1.
Definition of Distributed Computing and Its Popularity
Distributed computing is a system where multiple independent computers
work together as a unified system. These computers are connected via a
network and use middleware to enable coordination and resource sharing.
Users perceive them as a single, integrated system.
Why is Distributed Computing Popular?
• Enables resource sharing (hardware & software) across locations.
• Improves performance with shorter response times & higher
throughput.
• Enhances reliability against component failures.
• Supports scalability and incremental growth.
• Cost-effective with a better price-performance ratio.
• Meets user flexibility needs with adaptable configurations.
2. Comparison Between NOS and DOS
3. Issues in Distributed Computing
Distributed computing faces several challenges:
1. Transparency – Hiding complexities like location, failure, concurrency,
and access.
2. Reliability – Handling faults, failures, and recovery mechanisms.
3. Flexibility – Ease of modification and enhancement of the system.
4. Performance – Managing network traffic, caching, and parallelism.
5. Scalability – Expanding systems without performance degradation.
6. Security – Ensuring secure communication and data protection.
7. Heterogeneity – Managing diverse hardware and software environments.
8. Openness – Ensuring interoperability and standard protocols.
9. Concurrency – Synchronizing multiple users accessing shared resources.
4. Middleware and Its Models
Middleware is a software layer between the application and OS, enabling
distributed communication and resource sharing while abstracting underlying
complexities.
Different Middleware Models
1. Distributed File Systems – Provides transparent access to files across a
network (e.g., NFS).
2. Remote Procedure Calls (RPC) – Enables function execution on remote
machines (e.g., gRPC).
3. Distributed Objects – Uses object-oriented models to distribute
applications (e.g., CORBA, Java RMI).
4. Distributed Documents – Supports document sharing and collaborative
work (e.g., Google Docs).
Services Offered By Middleware Models :
• Naming service
• Transaction support
• Security services
• Communication & messaging
• Data synchronization
• Load balancing
Types of Distributed Systems:
1. Distributed Computing Systems
o A system where multiple computers work together to solve
computational problems.
o Uses parallel computing techniques to enhance performance.
o Examples: Cloud computing, supercomputers, high-performance
computing clusters.
2. Cluster Computing
o A group of interconnected computers that work as a single
system.
o Nodes in the cluster are similar in hardware and run the same
OS.
o Provides load balancing and fault tolerance.
o Example: Beowulf Cluster.
3. Grid Computing
o A distributed system where computers are loosely connected and
geographically dispersed.
o Heterogeneous environment (different hardware, OS, and
networks).
o Resources are shared among different organizations.
o Used for large-scale scientific research (e.g., CERN’s computing
grid).
4. Distributed Information Systems
o A system that enables access to distributed databases and
information repositories.
o Uses middleware to integrate various data sources.
o Examples: Enterprise Resource Planning (ERP), banking systems,
online reservation systems.
5. Transaction Processing Systems
o Ensures reliable execution of transactions in distributed
environments.
o Transactions must follow ACID (Atomicity, Consistency, Isolation,
Durability) properties.
o Examples: Online banking, e-commerce platforms, stock trading
systems.
6. Enterprise Application Integration (EAI) Systems
o Integrates various enterprise applications to enable seamless
communication.
o Uses middleware for data exchange.
o Examples: Customer Relationship Management (CRM) software,
supply chain management systems.
7. Distributed Pervasive Systems
o Systems designed for embedded computing and real-time
processing.
o Includes smart devices and IoT (Internet of Things) systems.
o Examples: Home automation, healthcare monitoring, smart
traffic systems.
8. Home Systems
o Distributed computing in smart homes for automation and
control.
o Integrates devices like smart thermostats, lighting, security
cameras.
o Examples: Google Home, Amazon Alexa.
9. Healthcare Systems
o Distributed systems for patient monitoring, medical imaging, and
telemedicine.
o Allows remote diagnosis and treatment.
o Examples: Hospital Information Systems (HIS), wearable health
monitors.
10.Sensor Networks
• Composed of distributed sensors collecting and transmitting
environmental data.
• Used in agriculture, disaster management, and military applications.
• Examples: Weather monitoring systems, earthquake detection
networks.
Grid and Cluster Computing Models:
• Cluster Computing:
o Homogeneous computers connected via high-speed LAN
o Parallel computing capability
o Uses inexpensive PC hardware
o Examples: Beowulf Clusters
• Grid Computing:
o Loosely modeled after electrical grids
o Heterogeneous computing environment
o Resources pooled across different locations
o Used for high-performance computing tasks
