Future Trends in Distributed Computing PDF

FUTURE OF CONCURRENT,PARALLEL, AND DISTRIBUTED COMPUTING Supervised by: Prof. Dr. Ibrahim N. Ibrahim Set by: Haidar Raad Shakir  Introduction Parallel and distributed computing help solve complex problems faster by using multiple processors. These technologies are widely u...

FUTURE OF CONCURRENT,PARALLEL, AND DISTRIBUTED COMPUTING Supervised by: Prof. Dr. Ibrahim N. Ibrahim Set by: Haidar Raad Shakir  Introduction Parallel and distributed computing help solve complex problems faster by using multiple processors. These technologies are widely used in AI, machine learning, and large-scale communication systems. Future advancements aim to improve efficiency and overcome existing challenges. Future Trends in Concurrent Computing 1. Dynamics and Interaction Concurrency deals with systems that perform multiple tasks simultaneously. It applies to software, hardware, and even biological sciences like cell interactions. Used in automated industries and robotics for better system coordination.  Future Trends in Concurrent Computing 2. Grid and Cloud Computing Feature Grid Computing Cloud Computing Distributed across Centralized by a Resource Sharing domains provider Manual adjustments Scalability Automatic scaling needed Complex due to multiple Centralized and more Management owners efficient Web hosting, software Use Cases Scientific research, HPC services Future Trends in Concurrent Computing 3. Hybrid Systems Combines real-world continuous systems (like motion) with digital events. Used in autonomous vehicles, robotics, and IoT networks. Challenges include complexity in modeling and coordination. Future Trends in Concurrent Computing Future Trends in Concurrent Computing 4. Mobility and Security Secure communication is vital for mobile computing. Security focuses on authentication, data protection, and secure access during movement. Research is ongoing to develop stronger security protocols. Future Trends in Concurrent Computing 5. Agents and Games Multi-agent systems require coordination for strategic decision-making. Games use concurrency for real-time interactions and AI-driven responses. Challenges include fairness, deadlock avoidance, and efficient decision-making. Future Trends in Concurrent Computing 2.6 Natural Computing Inspired by quantum computing and physics-based models. Used in space simulations and large-scale scientific calculations.  Future Trends in Parallel Computing 1. Machine Learning (ML) ML requires high computing power for training and prediction. Parallel computing speeds up data processing for AI and decision-making. Algorithms are being developed to handle encrypted and distributed data securely.  Future Trends in Parallel Computing 2. Data Analysis Big data analytics rely on parallel processing to handle vast amounts of information. Example: Hadoop MapReduce Algorithm breaks tasks into smaller parts for efficiency.  Future Trends in Parallel Computing 2. Data Analysis  Future Trends in Parallel Computing 3. Simulation Used in physics, engineering, and climate modeling. Example: Molecular Dynamics (MD) simulations model atomic interactions. Future applications include smart cities, disaster management, and agriculture. Future Trends in Parallel Computing  Future Trends in Parallel Computing 4. Brain-Machine Interfaces:  Neuromorphic Computing: Inspired by the brain’s neural networks, neuromorphic chips will enable parallel processing with low power consumption. Brain-machine interfaces will bridge the gap between humans and machines. Future Trends in Distributed Computing 1. Network Architecture Future networks aim for seamless connectivity and user-centered services. Peer-to-peer and overlay networks improve efficiency. Future Trends in Distributed Computing 2. Operating Systems Trends Virtual Machine Monitors (VMMs): Optimizing microservices. Embedded Systems: Algorithms for low-power sensor networks. Ad Hoc Supercomputing: Efficient resource sharing on GRID networks. Future Trends in Distributed Computing Future Trends in Distributed Computing 3. Real-Time Operating Systems Composability is key for integrating different components in real-time systems. But, it requires appropriate operating system and middleware support Future Trends in Distributed Computing Future Trends in Distributed Computing 4. Web Services Architecture Reliable and scalable web services need better security and integration methods. Must work on various devices from smartphones to servers.  Future Trends in Distributed Computing 5. Mobile Computing Security, energy efficiency, and better user interfaces will drive mobile computing improvements. Key Areas of Development: 1. Virtual cellular connections and ad hoc networks. 2. Networking and forwarding protocols. 3. Development tools for ad hoc networks. 4. Power control. 5. Security and access control. 6. Human interfaces and QoS.  Future Trends in Distributed Computing 6. Network Storage Services Future focuses on storage security, high availability, and better data management. Thank you