Distributed Systems: Difference between revisions

Revision as of 08:15, 6 July 2025

Introduction

A Distributed System is a model in computing where components located on networked computers communicate and coordinate their actions by passing messages. The components interact with one another in order to achieve a common goal, despite being situated in different physical locations. This architecture contrasts significantly with traditional centralized systems, where a single server or entity manages all the resources and computations.

Distributed systems have become increasingly significant in recent years, fueled by the proliferation of cloud computing, the Internet of Things (IoT), and large-scale data analytics. They provide several advantages, including improved performance, scalability, reliability, and resource sharing. However, designing and managing such systems poses unique challenges, including synchronization, fault tolerance, and security.

History or Background

The concept of distributed systems can be traced back to the 1970s, during the early development of networked computing. Early efforts focused on enabling communication between mainframe computers and terminals over local area networks (LANs). Pioneering work by researchers such as Vinton Cerf and Robert Kahn led to the development of the Transmission Control Protocol (TCP) and the Internet Protocol (IP), fundamental technologies that underlie modern distributed systems.

In the 1980s, key advancements emerged in the form of Client-Server models, where client computers request services from a centralized server. However, this architecture still maintained points of vulnerability and could become bottlenecks in performance. The introduction of peer-to-peer (P2P) networking in the late 1990s further democratized distributed systems, allowing nodes to operate both as clients and servers, thus enhancing decentralization and resilience.

Throughout the 2000s and 2010s, significant developments in distributed computing included the rise of cloud computing platforms like Amazon Web Services (AWS) and Google Cloud, enabling businesses to leverage distributed resources without investing in physical infrastructure. Technologies such as containerization (e.g., Docker) and orchestration (e.g., Kubernetes) further propelled the adoption of distributed systems by simplifying their deployment and management across various environments.

Design or Architecture

Architectural Models

Distributed systems can be categorized into various architectural models, each suitable for different applications and requirements:

Client-Server Model: This traditional model involves clients requesting resources or services from a centralized server. The server handles requests and returns results. It is widely used in various applications, including web services and databases.
Peer-to-Peer Model: In the P2P model, each participant (node) has equal privileges and can serve as both a client and a server. This model promotes decentralized resource sharing, as seen in file-sharing networks and blockchain technology.
Multi-tier Architecture: This architecture separates applications into multiple tiers, such as presentation, application logic, and data storage. Each tier operates independently, allowing for better scalability and manageability. Commonly used in web applications, this model supports dynamic resource allocation and load balancing.
Microservices Architecture: Microservices split applications into small, loosely coupled services that can be developed, deployed, and scaled independently. Each service typically runs in its own container, communicating over a network, usually via representational state transfer (REST) or messaging protocols.

Communication Models

Effective communication among distributed components is crucial. Several communication models are commonly employed:

Message Passing: Components exchange messages asynchronously, allowing for decoupled interactions. This model enhances reliability and scalability but introduces complexities in ensuring message order and delivery.
Remote Procedure Calls (RPC): RPC allows a program to execute a procedure on a different address space as if it were a local call. It abstracts the complexities of network communication, simplifying development.
Shared Memory: This model allows processes to communicate by accessing a shared memory space. While efficient, it typically requires additional mechanisms to manage consistency and concurrency.

Consistency Models

Ensuring data consistency across distributed components is vital. Several models define how consistency is managed:

Strong Consistency: Guarantees that all users see the same data at the same time, maintaining a single source of truth.
Eventual Consistency: Allows for temporary inconsistencies, with the guarantee that all updates will propagate and eventually lead to a consistent state.
Causal Consistency: Ensures that operations that are causally related are seen by processes in the same order, allowing for more flexibility than strict consistency models.

Usage and Implementation

Distributed systems find application across various domains, from business and healthcare to scientific research and entertainment.

Business Applications

In the business sector, distributed systems support services such as:

Cloud Computing: Providers like AWS, Microsoft Azure, and Google Cloud allow businesses to access scalable computing resources over the internet, supporting various applications from web hosting to big data analytics.
Enterprise Resource Planning (ERP): Many organizations employ distributed systems to integrate business processes across departments, enabling data sharing and collaboration.
Microservices for Web Applications: Companies are increasingly adopting microservices architectures to build scalable and resilient applications. This approach facilitates continuous integration and deployment, promoting agile development practices.

