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Least slack time (LST) scheduling is an algorithm for dynamic priority scheduling. It assigns priorities to processes based on their slack time. Slack time is the amount of time left after a job if the job was started now. This algorithm is also known as least laxity first.
The algorithm can function with any pipeline architecture and thus software requires few architecture-specific modifications. [3]: 183 Many modern processors implement dynamic scheduling schemes that are variants of Tomasulo's original algorithm, including popular Intel x86-64 chips. [5] [failed verification] [6]
Dynamic priority scheduling is a type of scheduling algorithm in which the priorities are calculated during the execution of the system. The goal of dynamic priority scheduling is to adapt to dynamically changing progress and to form an optimal configuration in a self-sustained manner.
The airline scheduling problem can be considered as an application of extended maximum network flow. The input of this problem is a set of flights F which contains the information about where and when each flight departs and arrives. In one version of airline scheduling the goal is to produce a feasible schedule with at most k crews.
The algorithms used in scheduling analysis "can be classified as pre-emptive or non-pre-emptive". [1] A scheduling algorithm defines how tasks are processed by the scheduling system. In general terms, in the algorithm for a real-time scheduling system, each task is assigned a description, deadline and an identifier (indicating priority).
Earliest deadline first (EDF) or least time to go is a dynamic priority scheduling algorithm used in real-time operating systems to place processes in a priority queue. Whenever a scheduling event occurs (task finishes, new task released, etc.) the queue will be searched for the process closest to its deadline.
Interval scheduling is a class of problems in computer science, particularly in the area of algorithm design. The problems consider a set of tasks. The problems consider a set of tasks. Each task is represented by an interval describing the time in which it needs to be processed by some machine (or, equivalently, scheduled on some resource).
The basic form of the problem of scheduling jobs with multiple (M) operations, over M machines, such that all of the first operations must be done on the first machine, all of the second operations on the second, etc., and a single job cannot be performed in parallel, is known as the flow-shop scheduling problem.