Syllabus Distributed Computing - (417531) Credit Examination Scheme : 03 In-Sem (Paper) : 30 Marks End-Sem (Paper) : 70 Marks Unit I Introduction to Distributed Computing Fundamentals of distributed computing : Characteristics of Distributed Systems : Issues, Goals and Types of distributed systems, Distributed System Models. Introduction to Artificial Intelligence and Data Science in distributed computing : Distributing computational tasks, handling large volumes of data and leveraging parallel processing capabilities, issues related to data storage and retrieval, data consistency, communication overhead, synchronization and fault tolerance. Use cases and applications of integrating AI and data science in distributed systems : Predictive Maintenance, Fraud Detection, Intelligent Transportation Systems, Supply Chain Optimization, Energy Management, Healthcare and Medical Diagnostics, Customer Behavior Analysis and Natural Language Processing (NLP). (Chapter - 1) Unit II Distributed Data Management and Storage Overview of Distributed Computing Frameworks and Technologies Parallel Computing, Distributed Computing Models, Message Passing, Distributed File Systems : Hadoop Distributed File System (HDFS) and Google File System (GFS), Cluster Computing : (AWS), Microsoft Azure and Google Cloud Platform (GCP), Message Brokers and Stream Processing, Edge Computing. Data Replication and Consistency Model : Eager Replication, Lazy Replication, Quorum-Based Replication, Consensus-Based Replication, Selective Replication, Strong Consistency, Eventual Consistency, Read-your-writes Consistency, Consistent Prefix Consistency, Causal Consistency. Distributed data indexing and retrieval techniques : Distributed Hash Tables (DHTs), Distributed Inverted Indexing, Range-based Partitioning, Content-based Indexing, Peer-to-Peer (P2P) Indexing, Hybrid Approaches. (Chapter - 2) Unit III Distributed Computing Algorithms Distributed Computing Algorithms : Communication and coordination in distributed systems Distributed consensus algorithms (Other consensus algorithms ● Viewstamped Replication ● RAFT ● ZAB ● Mencius ● Many variants of Paxos (Fast Paxos, Egalitarian Paxos etc) Fault tolerance and recovery in distributed systems. Load balancing and resource allocation strategies : Weighted Round Robin, Least Connection, Randomized Load Balancing, Dynamic Load Balancing, Centralized Load Balancing, Distributed Load Balancing, Predictive Load Balancing. Applying AI techniques to optimize distributed computing algorithms : Machine Learning for Resource Allocation, Reinforcement Learning for Dynamic Load Balancing, Genetic Algorithms for Task Scheduling, Swarm Intelligence for Distributed Optimization. (Chapter - 3) Unit IV Distributed Machine Learning and AI Introduction to distributed machine learning algorithms : Types of Distributed Machine Learning : Data Parallelism and Model Parallelism, Distributed Gradient Descent, Federated Learning, All-Reduce, Hogwild, Elastic Averaging SGD. Software to implement Distributed ML : Spark, GraphLab, Google TensorFlow, Parallel ML System (Formerly Petuum), Systems and Architectures for Distributed Machine Learning. Integration of AI algorithms in distributed systems : Intelligent Resource Management, Anomaly Detection and Fault Tolerance, Predictive Analytics, Intelligent Task Offloading. (Chapter - 4) Unit V Big Data Processing in Distributed Systems Big data processing frameworks in distributed computing : Hadoop, Apache Spark, Apache Storm, Samza, Flink. Parallel and distributed data processing techniques : Single Instruction Single Data (SISD), Multiple Instruction Single Data (MISD), Single Instruction Multiple Data (SIMD), Multiple Instruction Multiple Data (MIMD), Single program multiple data (SPMD), Massively parallel processing (MPP). Scalable data ingestion : types of data ingestion, Benefits, challenges, tools, transformation in distributed systems. Real-time analytics and Streaming analytics : types of real time analytics, types of streaming analytics, Comparison of real time analytics and streaming analytics, Applying AI and data science for large-scale data processing and analytics. (Chapter - 5) Unit VI Distributed Systems Security and Privacy Security Challenges in Distributed Systems, Insider Threats, Encryption and Secure Communication : TLS/SSL, PKI, VPN, AMQP, Privacy Preservation Techniques : Differential Privacy, Homomorphic Encryption, Secure Multi-Party Computation (SMPC), Federated Learning, Anonymization and Pseudonymization, Access Control and Data Minimization, AI-based Intrusion Detection and Threat Mitigation Techniques : Anomaly Detection, Behavior-based Detection, Threat Intelligence and Analysis, Real-time Response and Mitigation, Adaptive Security, User and Entity Behavior Analytics (UEBA), Threat Hunting and Visualization. (Chapter - 6)
