Distributed Systems: Difference between revisions

Latest revision as of 09:49, 6 July 2025

Distributed Systems is a field within computer science and engineering that encompasses a collection of independent entities that appear to applications as a single coherent system. These entities may include multiple computers, or nodes, that communicate and coordinate their actions by passing messages to one another. Contrary to centralized systems, where a single node or server performs all processing and serves all clients, distributed systems leverage the power of multiple interconnected systems, promoting scalability, robustness, and resource sharing.

Background or History

The concept of distributed systems is not a recent development; it can be traced back to the early days of computer science. The origins of distributed computing can be linked to the ARPANET project in the late 1960s and early 1970s, which was one of the first packet-switching networks. As the internet evolved and computers became more interconnected, the need for a standardized model of distributed communication became evident. Key theoretical advancements, such as those proposed by Leslie Lamport in his work on the Paxos consensus algorithm in the late 1970s, further guided the development of distributed systems.

Throughout the 1980s and 1990s, rapid advancements in networking technologies spurred the evolution of distributed systems research. Notably, the development of remote procedure calls (RPC) allowed programs on one computer to invoke services executed on another machine, giving rise to a range of distributed applications. The rise of client-server architecture marked significant progress, enabling applications to scale by distributing workloads efficiently across numerous clients and servers.

By the turn of the 21st century, grid computing and cloud computing emerged, firmly entrenching distributed systems in practical applications across various industries. This new wave of distributed systems allowed for leverage of computational resources over expansive networks, effectively addressing problems such as resource management, load balancing, and fault tolerance.

Architecture or Design

Distributed systems are characterized by various architectural models that determine how the components within the system interact with each other. Generally, there are three primary architectural styles for distributed systems: client-server, peer-to-peer, and multi-tier architectures.

Client-Server Architecture

In the client-server model, a dedicated server hosts resources or services that are accessed by multiple client nodes. The clients typically initiate requests that the server processes and responds to. A notable benefit of this model is the centralized management of resources, which simplifies data consistency and security protocols. However, this architecture may face bottlenecks if the server becomes overloaded, negatively impacting performance.

Peer-to-Peer Architecture

Peer-to-peer (P2P) systems distribute workloads among participants, allowing nodes to act both as clients and servers. This decentralized approach can improve resource utilization and resilience against failures, as each node can contribute resources to the system. P2P systems are commonly associated with file-sharing protocols and cryptocurrencies, yet they also present challenges such as security vulnerabilities and maintaining data consistency across numerous nodes.

Multi-Tier Architecture

Multi-tier architecture introduces additional layers between clients and servers. In this model, the system is divided into three or more tiers, with each tier responsible for specific functions within the application. Commonly, these tiers include the presentation layer, business logic layer, and data layer. This separation of concerns allows for easier management of the system while promoting scalability and flexibility. Multi-tier architectures are widely utilized in web applications and enterprise software systems.

Communication Mechanisms

Effective communication is a cornerstone of distributed systems, and numerous protocols facilitate interactions among nodes. These mechanisms can be categorized as synchronous and asynchronous communication. Synchronous communication necessitates that a node wait for a response before proceeding, which can hinder system performance if delays occur. Conversely, asynchronous communication allows nodes to continue processing while waiting for responses, thus enhancing efficiency. Various messaging protocols, such as Message Queue Telemetry Transport (MQTT), Advanced Message Queuing Protocol (AMQP), and the more ubiquitous HTTP, are often utilized to facilitate these interactions.

Implementation or Applications

The implementation of distributed systems spans various domains, including cloud computing, distributed databases, content delivery networks, and microservices architecture.

Cloud Computing

Cloud computing has redefined the allocation of computational resources. It operates on the principles of distributed systems, offering multiple services that can be accessed over the internet. Major cloud service providers, including Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP), maintain large-scale distributed systems that provide computing power, storage, and application services to users worldwide. These platforms leverage the advantages of elasticity and resource pooling, enabling organizations to scale services according to demand.

Distributed Databases

Distributed databases are a critical application of distributed systems. They allow data to be stored across multiple nodes, enhancing both performance and reliability. This architecture supports horizontal scaling, which is essential for handling vast amounts of data. Notable distributed databases include MongoDB, Cassandra, and Amazon DynamoDB, which implement various consistency models to ensure data reliability. The deployment of distributed databases enables seamless data access across different geographical regions, promoting fault tolerance and high availability.