Scientific Research

Distributed systems play a pivotal role in scientific computations, enabling researchers to analyze vast datasets and perform complex simulations. Examples include:

Grid Computing: A type of distributed computing that harnesses the unused processing power of computers in a network, commonly used in large-scale scientific problems such as climate modeling and genetic research.
Cloud-based Data Analysis: Services like Google BigQuery allow scientists to run complex analyses across massive datasets without managing physical infrastructure, accelerating research and enabling collaboration.

Internet of Things (IoT)

With the exponential growth of IoT devices, distributed systems underpin the operation of smart devices and their communication. They facilitate:

Data Collection and Analysis: Distributed systems aggregate and analyze data from numerous IoT sensors, supporting real-time decision-making.
Autonomous Systems: Technologies such as self-driving cars rely on distributed systems to communicate between vehicles, sensors, and cloud services for processing and navigation.

Real-world Examples or Comparisons

Numerous real-world systems exemplify the principles of distributed computing:

Google Distributed File System (GFS)

GFS is a distributed file system developed by Google to manage large amounts of data across commodity hardware. It employs a master-slave architecture, where a single master node manages metadata and multiple chunk servers store actual data chunks. GFS provides fault tolerance and scalability, allowing Google to manage massive datasets effectively.

Apache Hadoop

Hadoop is an open-source framework that allows for distributed storage and processing of large datasets using the MapReduce programming model. It utilizes a distributed file system (HDFS) to store data across a cluster of computers, enabling efficient data processing and analysis, particularly suited for big data applications.

Blockchain Technology

Blockchain serves as an innovative application of distributed systems, where a network of nodes maintains a shared and immutable ledger of transactions. Its decentralized architecture enhances security and trust among participants without relying on a central authority, making it particularly relevant for cryptocurrencies such as Bitcoin.

Content Delivery Networks (CDN)

CDNs like Akamai and Cloudflare distribute content across multiple geographically dispersed servers. By caching content close to end-users, CDNs enhance load times and reduce latency, illustrating the principles of distributed systems in improving user experience in web applications.

Criticism or Controversies

While distributed systems offer numerous benefits, they are not without challenges and criticism:

Complexity

The inherent complexity of designing, implementing, and maintaining distributed systems can be daunting. Developers must address various issues, including network latency, message ordering, and fault tolerance. This complexity can lead to increased development costs and operational challenges.

Security Concerns

Distributed systems are exposed to a wider range of security threats compared to centralized systems. Issues such as data breaches, malicious attacks, and unauthorized access can complicate the safeguarding of sensitive information. Ensuring security in a distributed architecture requires continuous monitoring and advanced strategies to mitigate risks.

Performance Trade-offs

While distributed systems can theoretically scale to handle increasing workloads, performance may degrade due to factors such as network latency and communication overhead. Understanding and optimizing these trade-offs is crucial for effective system performance.

Dependence on Network Reliability

The performance and reliability of distributed systems are significantly influenced by network conditions. Network failures can lead to service outages and data inconsistencies, necessitating robust fault tolerance mechanisms.

Influence or Impact

The impact of distributed systems on modern computing and society is profound:

Technological Advancement

Distributed systems have fostered the development of many innovative technologies that define contemporary computing paradigms, such as cloud services, big data analytics, and artificial intelligence.

Economic Impact

By enabling scalable solutions, distributed systems have facilitated the growth of startups and enterprises across various industries. Organizations can leverage distributed resources to minimize costs while expanding their service offerings and reach.

Collaboration and Research

Distributed systems have transformed how researchers collaborate, share data, and conduct experiments. Technologies such as cloud computing and distributed databases allow for joint research efforts across institutions and geographical boundaries, fostering breakthroughs in various fields.

Future Trends

As the demand for scalable and flexible computing increases, the evolution of distributed systems continues. Emerging trends include:

Edge Computing: Bringing computation and data storage closer to the source of data generation, reducing latency and bandwidth usage.
Serverless Architectures: Allowing developers to build applications without managing server infrastructure, enabling them to focus on writing code.
Decentralized Finance (DeFi): Developing financial systems using distributed ledger technology, offering alternatives to traditional banking methods.

