"A distributed algorithm is an algorithm designed to run on computer hardware constructed from interconnected processors."
Algorithms designed for data management, consistency, and fault tolerance in distributed systems.
Consensus Algorithms: These algorithms are designed to help achieve agreement between multiple nodes in a distributed system.
Mutual Exclusion Algorithms: Used to allow only one process to access specific resources in a distributed system at any given time.
Distributed Transactions: Used to manage multiple transactions across multiple nodes in a distributed system.
Distributed Coordination: Techniques used to coordinate multiple nodes in a distributed system, such as leader election.
Replication: The process of duplicating data across different nodes in a distributed system, to improve reliability and performance.
Failure Detection and Recovery: Techniques used to identify failed nodes in a distributed system and to recover from those failures.
Routing Algorithms: Used to determine the best route between nodes in a distributed system to facilitate efficient communication.
Load Balancing: Techniques used to distribute workload across multiple nodes in a distributed system to prevent overburdening any single node.
Distributed Hash Tables: Hash tables that are distributed across multiple nodes in a distributed system, to enable efficient storage and retrieval of data.
Concurrency Control: Techniques used to manage concurrency in a distributed system, to prevent conflicts between different processes that may be accessing the same resources at the same time.
Consensus Algorithms: These algorithms are designed to help a group of distributed entities reach agreement on a single result or decision.
Leader Election Algorithms: Also known as coordinators, these algorithms conduct a sequence of steps to elect one node as a leader in a distributed network.
Mutual Exclusion Algorithms: These algorithms ensure that only one node at a time can access a shared resource or critical section.
Fault Tolerance Algorithms: Algorithms designed to ensure a distributed system continues to function even when one or more nodes fail or go down.
Load Balancing Algorithms: These algorithms evenly distribute workloads across multiple nodes in a distributed system to ensure that no single node is overburdened.
Distributed Snapshots Algorithms: These algorithms are used to provide a global, consistent state across all the nodes in a distributed system.
Distributed Computing Algorithms: These algorithms enable multiple nodes from different locations to work together on a single task.
Overlay Network Algorithms: These algorithms establish a scalable and efficient overlay network among a set of nodes in a distributed network.
Replication Algorithms: These algorithms synchronize copies of a data item across multiple nodes in a distributed system to ensure high availability and fault tolerance.
MapReduce Algorithms: These are large-scale distributed processing algorithms that efficiently process large volumes of data.
Byzantine Fault Tolerance Algorithms: These algorithms enable a distributed system to function even when some nodes behave in a Byzantine manner, such as by sending false information.
Consistent Hashing Algorithms: These algorithms enable distributed systems to map data partitions onto nodes in a deterministic and scalable manner.
Message Ordering Algorithms: These algorithms ensure that all nodes in a distributed system receive messages in the same order, preventing conflicts and inconsistencies.
Stream Processing Algorithms: These algorithms enable processing data streams in real-time across distributed nodes.
Quorum-based Algorithms: These algorithms are designed to ensure that a sufficient number of nodes in a distributed system agree on a decision before taking action.
"Distributed algorithms are used in different application areas of distributed computing, such as telecommunications, scientific computing, distributed information processing, and real-time process control."
"Standard problems solved by distributed algorithms include leader election, consensus, distributed search, spanning tree generation, mutual exclusion, and resource allocation."
"Distributed algorithms are a sub-type of parallel algorithm, typically executed concurrently, with separate parts of the algorithm being run simultaneously on independent processors..."
"One of the major challenges in developing and implementing distributed algorithms is successfully coordinating the behavior of the independent parts of the algorithm in the face of processor failures and unreliable communications links."
"The choice of an appropriate distributed algorithm to solve a given problem depends on both the characteristics of the problem and characteristics of the system the algorithm will run on..."
"...such as the type and probability of processor or link failures, the kind of inter-process communication that can be performed, and the level of timing synchronization between separate processes." Note: The paragraph does not provide twenty distinct study questions; however, I have provided all the major concepts and information from the paragraph with their corresponding quotes.