The document discusses the history and concepts of distributed systems. It defines a distributed system as a collection of independent computers that appears as a single system to users. Distributed systems provide benefits like resource sharing, availability, scalability, and performance. However, they also introduce challenges around concurrency, security, partial failures, and heterogeneity. The document outlines common goals for distributed systems like transparency, openness, and scalability. It describes different approaches to scaling distributed systems through techniques like hiding latencies, distribution, and replication. Finally, it discusses key hardware concepts like multiprocessors and multicomputers as well as software approaches like distributed operating systems, network operating systems, and middleware.
This document provides an introduction and definition of distributed systems. It discusses that a distributed system consists of multiple autonomous computers that appear as a single system to users. It describes characteristics like transparency, openness, and scalability. Hardware concepts like shared memory multiprocessors and message passing multicomputers are covered. Software concepts like distributed operating systems and network operating systems are introduced. Transparency, organization, goals and examples of distributed systems are summarized.
The document provides an introduction to distributed systems, including definitions, goals, types, and challenges. It defines a distributed system as a collection of independent computers that appear as a single system to users. Distributed systems aim to share resources and data across multiple computers for availability, reliability, scalability, and performance. There are three main types: distributed computing systems, distributed information systems, and distributed pervasive systems. Developing distributed systems faces challenges around concurrency, security, partial failures, and heterogeneity.
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.
The document introduces distributed systems, defining them as collections of independent computers that appear as a single system to users, discusses the goals of transparency, openness, and scalability in distributed systems, and describes three main types - distributed computing systems for tasks like clustering and grids, distributed information systems for integrating applications, and distributed pervasive systems for mobile and embedded devices.
This document provides an overview of distributed systems. It discusses tightly-coupled and loosely-coupled multiprocessor systems, with loosely-coupled systems referring to distributed systems that have independent processors, memories, and operating systems. The document outlines some key properties of distributed systems, including that they consist of independent nodes that communicate through message passing, and accessing remote resources is more expensive than local resources. It also summarizes some advantages and challenges of distributed systems.
This document provides definitions and examples of distributed and parallel systems. It begins by defining a distributed system as a collection of independent computers that communicate over a network to collaborate on tasks. Examples given include computer networks, ATMs, and databases. Advantages include information sharing and scalability, while disadvantages include difficulty developing software and security issues. The document then defines a parallel system as having multiple processors with direct access to shared memory, and examples provided are supercomputers and server clusters. Key differences between distributed and parallel systems are also outlined.
The document discusses the history and goals of distributed systems. It begins by describing how computers evolved from large centralized mainframes in the 1940s-1980s, to networked systems in the mid-1980s enabled by microprocessors and computer networks. The key goals of distributed systems are to make resources accessible across a network, hide the distributed nature of resources to provide transparency, remain open to new services, and scale effectively with increased users and resources. Examples of distributed systems include the internet, intranets, and worldwide web.
This document defines and discusses key principles and characteristics of distributed systems. It states that a distributed system is a collection of independent computers that appear as a single coherent system to users. Important goals of distributed systems are connecting users to resources, transparency, openness, and scalability. Distributed systems are made up of hardware components like multiple autonomous machines that communicate over a network, as well as software like middleware that hides the underlying platform heterogeneity from applications.
This document provides an introduction and definition of distributed systems. It discusses that a distributed system consists of multiple autonomous computers that appear as a single system to users. It describes characteristics like transparency, openness, and scalability. Hardware concepts like shared memory multiprocessors and message passing multicomputers are covered. Software concepts like distributed operating systems and network operating systems are introduced. Transparency, organization, goals and examples of distributed systems are summarized.
The document provides an introduction to distributed systems, including definitions, goals, types, and challenges. It defines a distributed system as a collection of independent computers that appear as a single system to users. Distributed systems aim to share resources and data across multiple computers for availability, reliability, scalability, and performance. There are three main types: distributed computing systems, distributed information systems, and distributed pervasive systems. Developing distributed systems faces challenges around concurrency, security, partial failures, and heterogeneity.
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.
The document introduces distributed systems, defining them as collections of independent computers that appear as a single system to users, discusses the goals of transparency, openness, and scalability in distributed systems, and describes three main types - distributed computing systems for tasks like clustering and grids, distributed information systems for integrating applications, and distributed pervasive systems for mobile and embedded devices.
This document provides an overview of distributed systems. It discusses tightly-coupled and loosely-coupled multiprocessor systems, with loosely-coupled systems referring to distributed systems that have independent processors, memories, and operating systems. The document outlines some key properties of distributed systems, including that they consist of independent nodes that communicate through message passing, and accessing remote resources is more expensive than local resources. It also summarizes some advantages and challenges of distributed systems.
