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

@@ Line 1: / Line 1: @@
-'''Distributed Systems''' is a field of computer science that focuses on the design, implementation, and management of systems that operate on multiple interconnected computers. These systems work together to achieve a common goal and present themselves as a unified system to users. The study of distributed systems encompasses a wide array of applications and technologies, including the Internet, cloud computing, and peer-to-peer networks.
+'''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.
-The concept of distributed systems dates back to the early development of computer networks in the 1960s and 1970s. The pioneering work on the Advanced Research Projects Agency Network (ARPANET), which was the precursor to the modern Internet, laid the groundwork for future distributed computing. The emergence of networked personal computers in the 1980s further accelerated interest in distributed systems, as these machines could communicate and share resources over local networks.
+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.
-Theoretical foundations for distributed systems were established by researchers like Leslie Lamport, who introduced key concepts such as consensus algorithms, and Barbara Liskov, who contributed to the development of reliable distributed systems through practical implementations. As technology progressed into the 1990s and early 2000s, the rise of the Internet necessitated the development of more robust and scalable distributed systems to handle increasing amounts of data and user interactions.
+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.
-Throughout the late 20th and early 21st centuries, the field matured significantly, with advancements in technologies such as middleware, which facilitates communication and management among distributed components. The deployment of service-oriented architectures (SOA) and cloud computing frameworks marked significant milestones in the evolution of distributed systems, enabling organizations to leverage off-premises computing resources and scale applications dynamically.
 == 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.
-The architecture of distributed systems can be categorized into several prominent models, each with unique characteristics, advantages, and use cases. Understanding these architectural styles is essential for the design and implementation of distributed systems.
+=== 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.
-=== Client-Server Model ===
-In the client-server model, system components are divided into two main roles: clients and servers. Clients are entities that request services, while servers provide those services. This model is fundamental in many applications, including web services, databases, and enterprise applications. The client-server approach allows for centralized management of resources on servers, but it can lead to bottlenecks if many clients simultaneously access the server.
-=== Peer-to-Peer (P2P) Model ===
+=== 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.
-The peer-to-peer model allows all nodes in the system to act both as clients and servers. Each node, or peer, can initiate requests as well as respond to requests from other nodes. This design leads to improved scalability and fault tolerance, as the system does not rely on a central server. P2P systems are widely used in file sharing, blockchain networks, and collaborative applications.
+=== 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.
-=== Multi-tier Architecture ===
+=== 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.
-Multi-tier architecture is an extension of the client-server model that introduces additional layers between clients and servers. Typically, between the client interface and the data storage layer, an application server layer provides the logic needed for data processing. This separation of concerns allows for more manageable, scalable, and secure applications. Many modern web applications adopt this architecture for improved performance and maintainability.
-=== Microservices Architecture ===
-Microservices architecture is an evolution of the multi-tier model, in which applications are developed as a suite of small, independently deployable services. Each service typically addresses a specific business capability and communicates through well-defined APIs. This approach fosters agility, as teams can work on different services simultaneously, deploy independently, and scale components based on demand.
 == Implementation or Applications ==
+The implementation of distributed systems spans various domains, including cloud computing, distributed databases, content delivery networks, and microservices architecture.
-Distributed systems have found widespread application in various domains, fundamentally transforming how organizations operate and deliver services. The following sections explore significant applications and implications of distributed systems across different industries.
+=== 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.
-=== Internet and Cloud Computing ===
-The Internet is perhaps the most extensive example of a distributed system, characterized by millions of interconnected devices that communicate and share information. Cloud computing platforms, such as Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP), leverage distributed systems to provide scalable computing resources and services. Users can deploy applications, store data, and access computing power without investing in physical infrastructure.
-Cloud computing models can be categorized into Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS), each providing distinct levels of abstraction and management. These platforms rely on distributed systems to dynamically allocate resources, balance load, and maintain high availability, ensuring continuity of service.
 === 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.
-Distributed databases are designed to store data across multiple physical or virtual locations, allowing for greater resilience, scalability, and performance compared to traditional relational databases. These systems ensure data consistency and durability, even in the presence of failures. Some notable distributed database technologies include Apache Cassandra, Google Bigtable, and Amazon DynamoDB. These databases implement various consistency models, such as eventual consistency or strong consistency, which influence how data is accessed and modified across the distributed network.
+=== 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.
-=== Distributed File Systems ===
+=== 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.
-Distributed file systems (DFS) allow multiple clients to access files stored across various nodes. These systems manage the distribution, redundancy, replication, and consistency of files, making them accessible and fault-tolerant. Well-known implementations of distributed file systems include Google File System (GFS) and Hadoop Distributed File System (HDFS), which support the storage and processing of large datasets for analytics and big data applications.
-=== Internet of Things (IoT) ===
-The Internet of Things (IoT) comprises interconnected devices that share data and communicate over the Internet. IoT applications rely on distributed systems to process vast amounts of data generated by sensors and devices. These systems can perform real-time analytics, enabling insights and actions based on data from various sources. Examples include smart home devices, industrial automation, and health monitoring systems.
