7.1 Distributed system architectures
3 min read•august 15, 2024
Distributed system architectures form the backbone of modern computing, enabling resource sharing and collaboration across networks. This section explores the key characteristics, trade-offs, and architectural approaches that shape these systems, from centralized to decentralized models.
We'll dive into the role of , which acts as a crucial intermediary layer in distributed systems. Understanding these concepts is essential for designing scalable, fault-tolerant, and efficient distributed systems that power today's interconnected digital world.
Distributed Systems: Key Characteristics
Resource Sharing and Openness
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- Distributed systems comprise multiple autonomous computational entities (nodes) communicating and coordinating to achieve a common goal
- Resource sharing enables efficient utilization of computing resources across the network (hardware, software, and data)
- Openness allows system extension and modification through standardized interfaces and protocols
- Concurrency permits multiple processes to execute simultaneously across different nodes enhancing overall system performance
Scalability and Fault Tolerance
- accommodates growth in users, resources, or geographical distribution without significant performance degradation
- mechanisms enable continued system functioning in the presence of failures ensuring reliability and availability
- hides the complexity of the distributed nature from users and application programmers presenting the system as a single coherent unit
- Examples of fault tolerance mechanisms include replication (data mirroring) and redundancy (backup servers)
Centralized vs Decentralized vs Hybrid Architectures
Centralized and Decentralized Systems
- Centralized architectures rely on a single central server or cluster to manage all resources and operations
- Offers simplicity but potentially creates a single point of failure
- Provides stronger consistency and easier management
- Decentralized architectures distribute control and decision-making across multiple nodes
- Enhances fault tolerance and scalability but increases complexity
- Often employ peer-to-peer (P2P) networks where nodes have equal roles and responsibilities
- Examples of centralized systems include traditional client-server models (central database server)
- Examples of decentralized systems include blockchain networks (Bitcoin) and distributed file sharing (BitTorrent)
Hybrid Architectures and Considerations
- Hybrid architectures combine elements of both centralized and decentralized approaches
- Aims to balance advantages and disadvantages of centralized and decentralized systems
- May use hierarchical structure with centralized control at higher levels and decentralized operations at lower levels
- Choice between architectures depends on factors such as:
- System requirements
- Scalability needs
- Fault tolerance requirements
- Geographical distribution of resources and users
- Examples of hybrid systems include content delivery networks (CDNs) and platforms (AWS)
Trade-offs in Distributed Systems
Performance and Scalability
- Performance measured by metrics such as throughput, latency, and resource utilization
- Affected by network communication overhead and
- Scalability refers to system's ability to handle increased load by adding resources
- May impact performance due to increased coordination and communication requirements
- Improving scalability often involves partitioning data and services
- Complicates maintaining consistency across the system
- Examples of performance optimization techniques include caching (Redis) and load balancing (Nginx)
Fault Tolerance and Consistency
- Fault tolerance mechanisms enhance system reliability but introduce overhead and complexity
- Potentially affecting performance and scalability
- states impossibility of simultaneously providing consistency, availability, and partition tolerance
- Trade-offs must be made based on system requirements
- Increasing fault tolerance through replication may improve availability but negatively impact consistency
- Introduces additional network traffic
- Examples of fault tolerance strategies include primary-backup replication (MySQL replication) and sharding (MongoDB)
Middleware in Distributed Systems
Functions and Communication Paradigms
- Middleware acts as intermediary layer between distributed applications and underlying network infrastructure
- Provides abstraction and facilitates communication
- Key functions include:
- Providing uniform programming model
- Masking heterogeneity of hardware, operating systems, and network protocols
- Supports various communication paradigms:
- Remote procedure calls (RPC)
- Message-oriented middleware (MOM)
- Publish-subscribe systems
- Implements mechanisms for ensuring reliability, security, and quality of service in distributed communications
- Examples of middleware include gRPC (RPC framework) and Apache Kafka (message broker)
Types and Role in Transparency
- Common types of middleware:
- Object-oriented middleware (CORBA)
- Service-oriented middleware (web services)
- Message-oriented middleware (RabbitMQ)
- Provides services for naming, discovery, and location of resources and services within distributed system
- Plays crucial role in implementing transparency in distributed systems
- Hides complexities of distribution from application developers and users
- Examples of middleware-enabled transparency include distributed file systems (NFS) and distributed databases (Cassandra)
Key Terms to Review (18)
Asynchronous communication: Asynchronous communication is a method of data exchange where the sender and receiver do not need to be active or engaged at the same time. This allows for greater flexibility and efficiency, as messages can be sent and received independently, without waiting for immediate responses. It is particularly useful in distributed systems where components may operate on different schedules or time zones.
Blockchain technology: Blockchain technology is a decentralized digital ledger system that records transactions across multiple computers in such a way that the registered transactions cannot be altered retroactively. This technology ensures data integrity and security, making it highly relevant in various applications such as cryptocurrencies, smart contracts, and supply chain management.
