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Resources security and protection Distributed operating system
- 1. DISTRIBUTED OPERATING SYSTEM PRESENTED BY P. JEYASHRINITHI NADAR SARASWATHI COLLEGE OF ARTS AND SCIENCE RESOURCES SECURITY AND PROTECTION
- 2. 1. INTRODUCTION 1.2 COMPONENTS OF DB 2. WHAT IS DIFFERENCE? 2.1 ARCHITECTURE 2.2 TRANSPARENCY 2.3 SCALABILITY 2.4 RESOURCE MANAGEMENT 2.5 FAULT TOLERANCE 2.6 COMMUNICATION 2.7 USE CASE 3. REQUIREMENTS OF DISTRIBUTED OPERATING SYSTEM
- 3. A distributed operating system (DOS) manages a collection of independent computers and makes them appear to users as a single coherent system. Unlike traditional operating systems, which are designed for a single machine, a distributed operating system allows multiple computers to collaborate and share resources seamlessly. Here’s an overview of key concepts and components of distributed operating systems 1. INTRODUCTION
- 4. 1.Distributed File System (DFS): 1. Manages files distributed across multiple servers, allowing for data sharing and access as if it were local. 2.Process Management: 1. Handles the execution of processes across different machines, including scheduling and synchronization. 3.Network Management: 1. Manages the underlying network resources to ensure reliable communication among distributed components. 4.Security: 1. Implements authentication, authorization, and encryption to secure data and access across the distributed system. 1.2 Components of a Distributed Operating System
- 5. 1. Architecture • Distributed Operating Systems (DOS): • Comprises multiple interconnected computers that work together, appearing as a single coherent system. • Each node (computer) can operate independently but collaborates with others for resource sharing. • Traditional Operating Systems (OS): • Designed for a single computer, managing local resources (CPU, memory, I/O devices). • Operates on a monolithic architecture where the OS has complete control over the hardware. 2. WHAT IS DIFFERENCES BETWEEN DISTRIBUTED OS AND TRADITIONAL OS
- 6. 2. Transparency • DOS: • Aims for high levels of transparency (location, migration, replication, concurrency), so users are unaware of the complexities of the underlying distributed environment. • OS: • Generally lacks transparency; users and applications are aware of hardware and resource limitations, as everything is local. 3. Scalability • DOS: • Built to easily scale by adding more nodes to the network without significant redesign or performance loss. • OS: • Scalability is limited to the resources of the single machine; adding hardware usually requires specific configurations and may not support distributed workloads.
- 7. 4. Resource Management • DOS: • Involves complex resource management across multiple nodes, including distributed file systems and process scheduling. • OS: • Manages resources on a local machine, with simpler scheduling and memory management algorithms. 5. Fault Tolerance • DOS: • Designed to handle failures of individual nodes without affecting the entire system, often through redundancy and replication. • OS: • Typically fails if the single machine crashes, with limited recovery options for preserving state or data.
- 8. 6. Communication • DOS: • Relies on network communication protocols (e.g., message passing, RPC) to facilitate interaction between distributed processes. • OS: • Communication happens primarily through local system calls and inter-process communication (IPC) mechanisms within the same machine. 7. Use Cases • DOS: • Suitable for environments requiring resource sharing and collaboration, such as cloud computing, large-scale data processing, and IoT networks. • OS: • Best for standalone applications and scenarios where resource access is limited to a single user or system.
- 9. 1. Data Management • Efficient Storage: Must support structured data storage, indexing, and retrieval. • Data Integrity: Ensure accuracy and consistency of data through constraints and rules. • Transaction Management: Handle concurrent transactions, ensuring ACID (Atomicity, Consistency, Isolation, Durability) properties. 3. REQUIREMENTS OF DISTRIBUTED OPERATING SYSTEM
- 10. 2. Concurrency Control • Locking Mechanisms: Implement locking protocols to prevent data conflicts and maintain consistency during concurrent access. • Deadlock Resolution: Provide strategies to detect and resolve deadlocks that may occur during concurrent operations. 3. Data Security • User Authentication: Ensure that only authorized users can access or modify data. • Access Control: Implement fine-grained permissions for data access and modification. • Encryption: Support data encryption for secure storage and transmission.
- 11. 4. Performance Optimization • Query Optimization: Use algorithms to optimize the execution of queries, improving response time. • Caching: Implement caching strategies to speed up data access and reduce disk I/O. • Load Balancing: Distribute workloads evenly across resources to optimize performance. 5. Scalability • Horizontal and Vertical Scaling: Support scaling out (adding more nodes) and scaling up (enhancing existing nodes) to handle growing data volumes and user loads. • Partitioning: Enable data partitioning across different storage locations to improve performance and management. 6. Backup and Recovery • Automated Backups: Provide mechanisms for regular data backups to prevent data loss. • Recovery Mechanisms: Implement recovery procedures to restore data in case of failures or crashes.
- 12. 7. Interoperability • Support for Standards: Adhere to database standards (like SQL) for compatibility with various applications and systems. • APIs and Interfaces: Provide APIs for easy integration with different programming environments and applications. 8. User Interface • Management Tools: Offer user-friendly tools for database administration, monitoring, and reporting. • Query Languages: Support high-level query languages for data manipulation (like SQL) and interfaces for non-technical users. 9. Data Modeling • Schema Management: Support the creation and management of data schemas, allowing for flexibility in data organization. • Data Relationships: Manage relationships between different data entities efficiently.
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