This document provides definitions and examples of distributed and parallel systems. It begins by defining a distributed system as a collection of independent computers that communicate over a network to collaborate on tasks. Examples given include computer networks, ATMs, and databases. Advantages include information sharing and scalability, while disadvantages include difficulty developing software and security issues. The document then defines a parallel system as having multiple processors with direct access to shared memory, and examples provided are supercomputers and server clusters. Key differences between distributed and parallel systems are also outlined.
The document discusses the history and goals of distributed systems. It begins by describing how computers evolved from large centralized mainframes in the 1940s-1980s, to networked systems in the mid-1980s enabled by microprocessors and computer networks. The key goals of distributed systems are to make resources accessible across a network, hide the distributed nature of resources to provide transparency, remain open to new services, and scale effectively with increased users and resources. Examples of distributed systems include the internet, intranets, and worldwide web.
This document defines and discusses key principles and characteristics of distributed systems. It states that a distributed system is a collection of independent computers that appear as a single coherent system to users. Important goals of distributed systems are connecting users to resources, transparency, openness, and scalability. Distributed systems are made up of hardware components like multiple autonomous machines that communicate over a network, as well as software like middleware that hides the underlying platform heterogeneity from applications.
A distributed system is a collection of independent computers that appears to users as a single coherent system. Key properties of distributed systems include transparency, where differences between computers are hidden from users, coherency in providing consistent interaction regardless of location or time, and scalability to expand the system size and resources. Distributed systems aim to be reliable, remaining continuously available despite potential failures of individual components.
This document provides background information on cloud computing and distributed systems. It discusses how distributed systems involve a collection of independent computers that work together over a network to appear as a single system. The document outlines some key advantages and disadvantages of distributed systems, as well as design goals like transparency, scalability, fault tolerance, and security. It also discusses some common issues in distributed systems like global knowledge, naming, scalability, process synchronization, and resource management.
This document discusses distributed computing systems. It defines distributed computing as a collection of interconnected processors with local memory and peripherals that communicate via message passing over a network. There are two types of distributed systems - tightly coupled systems with shared memory and loosely coupled systems where processors have separate local memory and communicate by passing messages. Distributed computing became popular due to the difficulties of centralized processing on mainframes and the development of microprocessors and computer networks. The key benefits of distributed computing systems over centralized systems include scalability, redundancy, and independence from specific hardware or software standards.
A distributed computing system is a collection of interconnected processors with local memory that communicate via message passing. There are various models including minicomputer, workstation, workstation-server, and processor pool. Distributed systems provide advantages like supporting distributed applications, sharing information and resources, extensibility, shorter response times, higher reliability, flexibility, and better price-performance ratio compared to centralized systems.
CSI-503 - 11.Distributed Operating Systemghayour abbas
A distributed operating system connects multiple computers via a single communication channel. It allows for the distribution of computing resources and I/O files across several central processors to serve multiple users and real-time applications simultaneously. Distributed operating systems come in various types, including client-server systems, peer-to-peer systems, middleware, three-tier, and n-tier architectures. Their key features are openness, scalability, resource sharing, flexibility, transparency, and heterogeneity. Examples include Solaris, OSF/1, Micros, and DYNIX. Distributed operating systems find applications in network applications, telecommunication networks, parallel computation, and real-time process control.
The document discusses several topics related to distributed operating systems including:
- Distributed shared memory, which implements shared memory across distributed systems without physical shared memory.
- Central server and migration algorithms for managing shared data in distributed shared memory systems.
- Read replication and full replication algorithms that allow multiple nodes to read or write shared data.
- Memory coherence and coherence protocols for maintaining consistency across processor caches.
- Key components of distributed file systems such as naming, caching, writing policies, availability, scalability, and cache consistency.
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.
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.
- 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 discusses distributed systems. It defines a distributed system as a collection of independent computers that appear as a single system to users. Some key advantages are data and resource sharing, improved communication, flexibility, and reliability. Challenges include more difficult software development, network issues, and security risks. The document outlines concepts like MIMD, tightly vs loosely coupled systems, bus vs switched networks, and software types like network OSes, distributed systems, and multiprocessor time sharing. It also discusses important design issues for distributed systems such as transparency, flexibility, reliability, performance, and scalability.
The document provides an introduction to distributed systems, defining them as a collection of independent computers that communicate over a network to act as a single coherent system. It discusses the motivation for and characteristics of distributed systems, including concurrency, lack of a global clock, and independent failures. Architectural categories of distributed systems include tightly and loosely coupled, with examples given of different types of distributed systems such as database management systems, ATM networks, and the internet.
