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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.
Optimal job scheduling is a class of optimization problems related to scheduling. The inputs to such problems are a list of jobs (also called processes or tasks) and a list of machines (also called processors or workers). The required output is a schedule – an assignment of jobs to machines. The schedule should optimize a certain objective ...
Problems involving weighted interval scheduling are equivalent to finding a maximum-weight independent set in an interval graph. Such problems can be solved in polynomial time. [3] Assuming the vectors are sorted from earliest to latest finish time, the following pseudocode determines the maximum weight of a single-interval schedule in Θ(n) time:
That is, EDF can guarantee that all deadlines are met provided that the total CPU utilization is not more than 100%. Compared to fixed-priority scheduling techniques like rate-monotonic scheduling, EDF can guarantee all the deadlines in the system at higher loading. Note that use the schedulability test formula under deadline as period.
Single-machine scheduling or single-resource scheduling is an optimization problem in computer science and operations research. We are given n jobs J 1 , J 2 , ..., J n of varying processing times, which need to be scheduled on a single machine, in a way that optimizes a certain objective, such as the throughput .
Conceptually, it repeatedly selects a source of the dependency graph, appends it to the current instruction schedule and removes it from the graph. This may cause other vertices to be sources, which will then also be considered for scheduling. The algorithm terminates if the graph is empty. To arrive at a good schedule, stalls should be prevented.
In graph theory and combinatorial optimization, a closure of a directed graph is a set of vertices C, such that no edges leave C. The closure problem is the task of finding the maximum-weight or minimum-weight closure in a vertex-weighted directed graph. [1] [2] It may be solved in polynomial time using a reduction to the maximum flow problem.
This solved the problem of slow interactive response times on multi-core and multi-CPU systems when they were performing other tasks that use many CPU-intensive threads in those tasks. A simple explanation is that, with this patch applied, one is able to still watch a video, read email and perform other typical desktop activities without ...