CAP Theorem: The CAP Theorem states that in a distributed data store, it is impossible to simultaneously guarantee all three of the following properties: Consistency, Availability, and Partition Tolerance. This theorem highlights the trade-offs that must be made in the design of distributed systems, where achieving strong consistency may come at the cost of availability during network partitions.
Client-server architecture: Client-server architecture is a distributed computing model that separates the client, which requests services, from the server, which provides those services. This model enhances resource management and enables scalability by allowing multiple clients to interact with a single server or multiple servers concurrently. It forms the backbone of many networked applications and services, enabling efficient communication and data processing between users and systems.
Cloud computing: Cloud computing is a technology that allows users to access and store data and applications over the internet instead of on local servers or personal computers. It enables on-demand availability of computing resources, like servers and storage, which can be rapidly provisioned and released with minimal management effort. This flexibility supports various distributed system architectures and requires effective coordination and synchronization among distributed resources to ensure seamless performance.
Distributed file system: A distributed file system is a file system that allows multiple users on different computers to share files and storage resources across a network. It manages data across several machines while providing users with the illusion of a single, unified file system, enabling seamless access and collaboration irrespective of where the data physically resides.
Eventual consistency: Eventual consistency is a consistency model used in distributed systems that ensures that, if no new updates are made to a given data item, eventually all accesses to that item will return the last updated value. This model prioritizes availability and partition tolerance over immediate consistency, meaning that there may be a period where different replicas of the data return different values until they converge. This concept is critical in designing systems that operate across multiple nodes, ensuring that data remains accessible while synchronizing updates asynchronously.
Fault Tolerance: Fault tolerance is the ability of a system to continue functioning correctly in the event of a failure of some of its components. It is crucial for maintaining reliability, availability, and resilience in systems, especially when multiple elements are interconnected. By implementing redundancy and error detection mechanisms, systems can handle failures gracefully and ensure uninterrupted service, which is vital for both performance and user satisfaction.
Load Balancing: Load balancing is the process of distributing workloads across multiple computing resources, such as servers or processors, to optimize resource use, minimize response time, and avoid overload on any single resource. This technique enhances performance and reliability by ensuring that no single server becomes a bottleneck, thereby improving the overall efficiency of systems in various contexts.
Middleware: Middleware is software that acts as an intermediary layer between different software applications, enabling them to communicate and manage data efficiently. It plays a crucial role in distributed system architectures by facilitating the integration of various components, allowing them to work together seamlessly across diverse environments and platforms.
Peer-to-peer architecture: Peer-to-peer architecture is a decentralized network design where each participant (or 'peer') can act as both a client and a server, sharing resources directly with one another without the need for a central server. This design enhances scalability and resilience, as peers communicate directly to fulfill requests, allowing for more efficient resource sharing and reduced bottlenecks associated with traditional client-server models.
Resource allocation: Resource allocation refers to the process of distributing available resources among various tasks or processes to optimize performance and ensure that system requirements are met. This concept is essential for managing the limited resources of a system, including CPU time, memory, and I/O devices, while minimizing contention and maximizing efficiency.
REST (Representational State Transfer): REST is an architectural style for designing networked applications, focusing on stateless communication and the use of standard HTTP methods to manipulate resources. It emphasizes scalability, simplicity, and a uniform interface that allows different systems to interact over the web. REST is a key component of modern web services, making it easier for applications to communicate with one another in a distributed system environment.
Rpc (remote procedure call): RPC, or remote procedure call, is a powerful protocol that allows a program to execute code on a different address space or server as if it were a local procedure call. This capability enables seamless communication between distributed systems, allowing one program to request services from another program located on a different machine. RPC abstracts the complexities of the network communication, making it easier for developers to build distributed applications without having to manage the underlying details of data transmission and serialization.
Scalability: Scalability is the ability of a system, network, or process to handle a growing amount of work or its potential to accommodate growth. It involves the capability to increase resources and improve performance without requiring significant changes to the overall architecture. This concept is essential in various contexts where demand can fluctuate or expand over time, impacting efficiency, performance, and cost-effectiveness.
Synchronous communication: Synchronous communication refers to real-time interactions where participants communicate simultaneously, allowing for immediate feedback and responses. This type of communication is essential in distributed system architectures, as it enables coordination and collaboration among different components or processes that may be located in various geographical locations.
Task scheduling: Task scheduling refers to the method of deciding which tasks or processes will be executed by a computer system at a given time. This process is critical for managing resources efficiently and ensuring that multiple tasks can run smoothly without conflicts. It involves prioritizing tasks, allocating CPU time, and determining the order of execution, which directly impacts system performance and responsiveness.
Transparency: Transparency refers to the property of a distributed system that makes it appear as a single coherent system to users and applications, regardless of the underlying complexities. This concept emphasizes hiding the details of resource location, access, and replication from users, allowing them to interact with the system without needing to understand its distributed nature. Key features related to transparency include location transparency, migration transparency, replication transparency, and concurrency transparency.