Content Delivery Networks (CDNs)

CDNs utilize distributed systems to enhance the efficiency and speed of content delivery over the internet. By caching content across numerous geographical locations, CDNs ensure that users experience minimal latency and faster load times. This approach is particularly beneficial for media streaming and online services, where performance is critical. Major CDN providers, such as Akamai and Cloudflare, operate extensive networks of servers that store duplicated content, improving both redundancy and access speed.

Microservices Architecture

The microservices architectural style emphasizes the development of applications as independent services that can communicate through APIs. This distributed approach facilitates continuous development, deployment, and scaling of software applications. By breaking down a monolithic application into smaller, manageable components, organizations can efficiently allocate resources and enhance productivity. Tools and frameworks, such as Spring Boot and Kubernetes, have emerged to streamline the implementation of microservices-based architectures.

Real-world Examples

Distributed systems have been implemented in various industries, showcasing their versatility and effectiveness in solving complex problems.

Distributed File Systems

Distributed file systems, like Hadoop Distributed File System (HDFS) and Google File System (GFS), exemplify effective storage solutions that distribute data across multiple nodes. These systems ensure high availability and fault tolerance while allowing users to operate on massive datasets distributed across clusters of machines. Organizations frequently employ these systems for big data processing and analytics tasks, taking advantage of their scalability.

Blockchain Technology

Blockchain technology operates on principles of distributed systems, utilizing a decentralized ledger to verify and store transactions across multiple nodes. This architecture underpins cryptocurrencies, such as Bitcoin and Ethereum, enabling peer-to-peer transactions without the need for intermediaries. The consensus mechanisms employed by blockchain networks, including proof of work and proof of stake, ensure data integrity and security while demonstrating the application of distributed systems in fostering trust among participants.

Distributed Computing Frameworks

Frameworks like Apache Spark and Apache Flink provide robust platforms for distributed data processing. They enable the execution of complex data analytics tasks across clusters of computers, harnessing their combined computational power. These frameworks support fault tolerance and dynamic scaling, significantly boosting performance and enabling organizations to process large volumes of data in real time.

Industrial IoT Systems

In the domain of the Internet of Things (IoT), distributed systems facilitate the connectivity and coordination of numerous smart devices. Industrial IoT systems employ distributed architectures to gather and analyze data from various sensors and devices, enabling real-time monitoring and decision-making. These applications have proven invaluable in manufacturing, where they enhance operational efficiency and predictive maintenance, reducing downtime and costs.

Criticism or Limitations

Despite their numerous advantages, distributed systems face a host of challenges and limitations that can impact their effectiveness.

Complexity and Debugging

One notable challenge associated with distributed systems is the inherent complexity of designing, implementing, and managing such architectures. As the number of nodes increases, the difficulty of monitoring and troubleshooting also escalates. Issues such as network partitions, data inconsistency, and system failures can arise, often complicating debugging processes. Effective debugging tools and logging mechanisms are essential to mitigate these challenges and ensure system reliability.

Latency and Performance Overheads

Distributed systems can suffer from latency due to the time taken for messages to travel across networks. Additionally, performance overheads may result from the necessity of coordination among nodes, particularly in tightly-coupled systems that require frequent communication. Strategies such as data locality, caching, and reducing the granularity of interactions are often employed to minimize latency and optimize performance.

Security Concerns

Security is a critical concern in distributed systems, as the increased number of nodes and communication pathways provides more potential attack vectors for malicious actors. Ensuring data integrity, confidentiality, and authentication across distributed environments poses significant challenges. Best practices, such as employing encryption, access control, and network segmentation, are vital to safeguard distributed systems against evolving security threats.

Consistency Models

The trade-off between consistency, availability, and partition tolerance, known as the CAP theorem, underscores a major limitation of distributed systems. Given that it is impossible to achieve perfect consistency in a distributed environment, developers must make informed choices regarding how to maintain data accuracy, especially when operating under network partitions. The variety of consistency models, such as eventual consistency and strong consistency, each present specific benefits and drawbacks tailored to different application requirements.

