COMP 9243 — Week 4 ( 17 s 1 )

This lecture deals with one of the fundamental issues encountered when constructing a system made up of independent communicating processes: dealing with time and making sure that processes do the right thing at the right time. In essence this comes down to allowing processes to synchronise and coordinate their actions. Coordination refers to coordinating the actions of separate processes relative to each other and allowing them to agree on global state (such as values of a shared variable). Synchronisation is coordination with respect to time, and refers to the ordering of events and execution of instructions in time. Examples of synchronisation include ordering distributed events in a log file and ensuring that a process performs an action at a particular time. Examples of coordination include ensuring that processes agree on what actions will be performed (e.g., money will be withdrawn from the account), who will be performing actions (e.g., which replica will process a request), and the state of the system (e.g., the elevator is stopped). Synchronisation and coordination play an important role in most distributed algorithms (i.e., algorithms intended to work in a distributed environment). In particular, some distributed algorithms are used to achieve synchronisation and coordination, while others assume the presence of synchronisation or coordination mechanisms. Discussions of distributed algorithms generally assume one of two timing models for distributed systems. The first is a synchronous model, where the time to perform all actions, communication delay, and clock drift on all nodes, are bounded. In asynchronous distributed systems there are no such bounds. Most real distributed systems are asynchronous, however, it is easier to design distributed algorithms for synchronous distributed systems. Algorithms for asynchronous systems are always valid on synchronous systems, however, the converse is not true.