References

@@ Line 1: / Line 1: @@
-= Distributed Systems =
 == Introduction ==
-A '''distributed system''' is a model in which components located on networked computers communicate and coordinate their actions only by passing messages. The components interact with one another in order to achieve a common goal. Distributed systems are characterized by their ability to share resources and information across multiple nodes, making them crucial for a variety of applications, from cloud computing and large-scale web services to big data processing and Internet of Things (IoT).
+A '''Distributed System''' is a model in computing where components located on networked computers communicate and coordinate their actions by passing messages. The components interact with one another in order to achieve a common goal, despite being situated in different physical locations. This architecture contrasts significantly with traditional centralized systems, where a single server or entity manages all the resources and computations.
-Distributed systems can be implemented on various architectures, ranging from homogeneous setups where all nodes perform similar tasks to heterogeneous frameworks where nodes have different capabilities. Their design often focuses on enhancing reliability, scalability, and performance while minimizing latency and ensuring fault tolerance.
+Distributed systems have become increasingly significant in recent years, fueled by the proliferation of cloud computing, the Internet of Things (IoT), and large-scale data analytics. They provide several advantages, including improved performance, scalability, reliability, and resource sharing. However, designing and managing such systems poses unique challenges, including synchronization, fault tolerance, and security.
 == History or Background ==
-The concept of distributed systems began to take shape in the late 1960s and early 1970s with the development of time-sharing systems. Early examples include the ARPANET, which laid the groundwork for modern networking and distributed computing. The theoretical foundations were further explored by researchers such as Leslie Lamport, who contributed significant work on distributed algorithms and consensus problems.
+The concept of distributed systems can be traced back to the 1970s, during the early development of networked computing. Early efforts focused on enabling communication between mainframe computers and terminals over local area networks (LANs). Pioneering work by researchers such as Vinton Cerf and Robert Kahn led to the development of the Transmission Control Protocol (TCP) and the Internet Protocol (IP), fundamental technologies that underlie modern distributed systems.
-In the following decades, distributed systems evolved as innovations in computer networking emerged. The 1980s and 1990s witnessed advancements in client-server architectures and the advent of the World Wide Web. Technologies such as Remote Procedure Calls (RPC) and message-oriented middleware became popular, facilitating communication between distributed components.
+In the 1980s, key advancements emerged in the form of Client-Server models, where client computers request services from a centralized server. However, this architecture still maintained points of vulnerability and could become bottlenecks in performance. The introduction of peer-to-peer (P2P) networking in the late 1990s further democratized distributed systems, allowing nodes to operate both as clients and servers, thus enhancing decentralization and resilience.
-The rise of cloud computing in the 2000s significantly impacted distributed systems, as service-oriented architectures allowed for flexible and scalable solutions. The emergence of frameworks like Hadoop and Apache Spark changed the landscape for big data processing, transforming how organizations manage large volumes of data across distributed environments.
+Throughout the 2000s and 2010s, significant developments in distributed computing included the rise of cloud computing platforms like Amazon Web Services (AWS) and Google Cloud, enabling businesses to leverage distributed resources without investing in physical infrastructure. Technologies such as containerization (e.g., Docker) and orchestration (e.g., Kubernetes) further propelled the adoption of distributed systems by simplifying their deployment and management across various environments.
 == Design or Architecture ==
-Distributed systems can take several architectural forms, which influence their performance and scalability. Key architectural styles include:
+=== Architectural Models ===
+Distributed systems can be categorized into various architectural models, each suitable for different applications and requirements:
-=== Client-Server Architecture ===
+* '''Client-Server Model''': This traditional model involves clients requesting resources or services from a centralized server. The server handles requests and returns results. It is widely used in various applications, including web services and databases.
-In a client-server model, clients request resources or services from centralized servers. The server manages and responds to multiple client requests, providing a synchronous interaction model. This architecture is widely used in web applications, where web browsers act as clients.
+* '''Peer-to-Peer Model''': In the P2P model, each participant (node) has equal privileges and can serve as both a client and a server. This model promotes decentralized resource sharing, as seen in file-sharing networks and blockchain technology.
