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The running time of LPT is dominated by the sorting, which takes O(n log n) time, where n is the number of inputs. LPT is monotone in the sense that, if one of the input numbers increases, the objective function (the largest sum or the smallest sum of a subset in the output) weakly increases. [2] This is in contrast to Multifit algorithm.
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.
Busy-waiting itself can be made much less wasteful by using a delay function (e.g., sleep()) found in most operating systems. This puts a thread to sleep for a specified time, during which the thread will waste no CPU time. If the loop is checking something simple then it will spend most of its time asleep and will waste very little CPU time.
The task with the highest priority for which all dependent tasks have finished is scheduled on the worker which will result in the earliest finish time of that task. This finish time depends on the communication time to send all necessary inputs to the worker, the computation time of the task on the worker, and the time when that processor ...
A solution presented by William Stallings [7] is to allow a maximum of n-1 philosophers to sit down at any time. The last philosopher would have to wait (for example, using a semaphore) for someone to finish dining before he "sits down" and requests access to any fork.
Waiting time and response time increase as the process's computational requirements increase. Since turnaround time is based on waiting time plus processing time, longer processes are significantly affected by this. Overall waiting time is smaller than FIFO, however since no process has to wait for the termination of the longest process.
Objective function can be to minimize the makespan, the L p norm, tardiness, maximum lateness etc. It can also be multi-objective optimization problem. Jobs may have constraints, for example a job i needs to finish before job j can be started (see workflow). Also, the objective function can be multi-criteria. [4]
Anytime algorithms are designed so that it can be told to stop at any time and would return the best result it has found so far. [3] This is why it is called an interruptible algorithm. Certain anytime algorithms also maintain the last result, so that if they are given more time, they can continue from where they left off to obtain an even ...