Unrelated-machines scheduling is an optimization problem in computer science and operations research. It is a variant of optimal job scheduling. We need to schedule n jobs J1, J2, ..., Jn on m different machines, such that a certain objective function is optimized (usually, the makespan should be minimized). The time that machine i needs in order to process job j is denoted by pi,j. The term unrelated emphasizes that there is no relation between values of pi,j for different i and j. This is in contrast to two special cases of this problem: uniform-machines scheduling - in which pi,j = pi / sj (where sj is the speed of machine j), and identical-machines scheduling - in which pi,j = pi (the same run-time on all machines). In the standard three-field notation for optimal job scheduling problems, the unrelated-machines variant is denoted by R in the first field. For example, the problem denoted by " R||" is an unrelated-machines scheduling problem with no constraints, where the goal is to minimize the maximum completion time. In some variants of the problem, instead of minimizing the maximum completion time, it is desired to minimize the average completion time (averaged over all n jobs); it is denoted by R||. More generally, when some jobs are more important than others, it may be desired to minimize a weighted average of the completion time, where each job has a different weight. This is denoted by R||. In a third variant, the goal is to maximize the minimum completion time, " R||" . This variant corresponds to the problem of Egalitarian item allocation. (Wikipedia).
Into to the Mathematics of Scheduling
Terminology explained includes preference schedule, digraphs, tasks, arcs, processors, and timelines.
From playlist Discrete Math
Viswanath Nagarajan: Stochastic load balancing on unrelated machines
The lecture was held within the framework of the follow-up workshop to the Hausdorff Trimester Program: Combinatorial Optimization. Abstract: We consider the unrelated machine scheduling problem when job processing-times are stochastic. We provide the first constant factor approximation
From playlist Follow-Up-Workshop "Combinatorial Optimization"
Jannik Matuschke: Generalized Malleable Scheduling via Discrete Convexity
In malleable scheduling, jobs can b e executed simultaneously on multiple machines with the prcessing time depending on the numb er of allocated machines. Each job is required to be executed non-preemptively and in unison, i.e., it has to occupy the same time interval on all its allocated
From playlist Workshop: Approximation and Relaxation
Thomas Rothvoß: Scheduling with Communication Delays via LP Hierarchies and Clustering
We consider the classic problem of scheduling jobs with precedence constraints on identical machines to minimize makespan, in the presence of communication delays. In this setting, denoted by P | prec,c | Cmax, if two dependent jobs are scheduled on different machines, then at least c unit
From playlist Workshop: Approximation and Relaxation
An animation showing the main features of a process scheduling system including the ready queue, blocked queue, high level scheduler and low level scheduler. It explains the principle of a round robin scheduling algorithm.
From playlist Operating Systems
Scheduling: The List Processing Algorithm Part 1
This lesson explains and provides an example of the list processing algorithm to make a schedule given a priority list. Site: http://mathispower4u.com
From playlist Scheduling
Scheduling: The Decreasing Time Algorithm
This lesson explains how to use the decreasing time algorithm to create a priority list and then a schedule. Site: http://mathispower4u.com
From playlist Scheduling
This lesson introduces the topic of scheduling and define basic scheduling vocabulary. Site: http://mathispower4u.com
From playlist Scheduling
ElixirConf 2016 - Debugging Techniques in Elixir by Erich Kist
Debugging Techniques in Elixir by Erich Kist Show some results in the standard output is the first experience in how to debug your code. What is the next step? Probably, the first answer that you found was: start the observer. After some minutes with an awesome feeling, you asked yourself
From playlist ElixirConf 2016
Stochastic Gradient Descent: where optimization meets machine learning- Rachel Ward
2022 Program for Women and Mathematics: The Mathematics of Machine Learning Topic: Stochastic Gradient Descent: where optimization meets machine learning Speaker: Rachel Ward Affiliation: University of Texas, Austin Date: May 26, 2022 Stochastic Gradient Descent (SGD) is the de facto op
From playlist Mathematics
Relation Extraction | Stanford CS224U Natural Language Understanding | Spring 2021
For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/ai To learn more about this course visit: https://online.stanford.edu/courses/cs224u-natural-language-understanding To follow along with the course schedule and s
From playlist Stanford CS224U: Natural Language Understanding | Spring 2021
Theoretical analysis of unsupervised learning (Lecture 3) by Sanjeev Arora
DISTINGUISHED LECTURES THREE LECTURES ON MACHINE LEARNING SPEAKER: Sanjeev Arora (Princeton University and Institute for Advanced Study, USA) DATE: 12 February 2019 to 13 February 2019 VENUE: Ramanujan Lecture Hall, ICTS Bangalore Lecture 1: Mathematics of Machine Learning: An introdu
From playlist DISTINGUISHED LECTURES
DEFCON 14: DNS Abuse Infrastructure and Games
Speaker: Gadi Evron Abstract: DNS operations today are no longer just a secure configuration and bandwidth, but rather a whole world of online abuse and criminal activities. In this presentation we will discuss how DNS has become this infrastructure for online crime and abuse. Spam, DDoS
From playlist DEFCON 14
Scheduling: The List Processing Algorithm Part 2
This lesson explains and provides an example of the list processing algorithm to create a digraph and make a schedule. Site: http://mathispower4u.com
From playlist Scheduling
Mantis brains: How to see in 3D
Praying mantises are one of the only invertebrates that can see in 3D. Neuroscientist Jenny Read is using tiny 3D glasses to test their depth perception, while Ronny Rosner uncovers the nerve cells responsible for this so-called stereoscopic vision. In this film Ali Jennings goes to Newc
From playlist Neuro
2.6.5 Time versus Processors: Video
MIT 6.042J Mathematics for Computer Science, Spring 2015 View the complete course: http://ocw.mit.edu/6-042JS15 Instructor: Albert R. Meyer License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
From playlist MIT 6.042J Mathematics for Computer Science, Spring 2015
Ask Adam Savage: Why M7 Spun Off from M5
Tested member Robbie DeHaan asked Adam why, in early MythBusters episodes, Jamie and Adam worked with the "build team," but as time went on, only collaborated on special occasions. "What was the reasoning for this decision and did you prefer the two teams to work together or act separately
From playlist MythBusters-Related Videos