Distributed Computational Process

A Distributed Computational Process is a computational process that executes across multiple computing nodes with coordinated interaction.

• AKA: Distributed Process, Distributed Computation, Multi-Node Process, Distributed Execution Process.
• Context:
  • It can typically achieve Horizontal Scalability through node addition.
  • It can typically require Network Communication Protocols via message passing.
  • It can often face Distributed System Challenges through coordination complexity.
  • It can often benefit from Computational Orchestration Frameworks via workflow management.
  • It can range from being a Loosely-Coupled Distributed Computational Process to being a Tightly-Coupled Distributed Computational Process, depending on its interaction frequency.
  • It can range from being a Homogeneous Distributed Computational Process to being a Heterogeneous Distributed Computational Process, depending on its node uniformity.
  • It can range from being a Synchronous Distributed Computational Process to being an Asynchronous Distributed Computational Process, depending on its timing requirement.
  • It can range from being a Master-Slave Distributed Computational Process to being a Peer-to-Peer Distributed Computational Process, depending on its control structure.
  • It can integrate with Computational Consistency Models for data coherence.
  • It can integrate with Fault Tolerance Mechanisms for reliability assurance.
  • ...
• Examples:
  • MapReduce Distributed Computational Processes, such as:
    • Hadoop MapReduce Process for batch data processing.
    • Spark RDD Process for in-memory processing.
  • Stream Processing Distributed Computational Processes, such as:
    • Apache Kafka Stream Process for event streaming.
    • Apache Flink Process for stateful stream processing.
    • Apache Storm Process for real-time computation.
  • Distributed Machine Learning Processes, such as:
    • Federated Learning Process for privacy-preserving training.
    • Parameter Server Process for model training coordination.
    • Data Parallel Training Process for neural network training.
  • Distributed Database Processes, such as:
    • Distributed Transaction Process for ACID compliance.
    • Distributed Query Process for parallel query execution.
    • Replication Process for data availability.
  • Microservices Communication Processes, such as:
    • Service Mesh Process for inter-service communication.
    • API Gateway Process for request routing.
    • Event Bus Process for event-driven communication.
  • ...
• Counter-Examples:
  • Single-Threaded Process, which runs on single core.
  • Shared-Memory Process, which uses local memory.
  • Batch Process, which lacks distribution requirement.
• See: Computational Instruction System, Computational Execution Topology, Computational Orchestration Framework, Computational Consistency Model, Distributed System, Parallel Computing, Network Protocol, Message Passing Interface.

---