The document provides an introduction to distributed systems, defining them as a collection of independent computers that communicate over a network to act as a single coherent system. It discusses the motivation for and characteristics of distributed systems, including concurrency, lack of a global clock, and independence of failures. Architectural categories of distributed systems include tightly coupled and loosely coupled, with examples given of different types of distributed systems such as database management systems, ATM networks, and the internet.
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.
A distributed system consists of multiple connected CPUs that appear as a single system to users. Distributed systems provide advantages like communication, resource sharing, reliability and scalability. However, they require distribution-aware software and uninterrupted network connectivity. Distributed operating systems manage resources across connected computers transparently. They provide various forms of transparency and handle issues like failure, concurrency and replication. Remote procedure calls allow calling remote services like local procedures to achieve transparency.
Distributed System Unit 1 Notes by Dr. Nilam Choudhary, SKIT JaipurDrNilam Choudhary
Distributed System is a collection of autonomous computer systems that are physically separated but are connected by a centralized computer network that is equipped with distributed system software. The autonomous computers will communicate among each system by sharing resources and files and performing the tasks assigned to them.
This document defines and compares the two major types of network operating systems (NOS): peer-to-peer and client/server. A NOS is an operating system designed to support sharing of resources like files, printers, and applications between computers in a network. In a peer-to-peer NOS, all computers have equal abilities to access shared resources without a central server. In a client/server NOS, functions are centralized on dedicated file servers that provide resources to individual workstations or clients. Examples of each type and their relative advantages and disadvantages are provided.
Distributed computing system is a collection of interconnected computers that appear as a single system. There are two types of computer architectures for distributed systems - tightly coupled and loosely coupled. In tightly coupled systems, processors share a single memory while in loosely coupled systems, processors have their own local memory and communicate through message passing. Distributed systems provide advantages like better price-performance ratio, resource sharing, reliability, and scalability but also introduce challenges around transparency, communication, performance, heterogeneity, and fault tolerance.
The document discusses different types of operating systems and communication networks. It describes distributed operating systems, multiprocessor operating systems, database operating systems, and real-time operating systems. It also covers distributed system architectures, issues in distributed operating systems like naming and resource management, and communication networks including local area networks and protocols like CSMA/CD.
This document provides an introduction to distributed systems, including their key features and some related concepts. It discusses how distributed systems build upon networking to allow independent machines to act as a single system transparently to users. The document also mentions client-server architectures, advantages like economies of scale and fault tolerance, challenges around naming, operating systems, shared memory, and other issues in distributed environments.
A wireless local area network (WLAN) uses radio waves to transmit and receive data over the air, minimizing the need for wired connections. A basic wireless network can be set up between two PCs with wireless cards in a peer-to-peer configuration. Larger wireless networks use access points connected to a wired network to connect multiple clients and extend the range of the network. Factors like access point placement, use of extension points or directional antennas can help ensure clients stay connected as they roam across coverage areas. While wireless networks provide mobility and flexibility, their setup and maintenance require additional hardware, have security limitations, and may have lower bandwidth compared to wired networks.
WiMAX is a wireless technology standard that provides broadband connections over long distances, with mobility. It uses IEEE 802.16 standards for wireless metropolitan area networks (MAN). The WiMAX architecture includes mobile subscriber stations, an access service network that provides radio access, and a connectivity service network that provides IP connectivity. It allows for long range connectivity up to 30 miles, mobility, and accessibility connecting multiple base stations. Compared to WiFi, WiMAX provides wireless broadband for transferring data, voice, and video at higher rates between both fixed and mobile devices. Security is an important consideration for WiMAX implementations.
A distributed system is a collection of independent computers that appears to users as a single coherent system. Key properties of distributed systems include transparency, where differences between computers are hidden from users, coherency in providing consistent interaction regardless of location or time, and scalability to expand the system size and resources. Distributed systems aim to be reliable, remaining continuously available despite potential failures of individual components.
This document provides background information on cloud computing and distributed systems. It discusses how distributed systems involve a collection of independent computers that work together over a network to appear as a single system. The document outlines some key advantages and disadvantages of distributed systems, as well as design goals like transparency, scalability, fault tolerance, and security. It also discusses some common issues in distributed systems like global knowledge, naming, scalability, process synchronization, and resource management.
This document discusses distributed computing systems. It defines distributed computing as a collection of interconnected processors with local memory and peripherals that communicate via message passing over a network. There are two types of distributed systems - tightly coupled systems with shared memory and loosely coupled systems where processors have separate local memory and communicate by passing messages. Distributed computing became popular due to the difficulties of centralized processing on mainframes and the development of microprocessors and computer networks. The key benefits of distributed computing systems over centralized systems include scalability, redundancy, and independence from specific hardware or software standards.
