Distributed computing

Introduction and Overview of Distributed Computing - Distributed system components are located on different networked computers. - Components communicate and coordinate their actions by passing messages. - Distributed computing is a field of computer science that studies distributed systems. - Challenges of distributed systems include maintaining concurrency, overcoming the lack of a global clock, and managing independent failures. - When a component fails, the entire system does not fail. - Distributed systems are groups of networked computers with a common work goal. - Concurrent computing, parallel computing, and distributed computing have overlap. - Parallel computing is tightly coupled, while distributed computing is loosely coupled. - In parallel computing, processors have shared memory, while in distributed computing, each processor has its own memory. - Distributed systems are represented as a network topology, while parallel systems have direct access to shared memory. History and Evolution of Distributed Computing - Concurrent processes communicating through message-passing originated in the 1960s. - Local-area networks like Ethernet were the first widespread distributed systems. - ARPANET, the predecessor of the Internet, introduced in the late 1960s. - ARPANET e-mail was an early large-scale distributed application. - Distributed computing became its own branch of computer science in the late 1970s and early 1980s. - Andrews (2000) discusses the historical development of distributed computing. - Banks (2012) provides insights into the secret history of the internet and its founders. - Tel (2000) explores the evolution and advancements of distributed algorithms. - Ohlídal et al. (2006) discuss the application of evolutionary computing in distributed systems. - Real Time and Distributed Computing Systems (PDF) provides an overview of distributed computing systems. Architectures and Models in Distributed Computing - Hardware and software architectures are used for distributed computing. - CPUs are interconnected with networks at a lower level. - Processes running on CPUs are interconnected with communication systems at a higher level. - Distributed programming falls into architectures like client-server, three-tier, and peer-to-peer. - Architectures can be categorized as loose coupling or tight coupling. - Parallel algorithms in shared-memory model allow all processors to access a shared memory. - The parallel random-access machine (PRAM) model is commonly used. - Shared-memory programs can be extended to distributed systems with communication encapsulation. - Parallel algorithms in message-passing model involve designing the network structure and program execution for each computer. - Boolean circuits and sorting networks are used in this model. Applications and Use Cases of Distributed Computing - Distributed systems are used when data needs to be shared between multiple computers in different locations. - They allow for larger storage, faster compute, and higher bandwidth than a single machine. - Distributed systems provide more reliability as there is no single point of failure. - They are easier to expand and manage compared to a monolithic uniprocessor system. - Using a cluster of low-end computers can be more cost-efficient than a single high-end computer. - Vigna and Casey (2015) explore the impact of cryptocurrencies and blockchain on distributed computing. - Quang Hieu Vu et al. (2010) discuss the principles and applications of peer-to-peer computing. - Lind and Alm (2006) present a database-centric virtual chemistry system for distributed computing. - Chiu (1990) proposes a model for optimal database allocation in distributed computing systems. - Elmasri and Navathe (2000) provide an overview of distributed database systems. Algorithms, Techniques, and Challenges in Distributed Computing - Lynch (1996) describes the fundamentals of distributed computing. - Peleg (2000) provides an introduction to distributed systems. - Ghosh (2007) discusses the challenges and benefits of distributed computing. - Keidar (2008) explores the concepts and principles of distributed algorithms. - Papadimitriou (1994) presents a comprehensive overview of distributed computing. - Andrews (2000) discusses various algorithms and techniques used in distributed computing. - Arora and Barak (2009) explore the trade-offs between bit, message, and time complexity in distributed algorithms. - Cormen, Leiserson, and Rivest (1990) discuss distributed algorithms and their complexities. - Herlihy and Shavit (2008) provide in-depth coverage of concurrent programming and synchronization in distributed systems. - Lynch (1996) discusses the challenges and future directions in distributed computing.