 == Real-world Examples ==
+Distributed systems have been implemented in various industries, showcasing their versatility and effectiveness in solving complex problems.
-Several large-scale systems exemplify the principles and implementations of distributed systems. These case studies highlight the various challenges and benefits of distributed architectures.
+=== 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 Search ===
-Google Search is a prominent example of a highly optimized distributed system. It utilizes a distributed architecture for crawling, indexing, and serving search results from billions of web pages. Google's infrastructure employs thousands of servers across data centers worldwide, ensuring low latency and high fault tolerance. Through efficient algorithms, such as PageRank, and techniques like sharding and replication, Google effectively manages the massive scale and complexity of search queries.
 === 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.
-Blockchain is a decentralized technology that enables distributed systems to maintain a tamper-resistant ledger across multiple nodes. Each block in the chain stores a set of transactions, and the network applies consensus algorithms to validate changes. The most well-known implementation of blockchain technology is Bitcoin, which relies on a peer-to-peer network of nodes to secure and verify transactions without a central authority.
+=== 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.
-=== Content Delivery Networks (CDN) ===
+=== 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.
-Content Delivery Networks serve as complex distributed systems that cache content across various geographical locations to optimize delivery times and reduce latency. By distributing copies of static and dynamic content, CDNs can ensure that users have quick access to the data they request from servers closest to their location. Prominent examples of CDNs include Akamai, Cloudflare, and Amazon CloudFront.
-=== Distributed Artificial Intelligence (AI) ===
-Distributed systems have also made significant contributions to the field of artificial intelligence. Distributed AI refers to systems that process data and execute complex AI algorithms across multiple nodes, enabling faster computations and processing of large datasets. Techniques such as federated learning allow multiple entities to collaboratively train machine learning models while preserving data privacy by keeping data localized.
 == Criticism or Limitations ==
+Despite their numerous advantages, distributed systems face a host of challenges and limitations that can impact their effectiveness.
-Despite their numerous benefits, distributed systems are not without limitations and criticisms. Several critical challenges affect the performance, reliability, and usability of distributed architectures.
+=== 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.
-=== Complexity ===
-The design and implementation of distributed systems introduce significant complexity compared to centralized systems. Developers must consider various factors, such as network latency, data consistency, fault tolerance, and resource management. This complexity may lead to prolonged development cycles and difficulties in debugging and maintenance.
-=== Security Vulnerabilities ===
-Distributed systems are inherently more susceptible to security issues compared to centralized systems. The interconnected nature of distributed networks presents multiple points of attack. Risks such as data breaches, replay attacks, and denial-of-service attacks can threaten system integrity. Implementing robust security measures, such as encryption and access control, becomes paramount to mitigate these vulnerabilities.
-=== Latency and Bandwidth Limitations ===
-While distributed systems provide scalability, they also face challenges related to latency and bandwidth. Communication between distributed nodes is subject to network delay and congestion, potentially impacting the performance of applications. Furthermore, data transfer across wide-area networks can consume significant bandwidth, leading to increased costs and slower response times.
+=== 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.
-=== Data Management Challenges ===
+=== 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.
-Managing data across distributed systems is complex, particularly concerning consistency and reliability. Inconsistent data writes can lead to discrepancies and conflicts, especially when multiple nodes are involved in data modification. Distributed databases often employ various consistency models, but choosing the correct model for a specific application may require careful consideration.
+=== 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 ==
-* [[Computer Networking]]
 * [[Cloud Computing]]
-* [[Peer-to-Peer]]
+* [[Microservices]]
-* [[Distributed Database]]
+* [[Peer-to-Peer Networking]]
-* [[Fault Tolerance in Complex Systems]]
+* [[Distributed Computing]]
-* [[Middleware]]
+* [[Blockchain]]
-* [[Machine Learning]]
 == References ==
-* [http://research.google.com/archive/gfs.html Google File System - Google Research]
 * [https://aws.amazon.com/ Amazon Web Services]
-* [https://azure.microsoft.com/ Microsoft Azure]
+* [https://azure.microsoft.com/en-us/ Microsoft Azure]
 * [https://cloud.google.com/ Google Cloud Platform]
-* [https://hadoop.apache.org/docs/r1.2.1/hdfs_design.html Hadoop Distributed File System Design]
+* [https://hadoop.apache.org/ Apache Hadoop]
-* [https://www.akamai.com/ Akamai Technologies]
+* [https://www.mongodb.com/ MongoDB]
-* [https://www.cloudflare.com/ Cloudflare]
+* [https://cassandra.apache.org/ Apache Cassandra]
-* [https://bitcoin.org/ Bitcoin Official Site]
+* [https://blockchain.info/ Blockchain.info]
-[[Category:Distributed systems]]
+[[Category:Distributed computing]]
 [[Category:Computer science]]
-[[Category:Software engineering]]
+[[Category:Systems architecture]]