A distributed computing system is a collection of interconnected processors with local memory that communicate via message passing. There are various models including minicomputer, workstation, workstation-server, and processor pool. Distributed systems provide advantages like supporting distributed applications, sharing information and resources, extensibility, shorter response times, higher reliability, flexibility, and better price-performance ratio compared to centralized systems.
CSI-503 - 11.Distributed Operating Systemghayour abbas
A distributed operating system connects multiple computers via a single communication channel. It allows for the distribution of computing resources and I/O files across several central processors to serve multiple users and real-time applications simultaneously. Distributed operating systems come in various types, including client-server systems, peer-to-peer systems, middleware, three-tier, and n-tier architectures. Their key features are openness, scalability, resource sharing, flexibility, transparency, and heterogeneity. Examples include Solaris, OSF/1, Micros, and DYNIX. Distributed operating systems find applications in network applications, telecommunication networks, parallel computation, and real-time process control.
The document discusses several topics related to distributed operating systems including:
- Distributed shared memory, which implements shared memory across distributed systems without physical shared memory.
- Central server and migration algorithms for managing shared data in distributed shared memory systems.
- Read replication and full replication algorithms that allow multiple nodes to read or write shared data.
- Memory coherence and coherence protocols for maintaining consistency across processor caches.
- Key components of distributed file systems such as naming, caching, writing policies, availability, scalability, and cache consistency.
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.
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.
- 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 discusses distributed systems. It defines a distributed system as a collection of independent computers that appear as a single system to users. Some key advantages are data and resource sharing, improved communication, flexibility, and reliability. Challenges include more difficult software development, network issues, and security risks. The document outlines concepts like MIMD, tightly vs loosely coupled systems, bus vs switched networks, and software types like network OSes, distributed systems, and multiprocessor time sharing. It also discusses important design issues for distributed systems such as transparency, flexibility, reliability, performance, and scalability.
The document provides an introduction to distributed systems, defining them as a collection of independent computers that communicate over a network to act as a single coherent system. It discusses the motivation for and characteristics of distributed systems, including concurrency, lack of a global clock, and independent failures. Architectural categories of distributed systems include tightly and loosely coupled, with examples given of different types of distributed systems such as database management systems, ATM networks, and the internet.
The document provides an introduction to distributed systems, defining them as a collection of independent computers that communicate over a network to act as a single coherent system. It discusses the motivation for and characteristics of distributed systems, including concurrency, lack of a global clock, and independence of failures. Architectural categories of distributed systems include tightly coupled and loosely coupled, with examples given of different types of distributed systems such as database management systems, ATM networks, and the internet.
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.
A distributed system consists of multiple connected CPUs that appear as a single system to users. Distributed systems provide advantages like communication, resource sharing, reliability and scalability. However, they require distribution-aware software and uninterrupted network connectivity. Distributed operating systems manage resources across connected computers transparently. They provide various forms of transparency and handle issues like failure, concurrency and replication. Remote procedure calls allow calling remote services like local procedures to achieve transparency.
Distributed System Unit 1 Notes by Dr. Nilam Choudhary, SKIT JaipurDrNilam Choudhary
Distributed System is a collection of autonomous computer systems that are physically separated but are connected by a centralized computer network that is equipped with distributed system software. The autonomous computers will communicate among each system by sharing resources and files and performing the tasks assigned to them.
This document defines and compares the two major types of network operating systems (NOS): peer-to-peer and client/server. A NOS is an operating system designed to support sharing of resources like files, printers, and applications between computers in a network. In a peer-to-peer NOS, all computers have equal abilities to access shared resources without a central server. In a client/server NOS, functions are centralized on dedicated file servers that provide resources to individual workstations or clients. Examples of each type and their relative advantages and disadvantages are provided.
Distributed computing system is a collection of interconnected computers that appear as a single system. There are two types of computer architectures for distributed systems - tightly coupled and loosely coupled. In tightly coupled systems, processors share a single memory while in loosely coupled systems, processors have their own local memory and communicate through message passing. Distributed systems provide advantages like better price-performance ratio, resource sharing, reliability, and scalability but also introduce challenges around transparency, communication, performance, heterogeneity, and fault tolerance.
The document discusses different types of operating systems and communication networks. It describes distributed operating systems, multiprocessor operating systems, database operating systems, and real-time operating systems. It also covers distributed system architectures, issues in distributed operating systems like naming and resource management, and communication networks including local area networks and protocols like CSMA/CD.
This document provides an introduction to distributed systems, including their key features and some related concepts. It discusses how distributed systems build upon networking to allow independent machines to act as a single system transparently to users. The document also mentions client-server architectures, advantages like economies of scale and fault tolerance, challenges around naming, operating systems, shared memory, and other issues in distributed environments.