References

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-== Distributed Systems ==
+'''Distributed Systems''' is a field within computer science and engineering that encompasses a collection of independent entities that appear to applications as a single coherent system. These entities may include multiple computers, or nodes, that communicate and coordinate their actions by passing messages to one another. Contrary to centralized systems, where a single node or server performs all processing and serves all clients, distributed systems leverage the power of multiple interconnected systems, promoting scalability, robustness, and resource sharing.
-A '''distributed system''' is a model in which 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. Distributed systems can be categorized based on their architecture, networking topology, and consistency models, amongst other factors. They are increasingly important in computing, as they facilitate the development of applications that are more scalable, resilient, and accessible.
+== Background or History ==
+The concept of distributed systems is not a recent development; it can be traced back to the early days of computer science. The origins of distributed computing can be linked to the ARPANET project in the late 1960s and early 1970s, which was one of the first packet-switching networks. As the internet evolved and computers became more interconnected, the need for a standardized model of distributed communication became evident. Key theoretical advancements, such as those proposed by Leslie Lamport in his work on the Paxos consensus algorithm in the late 1970s, further guided the development of distributed systems.
-== Introduction ==
+Throughout the 1980s and 1990s, rapid advancements in networking technologies spurred the evolution of distributed systems research. Notably, the development of remote procedure calls (RPC) allowed programs on one computer to invoke services executed on another machine, giving rise to a range of distributed applications. The rise of client-server architecture marked significant progress, enabling applications to scale by distributing workloads efficiently across numerous clients and servers.
-Distributed systems are prevalent in modern computing and form the backbone of many major applications and services. They provide key advantages such as resource sharing, fault tolerance, scalability, and improved performance. In a distributed system, components located on multiple networked computers work together to perform tasks, effectively giving the appearance of a single coherent system to the user. The emergence of cloud computing, web services, and peer-to-peer systems has further propelled the relevance and use of distributed systems.
+By the turn of the 21st century, grid computing and cloud computing emerged, firmly entrenching distributed systems in practical applications across various industries. This new wave of distributed systems allowed for leverage of computational resources over expansive networks, effectively addressing problems such as resource management, load balancing, and fault tolerance.
-While distributed systems may seem similar to cluster computing or grid computing, they present unique challenges in terms of coordination, data consistency, and security. As technology advances and the demand for effective data management increases, distributed systems will continue to evolve and adapt.
+== Architecture or Design ==
+Distributed systems are characterized by various architectural models that determine how the components within the system interact with each other. Generally, there are three primary architectural styles for distributed systems: client-server, peer-to-peer, and multi-tier architectures.
-== History ==
+=== Client-Server Architecture ===
+In the client-server model, a dedicated server hosts resources or services that are accessed by multiple client nodes. The clients typically initiate requests that the server processes and responds to. A notable benefit of this model is the centralized management of resources, which simplifies data consistency and security protocols. However, this architecture may face bottlenecks if the server becomes overloaded, negatively impacting performance.
-The concept of distributed systems has its roots in the early days of computing when multiple computers were connected via networks to share resources. The genesis of distributed systems can be traced back to the following milestones:
+=== Peer-to-Peer Architecture ===
-* During the 1970s, early efforts such as the ARPANET showcased the potential of connecting computers remotely, facilitating communication and collaboration among researchers.
+Peer-to-peer (P2P) systems distribute workloads among participants, allowing nodes to act both as clients and servers. This decentralized approach can improve resource utilization and resilience against failures, as each node can contribute resources to the system. P2P systems are commonly associated with file-sharing protocols and cryptocurrencies, yet they also present challenges such as security vulnerabilities and maintaining data consistency across numerous nodes.
-* By the 1980s, the introduction of distributed file systems and early database management systems allowed organizations to manage data across multiple nodes, albeit with significant limitations in performance and scalability.
-* The 1990s saw the emergence of more sophisticated mechanisms such as remote procedure calls (RPC) and various protocols for inter-process communication, which laid the groundwork for modern distributed systems.
-* The late 1990s and early 2000s witnessed the rise of web-based applications and the shift towards service-oriented architectures enabling distributed computing on a global scale.
