Distributed Computing

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  1. Distributed Computing

Distributed computing involves breaking down a complex problem into smaller tasks and executing those tasks across multiple computers (nodes) working in parallel. This article provides a technical overview for newcomers to understanding and potentially implementing distributed computing solutions within our server infrastructure. This is particularly relevant for handling computationally intensive tasks like video transcoding, large data analysis, and rendering.

What is Distributed Computing?

Traditionally, computation was performed on a single, powerful machine. As problems grew in complexity, the limitations of single-machine processing became apparent. Distributed computing addresses these limitations by harnessing the collective power of multiple machines. It's not simply about having many computers; it's about coordinating them to work together as a single, cohesive system. Key concepts include:

  • Parallelism: Tasks are executed simultaneously.
  • Concurrency: Tasks *appear* to execute simultaneously, even on single-core processors through time-sharing.
  • Scalability: The system can handle increased workloads by adding more nodes.
  • Fault Tolerance: The system can continue operating even if some nodes fail.
  • Communication: Nodes must communicate to share data and coordinate tasks. This often involves networking protocols like TCP/IP.

Common Architectures

Several architectures are used in distributed computing. Here are a few prominent examples:

  • Client-Server: A central server provides resources or services to multiple clients. This is the most common architecture for web applications and databases. Our database servers frequently employ a client-server model.
  • Peer-to-Peer (P2P): Nodes share resources directly with each other without a central server. Examples include file sharing networks.
  • Cluster Computing: A group of tightly coupled computers that work together as a single system. Often used for high-performance computing.
  • Grid Computing: A distributed system that spans multiple administrative domains, often geographically dispersed. It’s used for large-scale scientific computations.

Hardware Considerations

Choosing the right hardware is crucial for successful distributed computing. The following table outlines key considerations:

Component Specification Importance
CPU Multi-core processors (e.g., Intel Xeon, AMD EPYC) High - Determines processing power.
RAM Large capacity (e.g., 64GB, 128GB or more) High - Prevents memory bottlenecks.
Storage Fast storage (e.g., SSDs, NVMe) Medium - Impacts data access speeds.
Network High-bandwidth, low-latency network (e.g., 10GbE, InfiniBand) Critical - Enables efficient communication between nodes.
Interconnect RDMA capable network cards High - Reduces CPU overhead for network communication

Software Stack

The software stack is equally important. Common components include:

  • Message Passing Interface (MPI): A standard for writing parallel programs. Used extensively in scientific computing. MPI libraries are available for various languages.
  • Apache Hadoop: A framework for distributed storage and processing of large datasets. Uses the Hadoop Distributed File System (HDFS).
  • Apache Spark: A fast, in-memory data processing engine built on top of Hadoop.
  • Kubernetes: A container orchestration platform that automates the deployment, scaling, and management of containerized applications. We are beginning to pilot Kubernetes deployments for some services.
  • Message Queues (e.g., RabbitMQ, Kafka): Enable asynchronous communication between nodes.

Network Infrastructure

A robust network is the backbone of any distributed system. Here's a breakdown of required network specifications:

Network Component Specification Importance
Network Speed 10 Gigabit Ethernet (10GbE) or higher Critical - Minimizes communication latency.
Network Topology Clos Network or similar High - Provides redundancy and scalability.
Switching Layer 3 switches with routing capabilities Medium - Enables efficient packet forwarding.
Protocols TCP/IP, UDP, RDMA Critical - Foundation of network communication.
Security Firewalls, Intrusion Detection Systems (IDS) Critical - Protects the system from unauthorized access.

Scalability and Load Balancing

Scalability is a key benefit of distributed computing. However, simply adding nodes isn't enough. We need mechanisms to distribute the workload evenly across those nodes.

  • Load Balancers: Distribute incoming requests across multiple servers. Our HAProxy configuration provides load balancing for web traffic.
  • Data Partitioning (Sharding): Divide a large dataset into smaller chunks and distribute them across multiple nodes. Used in database scaling.
  • Caching: Store frequently accessed data in memory to reduce the load on backend servers. We utilize Redis caching extensively.

Monitoring and Management

Effective monitoring and management are essential for maintaining a healthy distributed system.

Monitoring Metric Tool Importance
CPU Usage Nagios, Prometheus High - Identifies overloaded nodes.
Memory Usage Nagios, Prometheus High - Detects memory leaks and bottlenecks.
Network Latency Ping, Traceroute Medium - Measures communication performance.
Disk I/O iostat, Prometheus Medium - Monitors disk performance.
Application Logs ELK Stack (Elasticsearch, Logstash, Kibana) Critical - Provides insights into application behavior.

Security Considerations

Distributed systems introduce unique security challenges. It's vital to secure communication between nodes and protect data from unauthorized access. Consider using:

  • Encryption: Encrypt data in transit and at rest.
  • Authentication: Verify the identity of nodes and users.
  • Authorization: Control access to resources.
  • Firewalls: Protect the system from external threats. See our firewall rules documentation.


Main Page Server Administration Networking Database Administration Security Performance Tuning System Monitoring Virtualization Cloud Computing Containerization Scripting Automation Troubleshooting Capacity Planning Disaster Recovery


Intel-Based Server Configurations

Configuration Specifications Benchmark
Core i7-6700K/7700 Server 64 GB DDR4, NVMe SSD 2 x 512 GB CPU Benchmark: 8046
Core i7-8700 Server 64 GB DDR4, NVMe SSD 2x1 TB CPU Benchmark: 13124
Core i9-9900K Server 128 GB DDR4, NVMe SSD 2 x 1 TB CPU Benchmark: 49969
Core i9-13900 Server (64GB) 64 GB RAM, 2x2 TB NVMe SSD
Core i9-13900 Server (128GB) 128 GB RAM, 2x2 TB NVMe SSD
Core i5-13500 Server (64GB) 64 GB RAM, 2x500 GB NVMe SSD
Core i5-13500 Server (128GB) 128 GB RAM, 2x500 GB NVMe SSD
Core i5-13500 Workstation 64 GB DDR5 RAM, 2 NVMe SSD, NVIDIA RTX 4000

AMD-Based Server Configurations

Configuration Specifications Benchmark
Ryzen 5 3600 Server 64 GB RAM, 2x480 GB NVMe CPU Benchmark: 17849
Ryzen 7 7700 Server 64 GB DDR5 RAM, 2x1 TB NVMe CPU Benchmark: 35224
Ryzen 9 5950X Server 128 GB RAM, 2x4 TB NVMe CPU Benchmark: 46045
Ryzen 9 7950X Server 128 GB DDR5 ECC, 2x2 TB NVMe CPU Benchmark: 63561
EPYC 7502P Server (128GB/1TB) 128 GB RAM, 1 TB NVMe CPU Benchmark: 48021
EPYC 7502P Server (128GB/2TB) 128 GB RAM, 2 TB NVMe CPU Benchmark: 48021
EPYC 7502P Server (128GB/4TB) 128 GB RAM, 2x2 TB NVMe CPU Benchmark: 48021
EPYC 7502P Server (256GB/1TB) 256 GB RAM, 1 TB NVMe CPU Benchmark: 48021
EPYC 7502P Server (256GB/4TB) 256 GB RAM, 2x2 TB NVMe CPU Benchmark: 48021
EPYC 9454P Server 256 GB RAM, 2x2 TB NVMe

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⚠️ *Note: All benchmark scores are approximate and may vary based on configuration. Server availability subject to stock.* ⚠️

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