A wireless local area network (WLAN) uses radio waves to transmit and receive data over the air, minimizing the need for wired connections. A basic wireless network can be set up between two PCs with wireless cards in a peer-to-peer configuration. Larger wireless networks use access points connected to a wired network to connect multiple clients and extend the range of the network. Factors like access point placement, use of extension points or directional antennas can help ensure clients stay connected as they roam across coverage areas. While wireless networks provide mobility and flexibility, their setup and maintenance require additional hardware, have security limitations, and may have lower bandwidth compared to wired networks.
WiMAX is a wireless technology standard that provides broadband connections over long distances, with mobility. It uses IEEE 802.16 standards for wireless metropolitan area networks (MAN). The WiMAX architecture includes mobile subscriber stations, an access service network that provides radio access, and a connectivity service network that provides IP connectivity. It allows for long range connectivity up to 30 miles, mobility, and accessibility connecting multiple base stations. Compared to WiFi, WiMAX provides wireless broadband for transferring data, voice, and video at higher rates between both fixed and mobile devices. Security is an important consideration for WiMAX implementations.
This document is from a Cisco Systems presentation that provides an introduction to computer networks. It discusses how networks are used in everyday life and small businesses. The objectives are to explain network topologies, devices, characteristics that support business communication, and trends affecting business networks. The document covers topics such as the global community, how networks impact daily life, network sizes and components, LANs and WANs, the Internet, trends like BYOD and cloud computing, and network security.
This document summarizes key concepts around synchronization in distributed systems including clock synchronization, logical clocks, election algorithms, and mutual exclusion. It discusses how physical clocks become unsynchronized over time, introduces Lamport timestamps and logical clocks, describes the Bully and Ring election algorithms, and covers a centralized mutual exclusion algorithm that uses a coordinator to grant processes permission to enter a critical region.
This document discusses consistency models in distributed systems with replication. It describes reasons for replication including reliability and performance. Various consistency models are covered, including: strict consistency where reads always return the most recent write; sequential consistency where operations appear in a consistent order across processes; weak consistency which enforces consistency on groups of operations; and release consistency which separates acquiring and releasing locks to selectively guard shared data. Client-centric models like eventual consistency are also discussed, where updates gradually propagate to all replicas.
This document provides an overview of naming in distributed systems. It discusses how names are used to identify and refer to entities and resources. A naming system implements a name space that organizes names in a hierarchical structure. Name resolution involves mapping names to addresses or identifiers. The implementation of naming services is often distributed across multiple name servers to improve scalability and availability. Examples of naming systems like the Domain Name System (DNS) are also discussed.
The document discusses processes and threads in distributed systems. It explains that threads allow multiple executions within the same process by sharing resources like memory. Distributed systems can use multithreaded clients and servers to improve performance. Code migration is also discussed, where a program's code and execution state can be moved between machines for better load balancing or to reduce communication costs. The challenges of migrating local resources that may be fixed to a particular machine are also covered.
This document discusses communication in distributed systems and introduces several communication models. It begins by explaining that communication in distributed systems is based on message passing rather than shared memory. It then reviews four common communication models: Remote Procedure Call (RPC), Remote Method Invocation (RMI), Message-Oriented Middleware (MOM), and Streams. The document also discusses layered protocols like OSI and TCP/IP, and delves into specifics of RPC including parameter passing and extended RPC models like asynchronous RPC and doors.
This document discusses different types of distributed system architectures. It covers layered architectures where components are organized in layers and requests flow from top to bottom. Object-based architectures connect components through remote procedure calls. Data-centered architectures involve all components communicating through a shared data repository. Event-based architectures use publish-subscribe systems where components communicate through event propagation. The document also describes centralized client-server architectures and decentralized peer-to-peer architectures. Client-server involves clients requesting from servers, while peer-to-peer has no central control and nodes can act as clients or servers.
Unlocking Productivity: Leveraging the Potential of Copilot in Microsoft 365, a presentation by Christoforos Vlachos, Senior Solutions Manager – Modern Workplace, Uni Systems
Full-RAG: A modern architecture for hyper-personalizationZilliz
Mike Del Balso, CEO & Co-Founder at Tecton, presents "Full RAG," a novel approach to AI recommendation systems, aiming to push beyond the limitations of traditional models through a deep integration of contextual insights and real-time data, leveraging the Retrieval-Augmented Generation architecture. This talk will outline Full RAG's potential to significantly enhance personalization, address engineering challenges such as data management and model training, and introduce data enrichment with reranking as a key solution. Attendees will gain crucial insights into the importance of hyperpersonalization in AI, the capabilities of Full RAG for advanced personalization, and strategies for managing complex data integrations for deploying cutting-edge AI solutions.
