, but also to let different
applications communicate.
4. It also hides the differences in hardware and
operating systems from each application.
11. GOALS
• Four goals that should be met to make building a
distributed system worth the effort:
• 1. should make resources easily accessible
• 2. should reasonably hide the fact that resources are
distributed across a network;
• 3. should be open
• 4. should be scalable.
12. 1. MAKING RESOURCES ACCESSIBLE
• Main goal of a distributed system –
make it easy for the users (and applications) to
access remote resources
to share them in a controlled and efficient way.
• Resources - anything: printers, computers, storage
facilities, data, files, Web pages, and networks, etc.
14. 2. DISTRIBUTION TRANSPARENCY
• Goal - hide the fact that its processes and resources
are physically distributed across multiple computers –
systems should be transparent
• Different forms of transparency in a distributed system
(ISO, 1995).
15. Transparency Description
Access Hide differences in data representation and
how a resource is accessed
Migration Hide that a resource may move to another
location
Location Hide where a resource is located
Relocation Hide that a resource may be moved to another
location while in use
Replication Hide that a resource is replicated
Concurrency Hide that a resource may be shared by
several competitive users
Failure Hide the failure and recovery of a resource
16. Degree of Transparency
• Issues:
Timing:
• e.g. requesting an electronic newspaper to appear in
your mailbox before 7 A.M. local time, as usual, while
you are currently at the other end of the world living in
a different time zone.
17. Synchronization:
• e.g. a wide-area distributed system that connects a
process in San Francisco to a process in Amsterdam
limited by laws of physics - a message sent from one
process to the other takes about 35 milliseconds.
it takes several hundreds of milliseconds using a
computer network.
• signal transmission is not only limited by the speed of
light, but also by limited processing capacities of the
intermediate switches.
18. Performance:
• e.g. many Internet applications repeatedly try to
contact a server before finally giving up.
• Consequently, attempting to mask a transient server
failure before trying another one may slow down the
system as a whole.
19. Consistency:
• e.g. need to guarantee that several replicas, located
on different continents, need to be consistent all the
time - a single update operation may now even take
seconds to complete, something that cannot be
hidden from users.
20. Context Awareness:
• e.g. notion of location and context awareness is
becoming increasingly important, it may be best to
actually expose distribution rather than trying to hide
it.
• Consider an office worker who wants to print a file
from her notebook computer.
• It is better to send the print job to a busy nearby
printer, rather than to an idle one at corporate
headquarters in a different country.
21. Limits of Possibility:
• Recognizing that full distribution transparency is
simply impossible, we should ask ourselves whether it
is even wise to pretend that we can achieve it.
22. 3. OPENNESS
• Goal: offer services according to standard rules that
describe the syntax and semantics of those services.
e.g.
computer networks - standard rules govern the
format, contents, and meaning of messages sent and
received.
distributed systems - services are specified through
interfaces, which are often described in an Interface
Definition Language (IDL).
23. • Interface definitions written in an IDL nearly always
capture only the syntax of services
• specify names of the available functions with types of
parameters, return values, possible exceptions that can
be raised, etc.
• allows an arbitrary process that needs a certain
interface to talk to another process that provides that
interface
• allows two independent parties to build completely
different implementations of those interfaces, leading to
two separate distributed systems that operate in exactly
24. • Properties of specifications:
• Complete - everything that is necessary to make an
implementation has been specified.
• Neutral - specifications do not prescribe what an
implementation should look like
Lead to:
• Interoperability - characterizes the extent by which two
implementations of systems or components from
different manufacturers can co-exist and work
together by merely relying on each other's services as
specified by a common standard.
25. • Portability - characterizes to what extent an
application developed for a distributed system A can
be executed, without modification, on a different
distributed system B that implements the same
interfaces as A.
26. • Goals: an open distributed system should also be
extensible. i.e.
be easy to configure the system out of different
components (possibly from different developers).
be easy to add new components or replace existing
ones without affecting those components that stay in
place.
27. 4. SCALABILITY
• Scalability of a system is measured with respect to:
1. Size - can easily add more users and resources to
the system.
2. Geographic extent - a geographically scalable
system is one in which the users and resources may
lie far apart.
3. Administrative scalability - can be easy to manage
even if it spans many independent administrative
organizations.
28. Scalability limitations of size
Concept Example
Centralized services A single server for all users
Centralized data A single on-line telephone
book
Centralized algorithms Doing routing based on
complete information
29. ARCHITECTURE
Distributed programming typically falls into one of
several basic architectures or categories:
• Client-server — Smart client code contacts the
server for data, then formats and displays it to the
user. Input at the client is committed back to the
server when it represents a permanent change.
• 3-tier architecture — Three tier systems move the
client intelligence to a middle tier so that stateless
clients can be used. This simplifies application
deployment. Most web applications are 3-Tier.
30. • N-tier architecture — N-Tier refers typically to web
applications which further forward their requests to
other enterprise services. This type of application is
the one most responsible for the success of
application servers.
• Tightly coupled (clustered) — refers typically to a
set of highly integrated machines that run the same
process in parallel, subdividing the task in parts that
are made individually by each one, and then put back
together to make the final result.
31. • Peer-to-peer —an architecture where there is no
special machine or machines that provide a service or
manage the network resources. Instead all
responsibilities are uniformly divided among all
machines, known as peers. Peers can serve both as
clients and servers.
• Space based — refers to an infrastructure that
creates the illusion (virtualization) of one single
address-space. Data are transparently replicated
according to application needs. Decoupling in time,
space and reference is achieved.
32. SCALING TECHNIQUES
• Three techniques for scaling:
a. hiding communication latencies
b. distribution
c. replication
33. A. Hiding communication latencies - important to
achieving geographical scalability.
• 1. try to avoid waiting for responses to remote service
requests.
e.g, when a service has been requested at a remote
machine, an alternative to waiting for a reply from the
server is to do other useful work at the requester's
side.
construct the requesting application in such a way
that it uses only asynchronous communication.
34. • 2. reduce the overall communication
e.g. in interactive applications when a user sends a
request he will generally have nothing better to do
than to wait for the answer.
move part of the computation that is normally done at
the server to the client process requesting the service.
35. B. Distribution - splitting a component into smaller
parts and spreading those parts across the system.
e.g. Internet Domain Name System (DNS).
• The DNS name space is hierarchically organized into
a tree of domains, which are divided into non-
overlapping zones.
• Names in each zone are handled by a single name
server.
• Resolving a name means returning the network
address of the associated host.
36. C. Issues of caching and replication - multiple copies
of a resource -> modifying one copy makes that copy
different from the others -> leads to consistency
problems.
• Weak consistency – e.g. a cached Web document of
which the validity has not been checked for the last
few minutes.
• Strong consistency – e.g. electronic stock exchanges
and auctions.
• problem –
37. An update must be immediately propagated to all
other copies.
If two updates happen concurrently, it is often also
required that each copy is updated in the same order.
Generally requires some global synchronization
mechanism – hard to implement in a scalable way (i.e.
speed of light
38. PITFALLS
• False assumptions that everyone makes when
developing a distributed application for the first time
(by Peter Deutsch):
• 1. The network is reliable.
• 2. The network is secure.
• 3. The network is homogeneous.
• 4. The topology does not change.
39. • 5. Latency is zero.
• 6. Bandwidth is infinite.
• 7. Transport cost is zero.
• 8. There is one administrator.