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Distributed and Cloud Systems Micro Specialization
The chapter covers the core technologies pivotal for processing and managing vast datasets and real-time data in cloud environments, focusing on MapReduce, Apache Spark, and Apache Kafka. It explains the foundational principles of distributed data processing, the evolution of MapReduce to Spark for enhanced performance, and the role of Kafka in constructing scalable and fault-tolerant data pipelines. Understanding these systems is crucial for developing cloud-native applications aimed at big data analytics and machine learning.
Course Chapters
Introduction to Clouds, Virtualization and Virtual Machine
Cloud computing revolutionizes the way IT resources are provisioned and managed, moving from capital-heavy investments to a pay-as-you-go model. It offers elasticity, operational agility, and global accessibility while enhancing disaster recovery and fault tolerance. Virtualization serves as a foundational technology, enabling efficient resource management and scalability in cloud infrastructures through various virtualization methods including CPU and I/O virtualization.
Network Virtualization and Geo-distributed Clouds
The chapter focuses on network virtualization and geo-distributed cloud architectures, emphasizing key principles and technologies that enable efficient resource management in cloud infrastructures. It covers server virtualization methods, software-defined networking (SDN), and the challenges of maintaining performance and reliability across geographically dispersed data centers. The content provides foundational knowledge for understanding the scalability and dynamism required in modern cloud services.
Leader Election in Cloud, Distributed Systems and Industry Systems
The module explores leader election in distributed systems, emphasizing its role in achieving coordination, consensus, and fault tolerance without a central authority. It outlines classical algorithms, including ring-based methods such as the LCR and HS algorithms, and delves into more complex systems like the Bully algorithm. The chapter ends with a discussion on practical implementations of leader election in services like Google’s Chubby and Apache ZooKeeper.
Classical Distributed Algorithms and the Industry Systems
The module focuses on classical distributed algorithms essential for building robust and scalable cloud computing systems. It delves into the foundational challenges like time synchronization, global state recording, and mutual exclusion, demonstrating their theoretical and practical significance in cloud infrastructures. Additionally, it explores various algorithms for achieving these objectives and highlights real-world examples like Google's Chubby distributed lock service.
Consensus, Paxos and Recovery in Clouds
The module delves into consensus mechanisms, crucial for achieving consistency in distributed systems, especially within cloud environments. It examines theoretical foundations such as the Paxos algorithm and the challenges posed by Byzantine failures. Additionally, it explores recovery mechanisms essential for maintaining operational reliability in the face of failures.
Cloud Storage: Key-value Stores/NoSQL
Key-Value Stores provide a flexible, schema-less architecture designed for high scalability and availability, essential for cloud applications. Apache Cassandra and HBase serve as two prominent examples of Key-Value Stores, each with distinctive architectures and operational approaches to data management. The distinction between the eventual consistency of Cassandra and the strong consistency of HBase highlights different strategies in handling distributed data in cloud environments.
Peer-to-Peer Systems and Their Use in Industry Systems
The exploration of Peer-to-Peer (P2P) systems is highlighted through a detailed understanding of their architectures, operational models, and significant influence on distributed computing evolution. Key aspects include the decentralization of resource management, the different types of P2P systems, and their applications in modern cloud computing and industry. This analysis showcases the trajectory from early unstructured networks to sophisticated models like Distributed Hash Tables (DHTs).
Cloud Applications: MapReduce, Spark, and Apache Kafka
The chapter covers the core technologies pivotal for processing and managing vast datasets and real-time data in cloud environments, focusing on MapReduce, Apache Spark, and Apache Kafka. It explains the foundational principles of distributed data processing, the evolution of MapReduce to Spark for enhanced performance, and the role of Kafka in constructing scalable and fault-tolerant data pipelines. Understanding these systems is crucial for developing cloud-native applications aimed at big data analytics and machine learning.