Climate Impact of Software Testing at Nordic Testing DaysKari Kakkonen
My slides at Nordic Testing Days 6.6.2024
Climate impact / sustainability of software testing discussed on the talk. ICT and testing must carry their part of global responsibility to help with the climat warming. We can minimize the carbon footprint but we can also have a carbon handprint, a positive impact on the climate. Quality characteristics can be added with sustainability, and then measured continuously. Test environments can be used less, and in smaller scale and on demand. Test techniques can be used in optimizing or minimizing number of tests. Test automation can be used to speed up testing.
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
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:
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.
Building Production Ready Search Pipelines with Spark and MilvusZilliz
Spark is the widely used ETL tool for processing, indexing and ingesting data to serving stack for search. Milvus is the production-ready open-source vector database. In this talk we will show how to use Spark to process unstructured data to extract vector representations, and push the vectors to Milvus vector database for search serving.
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024Neo4j
Neha Bajwa, Vice President of Product Marketing, Neo4j
Join us as we explore breakthrough innovations enabled by interconnected data and AI. Discover firsthand how organizations use relationships in data to uncover contextual insights and solve our most pressing challenges – from optimizing supply chains, detecting fraud, and improving customer experiences to accelerating drug discoveries.
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/
DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
Diese Themen werden behandelt
- Reduzierung der Lizenzkosten durch Auffinden und Beheben von Fehlkonfigurationen und überflüssigen Konten
- Wie funktionieren CCB- und CCX-Lizenzen wirklich?
- Verstehen des DLAU-Tools und wie man es am besten nutzt
- Tipps für häufige Problembereiche, wie z. B. Team-Postfächer, Funktions-/Testbenutzer usw.
- Praxisbeispiele und Best Practices zum sofortigen Umsetzen
Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!SOFTTECHHUB
As the digital landscape continually evolves, operating systems play a critical role in shaping user experiences and productivity. The launch of Nitrux Linux 3.5.0 marks a significant milestone, offering a robust alternative to traditional systems such as Windows 11. This article delves into the essence of Nitrux Linux 3.5.0, exploring its unique features, advantages, and how it stands as a compelling choice for both casual users and tech enthusiasts.
Removing Uninteresting Bytes in Software FuzzingAftab Hussain
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
Pushing the limits of ePRTC: 100ns holdover for 100 daysAdtran
At WSTS 2024, Alon Stern explored the topic of parametric holdover and explained how recent research findings can be implemented in real-world PNT networks to achieve 100 nanoseconds of accuracy for up to 100 days.
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.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/building-and-scaling-ai-applications-with-the-nx-ai-manager-a-presentation-from-network-optix/
Robin van Emden, Senior Director of Data Science at Network Optix, presents the “Building and Scaling AI Applications with the Nx AI Manager,” tutorial at the May 2024 Embedded Vision Summit.
In this presentation, van Emden covers the basics of scaling edge AI solutions using the Nx tool kit. He emphasizes the process of developing AI models and deploying them globally. He also showcases the conversion of AI models and the creation of effective edge AI pipelines, with a focus on pre-processing, model conversion, selecting the appropriate inference engine for the target hardware and post-processing.
van Emden shows how Nx can simplify the developer’s life and facilitate a rapid transition from concept to production-ready applications.He provides valuable insights into developing scalable and efficient edge AI solutions, with a strong focus on practical implementation.
2. 2
1.1 Introduction and Definition
before the mid-80s, computers were
Very expensive (hundred of thousands or even millions
of dollars)
Very slow
Not connected among themselves
after the mid-80s: two major developments
cheap and powerful microprocessor-based computers
appeared
computer networks
LANs at speeds ranging from 10 to 1000 Mbps
WANs at speed ranging from 64 Kbps to gigabits/sec
3. 3
Definition of a Distributed System
a distributed system is:
a collection of independent computers that appears to its
users as a single coherent system - computer (Tanenbaum
& Van Steen)
this definition has two aspects:
1. hardware: autonomous(independent) machines
2. software: a single system view for the users
4. 4
Why Distributed?
Resource and Data Sharing
printers, databases, multimedia servers, ...