+* '''Multi-tier Architecture''': This architecture separates applications into multiple tiers, such as presentation, application logic, and data storage. Each tier operates independently, allowing for better scalability and manageability. Commonly used in web applications, this model supports dynamic resource allocation and load balancing.
-=== Peer-to-Peer (P2P) Architecture ===
+* '''Microservices Architecture''': Microservices split applications into small, loosely coupled services that can be developed, deployed, and scaled independently. Each service typically runs in its own container, communicating over a network, usually via representational state transfer (REST) or messaging protocols.
-In P2P systems, each participant (or node) acts both as a client and a server. This decentralized approach promotes resource sharing and eliminates single points of failure. Notable examples include file-sharing networks like BitTorrent and cryptocurrencies like Bitcoin.
-=== Microservices Architecture ===
+=== Communication Models ===
-Microservices architecture decomposes monolithic applications into smaller, loosely coupled services that communicate over a network. This design enhances modularity, allowing teams to develop, deploy, and scale services independently, which aligns well with continuous integration and continuous deployment practices.
+Effective communication among distributed components is crucial. Several communication models are commonly employed:
+* '''Message Passing''': Components exchange messages asynchronously, allowing for decoupled interactions. This model enhances reliability and scalability but introduces complexities in ensuring message order and delivery.
+* '''Remote Procedure Calls (RPC)''': RPC allows a program to execute a procedure on a different address space as if it were a local call. It abstracts the complexities of network communication, simplifying development.
+* '''Shared Memory''': This model allows processes to communicate by accessing a shared memory space. While efficient, it typically requires additional mechanisms to manage consistency and concurrency.
-=== Publish-Subscribe Model ===
+=== Consistency Models ===
-This event-driven architecture decouples the production of information from its consumption. Publishers send messages to a message broker, which then forwards them to subscribers interested in specific topics, fostering scalability and resilience in information dissemination.
+Ensuring data consistency across distributed components is vital. Several models define how consistency is managed:
+* '''Strong Consistency''': Guarantees that all users see the same data at the same time, maintaining a single source of truth.
-=== Event Sourcing and CQRS ===
+* '''Eventual Consistency''': Allows for temporary inconsistencies, with the guarantee that all updates will propagate and eventually lead to a consistent state.
-Event Sourcing is an architecture that persistently stores the state changes of an application as events, while Command Query Responsibility Segregation (CQRS) separates the read and write operations of an application. Together, they facilitate scalability and provide a clear audit trail.
+* '''Causal Consistency''': Ensures that operations that are causally related are seen by processes in the same order, allowing for more flexibility than strict consistency models.
 == Usage and Implementation ==
-Distributed systems find applications across various domains, including but not limited to:
+Distributed systems find application across various domains, from business and healthcare to scientific research and entertainment.
-=== Cloud Computing ===
+=== Business Applications ===
-Distributed systems underpin cloud computing, enabling service providers to offer elastic resources and applications over the internet. Through virtualization and containerization technologies, cloud architectures can dynamically allocate compute resources based on demand.
+In the business sector, distributed systems support services such as:
+* '''Cloud Computing''': Providers like AWS, Microsoft Azure, and Google Cloud allow businesses to access scalable computing resources over the internet, supporting various applications from web hosting to big data analytics.
+* '''Enterprise Resource Planning (ERP)''': Many organizations employ distributed systems to integrate business processes across departments, enabling data sharing and collaboration.
+* '''Microservices for Web Applications''': Companies are increasingly adopting microservices architectures to build scalable and resilient applications. This approach facilitates continuous integration and deployment, promoting agile development practices.
-=== Distributed Databases ===
+=== Scientific Research ===
-Databases such as Google Spanner and Apache Cassandra utilize distributed architecture to store and manage data across multiple nodes while maintaining high availability and consistency, achieving scalability even in the face of node failures.
+Distributed systems play a pivotal role in scientific computations, enabling researchers to analyze vast datasets and perform complex simulations. Examples include:
+* '''Grid Computing''': A type of distributed computing that harnesses the unused processing power of computers in a network, commonly used in large-scale scientific problems such as climate modeling and genetic research.
-=== Content Delivery Networks (CDNs) ===
+* '''Cloud-based Data Analysis''': Services like Google BigQuery allow scientists to run complex analyses across massive datasets without managing physical infrastructure, accelerating research and enabling collaboration.
