Distributed Systems

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Distributed Systems by Mind Map: Distributed Systems

1. Distributed Algorithms

1.1. Time & Global States

1.1.1. Time Physical clocks Christian algorithms Berkeley algorithm Logical Clocks Lamport clocks Vector clocks

1.1.2. Global States Distributed snapshot Global Predicates Safety Liveliness Stability (True/False)

1.2. Coordination & Agreement

1.2.1. Election algorithms Bully algorithm Chang-robert ring Enhanced Ring

1.2.2. Multicast Basic reliable Scalable reliable Ordered Atomic

1.2.3. Consensus Doler & Strong algorithm Byzantine general problems OM (m) algorithm Paxos

2. Distributed shared data

2.1. Distributed Transactions

2.1.1. Properties Atomicity Consistency: Isolation Durability:

2.1.2. Problems with concurrent transactions Lost update problem Inconsistent retrievals problem Dirty read problem Recoverability from aborts Premature write problem

2.1.3. Concurrency control Strict two-phase locking Timestamp ordering Optimistic concurrency control

2.1.4. Distributed commit (Two phase)

2.2. Consistency and replication

2.2.1. Data-centric consistency models Strong consistency models Stric consistency Sequential consistency Strong consistency Causal consistency FIFO consistency Relaxed consistency models Weak Release Entry

2.2.2. Client-centric consistency models Eventual models Monotonic Reads Monotonic Writes Read Your Writes Writes Follow Reads

2.2.3. Consistency protocols Primary-based protocols Primary-backup remote-write protocols Primary-backup local-write protocols Replicated-write protocols Active replication Quorum-based protocols (majority voting)