Availability, Reliability
the loss of some instances can be hidden
Scalability, Extensibility
the system grows with demand (e.g., extra servers)
Performance
huge power (CPU, memory, ...) available
Inherent distribution, communication
organizational distribution, e-mail, video
5. 5
Problems of Distribution
Concurrency, Security
clients must not disturb each other
Privacy
e.g., when building a preference profile
unwanted communication such as spam
Partial failure
we often do not know where the error is (e.g., RPC)
Location, Migration, Replication
clients must be able to find their servers
Heterogeneity
hardware, platforms, languages, management
6. 6
1.2 Organization and Goals of a Distributed System
to support heterogeneous computers and networks and
to provide a single-system view, a distributed system is
often organized by means of a layer of software called
middleware that extends over multiple machines
a distributed system organized as middleware; note that the middleware
layer extends over multiple machines
7. 7
Goals of a distributed system: a distributed system should
easily connect users with resources (printers, computers,
storage facilities, data, files, Web pages, ...)
reasons: economics, to collaborate and exchange
information
be transparent: hide the fact that the resources and
processes are distributed across multiple computers
be open
be scalable
Transparency in a Distributed System
a distributed system that is able to present itself to users
and applications as if it were only a single computer
system is said to be transparent
8. 8
different forms of transparency in a distributed system
Transparency Description
Access Hide differences in data representation
(file naming, ...) and how a resource
is accessed
Location Hide where a resource is physically located; where
is http://www.google.com? (naming)
Migration Hide that a resource may move to another location
Relocation Hide that a resource may be moved to another
location while in use; e.g., mobile users using their
wireless laptops
Replication Hide that a resource is replicated
Concurrency Hide that a resource may be shared by several
competitive users; a resource must be left in a
consistent state
Failure Hide the failure and recovery of a resource
Persistence Hide whether a (software) resource is in memory
or on disk
9. 9
Openness in a Distributed System
A distributed system should be open
we need well-defined interfaces
Interoperability
components of different origin can communicate
Portability
components work on different platforms
Another goal of an open distributed system is that it
should be flexible and extensible;
easy to configure the system out of different components;
easy to add new components,
easy to replace existing ones
an Open Distributed System is a system that offers
services according to standard rules that describe the
syntax and semantics of those services; e.g., protocols in
networks
10. 10
Scalability in Distributed Systems
a distributed system should be scalable
Size: adding more users and resources to the system
Geographically: users and resources may be far apart
Administratively: should be easy to manage even if it
spans many administrative organizations
11. 11
Concept Example
Centralized services
Single server for all users-mostly for security
reasons
Centralized data A single on-line telephone book
Centralized algorithms Doing routing based on complete information
examples of scalability limitations
Scalability problems: performance problems caused by
limited capacity of servers and networks
Scaling Techniques
how to solve scaling problems
For geographical scalability: the problem is mainly
performance, due to the limitations in the capacity of
servers and networks
three possible solutions: hiding communication latencies,
distribution, and replication
12. 12
a. Hide Communication Latencies
Try to avoid waiting for responses to remote service
requests
let the requester do other useful job
i.e., construct requesting applications that use only
asynchronous communication instead of synchronous
communication; when a reply arrives the application is
interrupted
Good for batch processing and parallel applications but
not for interactive applications
for interactive applications, move part of the job to the
client to reduce communication; e.g. filling a form and
checking the entries
13. 13
(a) a server checking the correctness of field entries
(b) a client doing the job
14. 14
b. Distribution
e.g., DNS - Domain Name System
divide the name space into zones
for details, see later in Chapter 4 - Naming
an example of dividing the DNS name space into zones
15. 15
c. Replication
Replicate components across a distributed system to
increase availability and for load balancing, leading to
better performance
decided by the owner of a resource
caching (a special form of replication) also reduces
communication latency; decided by the user
but, caching and replication may lead to consistency
problems (see Chapter 6 - Consistency and Replication)
16. 16
1.3 Hardware and Software Concepts
Hardware Concepts
different classification schemes exist
multiprocessors - with shared memory
multicomputers - that do not share memory
can be homogeneous or heterogeneous
18. 18
a bus-based multiprocessor
Multiprocessors - Shared Memory
the shared memory has to be coherent –
the same value written by one processor must be read by another
processor
Performance problem for bus-based organization since the
bus will be overloaded as the number of processors
increases
The solution is to add a high-speed cache memory
between the processors and the bus to hold the most
recently accessed words; may result in incoherent memory
Bus-based multiprocessors are difficult to scale even with
caches
Two possible solutions: crossbar switch and omega
network
19. 19
Crossbar switch
divide memory into modules and connect them to the
processors with a crossbar switch
at every intersection, a crosspoint switch is opened and
closed to establish connection
problem: expensive; with n CPUs and n memories, n2
switches are required
20. 20
Omega network
use switches with multiple input and output lines
drawback: high latency because of several switching
stages between the CPU and memory
21. 21
Homogeneous Multicomputer Systems
also referred to as System Area Networks (SANs)
the nodes are mounted on a big rack and connected
through a high-performance network
could be bus-based or switch-based
bus-based
shared multiaccess network such as Fast Ethernet can be
used and messages are broadcasted
Performance drops highly with more than 25-100 nodes
(contention)
23. 23
Heterogeneous Multi-Computer Systems (HMCS)
most distributed systems are built on heterogeneous
multicomputer systems
the computers could be different in processor type,
memory size, architecture, power, operating system, etc.