-* Recent developments in cloud computing and microservices have further transformed the landscape of distributed systems, allowing for highly scalable and fault-tolerant applications.
-== Design and Architecture ==
+=== Multi-Tier Architecture ===
+Multi-tier architecture introduces additional layers between clients and servers. In this model, the system is divided into three or more tiers, with each tier responsible for specific functions within the application. Commonly, these tiers include the presentation layer, business logic layer, and data layer. This separation of concerns allows for easier management of the system while promoting scalability and flexibility. Multi-tier architectures are widely utilized in web applications and enterprise software systems.
-The design of a distributed system can vary greatly based on the intended use case, architecture, and protocols employed. It typically involves several key components and design patterns:
+=== Communication Mechanisms ===
+Effective communication is a cornerstone of distributed systems, and numerous protocols facilitate interactions among nodes. These mechanisms can be categorized as synchronous and asynchronous communication. Synchronous communication necessitates that a node wait for a response before proceeding, which can hinder system performance if delays occur. Conversely, asynchronous communication allows nodes to continue processing while waiting for responses, thus enhancing efficiency. Various messaging protocols, such as Message Queue Telemetry Transport (MQTT), Advanced Message Queuing Protocol (AMQP), and the more ubiquitous HTTP, are often utilized to facilitate these interactions.
-=== 1. Components ===
+== Implementation or Applications ==
+The implementation of distributed systems spans various domains, including cloud computing, distributed databases, content delivery networks, and microservices architecture.
-Distributed systems consist of multiple autonomous components that work collaboratively. The primary component types include:
+=== Cloud Computing ===
-* '''Clients''': Users or systems that request services from servers.
+Cloud computing has redefined the allocation of computational resources. It operates on the principles of distributed systems, offering multiple services that can be accessed over the internet. Major cloud service providers, including Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP), maintain large-scale distributed systems that provide computing power, storage, and application services to users worldwide. These platforms leverage the advantages of elasticity and resource pooling, enabling organizations to scale services according to demand.
-* '''Servers''': Components that provide services to clients, typically by processing requests and returning results.
-* '''Middleware''': Software that lies between client applications and server resources, aiding communication and data management.
-=== 2. Architectural Models ===
+=== Distributed Databases ===
+Distributed databases are a critical application of distributed systems. They allow data to be stored across multiple nodes, enhancing both performance and reliability. This architecture supports horizontal scaling, which is essential for handling vast amounts of data. Notable distributed databases include MongoDB, Cassandra, and Amazon DynamoDB, which implement various consistency models to ensure data reliability. The deployment of distributed databases enables seamless data access across different geographical regions, promoting fault tolerance and high availability.
-Several architectural models guide the design of distributed systems:
+=== Content Delivery Networks (CDNs) ===
-* '''Client-Server Architecture''': In this model, clients request resources or services from centralized servers which provide responses. This is the most common distributed system architecture.
+CDNs utilize distributed systems to enhance the efficiency and speed of content delivery over the internet. By caching content across numerous geographical locations, CDNs ensure that users experience minimal latency and faster load times. This approach is particularly beneficial for media streaming and online services, where performance is critical. Major CDN providers, such as Akamai and Cloudflare, operate extensive networks of servers that store duplicated content, improving both redundancy and access speed.
-* '''Peer-to-Peer (P2P) Architecture''': All nodes have equal responsibilities and can act as both client and server. This model promotes resource sharing and decentralization.
-* '''Multi-tier Architecture''': An extension of the client-server model that separates different functions (such as presentation, application processing, and database management) into different layers.
-=== 3. Communication Protocols ===
+=== Microservices Architecture ===
+The microservices architectural style emphasizes the development of applications as independent services that can communicate through APIs. This distributed approach facilitates continuous development, deployment, and scaling of software applications. By breaking down a monolithic application into smaller, manageable components, organizations can efficiently allocate resources and enhance productivity. Tools and frameworks, such as Spring Boot and Kubernetes, have emerged to streamline the implementation of microservices-based architectures.
-The choice of communication protocols significantly impacts the performance and reliability of a distributed system. Common protocols include:
-* '''Remote Procedure Call (RPC)''': Allows a program to cause a procedure to execute in another address space.
-* '''Message Queuing Protocols (e.g., MQTT, AMQP)''': Provides a mechanism for distributed applications to communicate asynchronously.