-CDNs distribute content across geographically dispersed servers to optimize delivery speed and reduce latency. By caching content closer to end-users, CDNs enhance performance for streaming, gaming, and web applications.
 === Internet of Things (IoT) ===
-The proliferation of IoT devices necessitates distributed systems for managing vast networks of interconnected devices. By distributing processing power across edge devices, IoT architectures can minimize latency and bandwidth usage.
+With the exponential growth of IoT devices, distributed systems underpin the operation of smart devices and their communication. They facilitate:
+* '''Data Collection and Analysis''': Distributed systems aggregate and analyze data from numerous IoT sensors, supporting real-time decision-making.
-=== Distributed Ledger Technology ===
+* '''Autonomous Systems''': Technologies such as self-driving cars rely on distributed systems to communicate between vehicles, sensors, and cloud services for processing and navigation.
-Distributed ledger systems like blockchain decentralize record-keeping using cryptographic techniques to ensure data integrity and transparency. They have applications in finance, supply chain management, and healthcare.
 == Real-world Examples or Comparisons ==
-Distributed systems exhibit numerous implementations across industries. Key real-world examples include:
+Numerous real-world systems exemplify the principles of distributed computing:
-=== Google File System (GFS) ===
+=== Google Distributed File System (GFS) ===
-GFS is a distributed file system designed for large-scale data processing. It allows multiple clients to read and write data concurrently while managing replicas for fault tolerance and high availability.
+GFS is a distributed file system developed by Google to manage large amounts of data across commodity hardware. It employs a master-slave architecture, where a single master node manages metadata and multiple chunk servers store actual data chunks. GFS provides fault tolerance and scalability, allowing Google to manage massive datasets effectively.
 === Apache Hadoop ===
-Hadoop is a widely used open-source framework for processing and storing large datasets in a distributed manner. It consists of the Hadoop Distributed File System (HDFS) and a processing engine called MapReduce.
+Hadoop is an open-source framework that allows for distributed storage and processing of large datasets using the MapReduce programming model. It utilizes a distributed file system (HDFS) to store data across a cluster of computers, enabling efficient data processing and analysis, particularly suited for big data applications.
-=== Amazon Web Services (AWS) ===
-AWS exemplifies cloud service delivery through a vast array of distributed services such as EC2 for compute power and S3 for scalable storage. The architecture allows on-demand access to virtual resources.
-=== Microsoft Azure ===
+=== Blockchain Technology ===
-Like AWS, Microsoft Azure provides a platform for deploying distributed applications and services in the cloud. Its architecture enables users to build, test, and deploy in a highly scalable environment.
+Blockchain serves as an innovative application of distributed systems, where a network of nodes maintains a shared and immutable ledger of transactions. Its decentralized architecture enhances security and trust among participants without relying on a central authority, making it particularly relevant for cryptocurrencies such as Bitcoin.
-=== Kubernetes ===
+=== Content Delivery Networks (CDN) ===
-Kubernetes is an open-source orchestration system for automating the deployment, scaling, and management of containerized applications in a distributed environment. It allows for resource optimization and enhances service availability.
+CDNs like Akamai and Cloudflare distribute content across multiple geographically dispersed servers. By caching content close to end-users, CDNs enhance load times and reduce latency, illustrating the principles of distributed systems in improving user experience in web applications.
 == Criticism or Controversies ==
-While distributed systems offer many advantages, they also face criticisms and challenges that warrant consideration:
+While distributed systems offer numerous benefits, they are not without challenges and criticism:
 === Complexity ===
-The design and implementation of distributed systems can be significantly more complex than single-node systems. Challenges such as network latency, synchronization issues, and failure handling require specialized knowledge and robust tools.
+The inherent complexity of designing, implementing, and maintaining distributed systems can be daunting. Developers must address various issues, including network latency, message ordering, and fault tolerance. This complexity can lead to increased development costs and operational challenges.
 === Security Concerns ===
-Distributed systems expose various security vulnerabilities, including data interception during transmission and unauthorized access to services. The decentralized nature complicates enforcement of security policies and monitoring of malicious activities.
+Distributed systems are exposed to a wider range of security threats compared to centralized systems. Issues such as data breaches, malicious attacks, and unauthorized access can complicate the safeguarding of sensitive information. Ensuring security in a distributed architecture requires continuous monitoring and advanced strategies to mitigate risks.
-=== Debugging and Maintenance ===