and
I/O bandwidth and the interconnection network may be
highly are different
the distributed system provides a software layer to hide
the heterogeneity at the hardware level; i.e., provides
transparency
25. 25
Operating systems for distributed computers can be
roughly divided into two categories:
Tightly-coupled systems:
The OS tries to maintain a single, global view of the resources
Generally, referred to as distributed OSs (DOS)
Used for multiprocessors and homogeneous multi-computers
Each component dependent on the others
Manages all the resources in a distributed system,
Provides transparency (location, migration, concurrency,
replication)
Loosely-coupled systems, referred to as network OSs
(NOS)
In which, there are a collection of computers each running their
own operating system;
However, these OS work together to make their services and
resources available to others
Used for heterogeneous multi-computers
Software Concepts…….
26. Middleware:
To enhance the services of network operating
system (NOS)
To provide distribution transparency
To actually come to a distributed system
26
Software Concepts……
27. 27
System Description Main Goal
DOS
Tightly-coupled operating system for multi-
processors and homogeneous
multicomputers
Hide and manage
hardware
resources
NOS
Loosely-coupled operating system for
heterogeneous multicomputers (LAN and
WAN)
Offer local
services to remote
clients
Middleware
Additional layer atop of NOS implementing
general-purpose services
Provide
distribution
transparency
Summary of main issues
an overview of DOSs, NOSs, and middleware
28. 28
Distributed Operating Systems
Two types of Distributed systems
Multiprocessor operating system:
Manages the resources of a multiprocessor
Multicomputer operating system:
An operating system that is developed for
homogeneous multi-computers
29. 29
Uniprocessor Operating Systems:
One large kernel that handles everything (e.g., MS-
DOS, early UNIX).
Microkernel architecture: The OS kernel is
decomposed into micro kernels,
Separating applications from operating system code
through a microkernel
30. 30
More on Multiprocessor Operating Systems
Extended from uniprocessor operating systems to support
multiple processors having access to a shared memory
Problem: Consistency
Solution: using a protection mechanism (or two
synchronization mechanisms): semaphores and monitors
Semaphore: an integer with two atomic operations down
and up
Monitor: a programming language construct consisting
of procedures and variables that can be accessed only
by the procedures of the monitor;
only a single process at a time is allowed to execute
a procedure
31. 31
Multicomputer Operating Systems
general structure of a multicomputer operating system
Processors can not share memory; instead communication
is through message passing
Each node has its own
kernel for managing local resources
separate module for handling interprocessor
communication
32. 32
Distributed Shared Memory Systems
how to emulate shared memories on distributed systems to
provide a virtual shared memory
page-based distributed shared memory (DSM) - use the
virtual memory capabilities of each individual node
pages of address space distributed among four machines
33. 33
situation if page 10 is read only and replication is used
situation after CPU 1 references page 10
read-only pages can be easily replicated
34. 34
Network Operating Systems
possibly heterogeneous underlying hardware
constructed from a collection of uniprocessor systems, each
with its own operating system and connected to each other
in a computer network
general structure of a network operating system
35. 35
Services offered by network operating systems
remote login (rlogin)
remote file copy (rcp)
shared file systems through file servers
two clients and a server in a network operating system
36. 36
Middleware
A distributed operating system is not intended to handle a
collection of independent computers but provides
transparency and ease of use
A network operating system does not provide a view of a
single coherent system but is scalable and open
Combine the scalability and openness of network operating
systems and the transparency and ease of use of distributed
operating systems
this is achieved through a middleware, another layer of
software
38. 38
Item
Distributed OS Network
OS
Middleware
-based OS
Multiproc Multicomp
Degree of
transparency
Very High High Low High
Same OS on all nodes Yes Yes No No
Number of copies of
OS
1 N N N
Basis for
communication
Shared
memory
Messages Files
Model
specific
Resource management
Global,
central
Global,
distributed
Per node Per node
Scalability No Moderately Yes Varies
Openness Closed Closed Open Open
a comparison between multiprocessor operating systems,
multicomputer operating systems, network operating
systems, and middleware-based distributed systems
39. Networks
Started in 1070s
Ethernet and LAN were evented
The internet continue to be the biggest examples of distributed
systems.
Distributed systems have evolved from “LAN” based to
“Internet” based.
Telecommunication networks
Real-time systems
Flight control systems
Uber and Ride
Manufacturing automation control systems
Parallel Processing
Distributed artificial intelligence
Distributed Database Systems 39
Examples of Distributed Systems