-* '''HTTP/REST''': A stateless communication model often used in web services, which allows clients and servers to exchange data over the internet.
-=== 4. Consistency Models ===
-Data consistency is a critical aspect of distributed systems, often dictated by the chosen consistency model such as:
-* '''Strong Consistency''': Guarantees that all accesses will return the latest data after an update.
-* '''Eventual Consistency''': Allows for temporary inconsistencies, with the guarantee that all replicas will become consistent eventually.
-* '''Causal Consistency''': Ensures that operations that are causally related are seen by all processes in the same order.
-=== 5. Fault Tolerance and Replication ===
-To ensure reliability, distributed systems often incorporate fault tolerance mechanisms, such as data replication, consensus algorithms (e.g., Paxos, Raft), and failure detection strategies. These methods allow systems to continue functioning despite the presence of hardware or software failures.
-== Usage and Implementation ==
-Distributed systems find applications across various domains, including:
-* '''Cloud Computing''': Offers on-demand access to a network of servers, allowing scalable and flexible resource utilization.
-* '''Big Data Processing''': Frameworks like Hadoop and Spark leverage distributed systems to process large data sets efficiently.
-* '''Content Delivery Networks (CDNs)''': Distribute content geographically to improve access speed and redundancy by caching data across multiple nodes.
-* '''Blockchain''': A distributed ledger technology that ensures secure peer-to-peer transactions without a central authority.
-The implementation of distributed systems requires a deep understanding of both the technical challenges involved and the operational requirements of the applications being developed. Developers must consider aspects such as network latency, data locality, and synchronization to achieve optimal performance.
-=== Challenges in Implementation ===
-Implementing distributed systems introduces several challenges, including:
-* '''Network Partitioning''': Communication failures that lead to split-brain scenarios can compromise data consistency.
-* '''Latency Issues''': Network delays can impact system responsiveness, particularly in real-time applications.
-* '''Complex Debugging''': The distributed nature of the system can complicate troubleshooting and error detection.
-Addressing these challenges requires robust designs, continuous monitoring, and efficient resource management.
 == Real-world Examples ==
+Distributed systems have been implemented in various industries, showcasing their versatility and effectiveness in solving complex problems.
-=== 1. Google Distributed Systems ===
+=== Distributed File Systems ===
+Distributed file systems, like Hadoop Distributed File System (HDFS) and Google File System (GFS), exemplify effective storage solutions that distribute data across multiple nodes. These systems ensure high availability and fault tolerance while allowing users to operate on massive datasets distributed across clusters of machines. Organizations frequently employ these systems for big data processing and analytics tasks, taking advantage of their scalability.
-Google has developed a range of distributed systems including:
-* '''Google File System (GFS)''': Designed to provide high-throughput access to large datasets using a distributed file system architecture.
-* '''Bigtable''': A distributed storage system for managing structured data, designed to scale to petabytes across thousands of servers.
-* '''MapReduce''': A programming model designed for distributed processing of large data sets across clusters.
-=== 2. Amazon Web Services (AWS) ===
-AWS provides cloud computing services that leverage distributed system architectures, including:
-* '''Amazon S3 (Simple Storage Service)''': Allows storage and retrieval of any amount of data at any time, featuring high availability and scalability.
-* '''Amazon DynamoDB''': A fully managed NoSQL database service that delivers fast and predictable performance with seamless scalability.
-* '''AWS Lambda''': A serverless compute service that automatically manages the underlying infrastructure, allowing developers to execute code in response to events.
-=== 3. Apache Hadoop Ecosystem ===
+=== Blockchain Technology ===
+Blockchain technology operates on principles of distributed systems, utilizing a decentralized ledger to verify and store transactions across multiple nodes. This architecture underpins cryptocurrencies, such as Bitcoin and Ethereum, enabling peer-to-peer transactions without the need for intermediaries. The consensus mechanisms employed by blockchain networks, including proof of work and proof of stake, ensure data integrity and security while demonstrating the application of distributed systems in fostering trust among participants.
-Apache Hadoop is a suite of tools designed for distributed storage and processing of large data sets. Its ecosystem includes:
+=== Distributed Computing Frameworks ===
-* '''Hadoop Distributed File System (HDFS)''': A distributed file system that provides high-throughput access to application data.
+Frameworks like Apache Spark and Apache Flink provide robust platforms for distributed data processing. They enable the execution of complex data analytics tasks across clusters of computers, harnessing their combined computational power. These frameworks support fault tolerance and dynamic scaling, significantly boosting performance and enabling organizations to process large volumes of data in real time.
-* '''YARN (Yet Another Resource Negotiator)''': A resource management layer that allocates system resources to applications running in a Hadoop cluster.