+=== Performance Trade-offs ===
-Identifying and resolving issues in distributed systems can be difficult due to their inherent complexity and asynchronous nature. Tools and methodologies for monitoring different nodes and environments continuously are essential for effective management.
+While distributed systems can theoretically scale to handle increasing workloads, performance may degrade due to factors such as network latency and communication overhead. Understanding and optimizing these trade-offs is crucial for effective system performance.
-=== Consensus and Coordination ===
+=== Dependence on Network Reliability ===
-Distributed systems often face challenges in achieving consensus among nodes, particularly in the presence of network partitions. Protocols like Paxos and Raft have been developed to address these issues, but they add further complexity to the system.
+The performance and reliability of distributed systems are significantly influenced by network conditions. Network failures can lead to service outages and data inconsistencies, necessitating robust fault tolerance mechanisms.
 == Influence or Impact ==
-The development and proliferation of distributed systems have had a profound impact on computing, influencing both academic research and practical implementations:
+The impact of distributed systems on modern computing and society is profound:
-=== Research and Theory ===
+=== Technological Advancement ===
-Distributed systems remain a critical area of research in computer science, with ongoing studies addressing theoretical aspects like fault tolerance and consistency models. New paradigms, including artificial intelligence and machine learning, are continually being integrated into distributed frameworks.
+Distributed systems have fostered the development of many innovative technologies that define contemporary computing paradigms, such as cloud services, big data analytics, and artificial intelligence.
-=== Economic and Business Transformation ===
+=== Economic Impact ===
-Distributed systems have enabled new business models and economic opportunities, particularly in sectors like fintech, e-commerce, and cloud services. Companies leverage distributed architectures to deliver enhanced customer experiences through speed and reliability.
+By enabling scalable solutions, distributed systems have facilitated the growth of startups and enterprises across various industries. Organizations can leverage distributed resources to minimize costs while expanding their service offerings and reach.
-=== Societal Changes ===
+=== Collaboration and Research ===
-The ubiquity of distributed systems has facilitated global connectivity and communication. Technologies such as social media and cloud-based collaboration tools have transformed how individuals and organizations interact and share information.
+Distributed systems have transformed how researchers collaborate, share data, and conduct experiments. Technologies such as cloud computing and distributed databases allow for joint research efforts across institutions and geographical boundaries, fostering breakthroughs in various fields.
 === Future Trends ===
-Emerging technologies, including edge computing and quantum computing, are poised to further evolve distributed systems. These innovations promise to enhance the resilience, scalability, and performance of distributed architectures.
+As the demand for scalable and flexible computing increases, the evolution of distributed systems continues. Emerging trends include:
+* '''Edge Computing''': Bringing computation and data storage closer to the source of data generation, reducing latency and bandwidth usage.
+* '''Serverless Architectures''': Allowing developers to build applications without managing server infrastructure, enabling them to focus on writing code.
+* '''Decentralized Finance (DeFi)''': Developing financial systems using distributed ledger technology, offering alternatives to traditional banking methods.
 == See also ==
-* [[Cloud Computing]]
+* [[Cloud computing]]
+* [[Peer-to-peer]]
 * [[Microservices]]
+* [[Grid computing]]
 * [[Blockchain]]
-* [[Paxos]]
-* [[Kubernetes]]
-* [[Distributed Algorithms]]
 == References ==
-* [https://www.microsoft.com/en-us/research/project/distributed-systems-distributed-computing/ Microsoft Research: Distributed Systems]
+* [https://aws.amazon.com Amazon Web Services]
-* [https://hadoop.apache.org/ Apache Hadoop Official Site]
+* [https://azure.microsoft.com Microsoft Azure]
-* [https://aws.amazon.com/ Amazon Web Services Official Site]
+* [https://hadoop.apache.org Apache Hadoop]
-* [https://azure.microsoft.com/ Microsoft Azure Official Site]
+* [https://www.google.com/intl/en_us/drive/ Google Drive]
-* [https://kubernetes.io/ Kubernetes Official Site]
+* [https://www.cloudflare.com Cloudflare]
-* [https://www.oreilly.com/library/view/concurrent-programming-in/9780132939210/ O'Reilly Media: Concurrent Programming in Java]
+* [https://www.akamai.com Akamai Technologies]
-* [https://queue.acm.org/detail.cfm?id=945136 ACM Queue: The Challenges of Distributed Systems]
+* [https://www.ibm.com/cloud/learn/distributed-systems IBM Cloud: Distributed Systems]
+* [https://www.oracle.com/solutions/cloud/distributed-systems.html Oracle: Distributed Systems Overview]
+* [https://en.wikipedia.org/wiki/Distributed_computing Wikipedia: Distributed Computing]
+* [https://www.investopedia.com/terms/d/decentralized.asp Investopedia: Decentralized Finance]
 [[Category:Distributed computing]]
 [[Category:Computer science]]
-[[Category:Computer networks]]
+[[Category:Systems architecture]]