-* '''MapReduce''': A programming model for processing large data sets in parallel across a Hadoop Cluster.
-== Criticism and Controversies ==
+=== Industrial IoT Systems ===
+In the domain of the Internet of Things (IoT), distributed systems facilitate the connectivity and coordination of numerous smart devices. Industrial IoT systems employ distributed architectures to gather and analyze data from various sensors and devices, enabling real-time monitoring and decision-making. These applications have proven invaluable in manufacturing, where they enhance operational efficiency and predictive maintenance, reducing downtime and costs.
-Despite their advantages, distributed systems face criticism and several controversies, particularly regarding issues of security, data privacy, and inefficiency:
+== Criticism or Limitations ==
-* '''Security Concerns''': The distributed nature of these systems can expose them to a variety of attacks such as Distributed Denial of Service (DDoS), making security a paramount concern.
+Despite their numerous advantages, distributed systems face a host of challenges and limitations that can impact their effectiveness.
-* '''Data Privacy''': The handling of sensitive data across multiple nodes raises concerns about unauthorized access and data breaches.
-* '''Complexity and Cost''': The implementation and maintenance of distributed systems can be complex and costly, especially for small enterprises without dedicated resources.
-Understanding these criticisms is crucial for developers and organizations to address potential pitfalls effectively.
+=== Complexity and Debugging ===
+One notable challenge associated with distributed systems is the inherent complexity of designing, implementing, and managing such architectures. As the number of nodes increases, the difficulty of monitoring and troubleshooting also escalates. Issues such as network partitions, data inconsistency, and system failures can arise, often complicating debugging processes. Effective debugging tools and logging mechanisms are essential to mitigate these challenges and ensure system reliability.
-== Influence and Impact ==
+=== Latency and Performance Overheads ===
+Distributed systems can suffer from latency due to the time taken for messages to travel across networks. Additionally, performance overheads may result from the necessity of coordination among nodes, particularly in tightly-coupled systems that require frequent communication. Strategies such as data locality, caching, and reducing the granularity of interactions are often employed to minimize latency and optimize performance.
-Distributed systems have profoundly influenced the landscape of modern computing, driving innovations across various fields:
+=== Security Concerns ===
-* They have enabled businesses to increase scalability and reliability in their operations.
+Security is a critical concern in distributed systems, as the increased number of nodes and communication pathways provides more potential attack vectors for malicious actors. Ensuring data integrity, confidentiality, and authentication across distributed environments poses significant challenges. Best practices, such as employing encryption, access control, and network segmentation, are vital to safeguard distributed systems against evolving security threats.
-* The rise of cloud computing, driven by distributed systems, has reshaped the IT industry, affecting how organizations manage resources and data.
-* Innovations in big data technologies, such as Apache Spark and Kafka, are heavily reliant on distributed system paradigms.
-* The development of blockchain technologies represents a push towards more decentralized, secure, and transparent systems.
-The ongoing evolution of distributed systems is expected to contribute further to advancements in computing, facilitating new application possibilities and addressing global challenges.
+=== Consistency Models ===
+The trade-off between consistency, availability, and partition tolerance, known as the CAP theorem, underscores a major limitation of distributed systems. Given that it is impossible to achieve perfect consistency in a distributed environment, developers must make informed choices regarding how to maintain data accuracy, especially when operating under network partitions. The variety of consistency models, such as eventual consistency and strong consistency, each present specific benefits and drawbacks tailored to different application requirements.
 == See also ==
 * [[Cloud Computing]]
-* [[Cluster Computing]]
-* [[Grid Computing]]
 * [[Microservices]]
-* [[Byzantine Fault Tolerance]]
-* [[Distributed Ledger Technology]]
 * [[Peer-to-Peer Networking]]
+* [[Distributed Computing]]
+* [[Blockchain]]
 == References ==
-* [https://www.microsoft.com/cloud-computing] - Microsoft Cloud Computing
+* [https://aws.amazon.com/ Amazon Web Services]
-* [https://www.ibm.com/cloud/learn/distributed-systems] - IBM's Overview of Distributed Systems
+* [https://azure.microsoft.com/en-us/ Microsoft Azure]
-* [https://www.digitalocean.com/community/tutorials/what-is-cloud-computing] - DigitalOcean's Guide to Cloud Computing
+* [https://cloud.google.com/ Google Cloud Platform]
-* [https://hadoop.apache.org/] - Apache Hadoop Official Website
+* [https://hadoop.apache.org/ Apache Hadoop]
-* [https://aws.amazon.com/what-is-aws/] - Introduction to Amazon Web Services
+* [https://www.mongodb.com/ MongoDB]
-* [https://research.google/pubs/archive/87533.pdf] - "The Google File System," by Sanjay Ghemawat et al.
+* [https://cassandra.apache.org/ Apache Cassandra]
+* [https://blockchain.info/ Blockchain.info]
 [[Category:Distributed computing]]
 [[Category:Computer science]]
-[[Category:Systems theory]]
+[[Category:Systems architecture]]