Uniform machine scheduling (also called uniformly-related machine scheduling or related machine scheduling) is an optimization problem in computer science and operations research. It is a variant of optimal job scheduling. We are given n jobs J1, J2, ..., Jn of varying processing times, which need to be scheduled on m different machines. The goal is to minimize the makespan - the total time required to execute the schedule. The time that machine i needs in order to process job j is denoted by pi,j. In the general case, the times pi,j are unrelated, and any matrix of positive processing times is possible. In the specific variant called uniform machine scheduling, some machines are uniformly faster than others. This means that, for each machine i, there is a speed factor si, and the run-time of job j on machine i is pi,j = pj / si. In the standard three-field notation for optimal job scheduling problems, the uniform-machine variant is denoted by Q in the first field. For example, the problem denoted by " Q||" is a uniform machine scheduling problem with no constraints, where the goal is to minimize the maximum completion time. A special case of uniform machine scheduling is identical machine scheduling, in which all machines have the same speed. This variant is denoted by P in the first field. 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 Q||. 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 Q||. (Wikipedia).
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
This lesson introduces the topic of scheduling and define basic scheduling vocabulary. Site: http://mathispower4u.com
From playlist Scheduling
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
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
Into to the Mathematics of Scheduling
Terminology explained includes preference schedule, digraphs, tasks, arcs, processors, and timelines.
From playlist Discrete Math
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
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
(ML 11.4) Choosing a decision rule - Bayesian and frequentist
Choosing a decision rule, from Bayesian and frequentist perspectives. To make the problem well-defined from the frequentist perspective, some additional guiding principle is introduced such as unbiasedness, minimax, or invariance.
From playlist Machine Learning
Optimizations for Digital Forensics Application Development
Mathematica, and by extension the Wolfram Language, provide an incredible array of capabilities that can be brought to bear on complex computer-/digital forensics–related problems. Through the systematic application of Mathematica/Wolfram Language functionality and, where appropriate, leve
From playlist Wolfram Technology Conference 2021
Unsupervised Data Augmentation | AISC
For more details, visit: https://aisc.ai.science/events/2019-07-08/
From playlist Generative Models
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
Psych9B. Psychology Fundamentals. Lecture 7:
UCI Psych 9B: Psych Fundamentals (Fall 2015) Lec 07. Psych Fundamentals View the complete course: http://ocw.uci.edu/courses/psych_9bpsy_beh_11b_psychology_fundamentals.html Instructor: Mark Steyvers, Ph.D. License: Creative Commons CC-BY-SA Terms of Use: http://ocw.uci.edu/info. More cou
From playlist Psych 9B: Psych Fundamentals
Ultimate Guide to Diffusion Models | ML Coding Series | Denoising Diffusion Probabilistic Models
❤️ Become The AI Epiphany Patreon ❤️ https://www.patreon.com/theaiepiphany 👨👩👧👦 Join our Discord community 👨👩👧👦 https://discord.gg/peBrCpheKE In this 3rd video of my ML coding series, we do a deep dive into diffusion models! Diffusion is the powerhouse behind recent text-to-image g
From playlist Diffusion models
NIPS 2011 Big Learning - Algorithms, Systems, & Tools Workshop: Real time data...
Big Learning Workshop: Algorithms, Systems, and Tools for Learning at Scale at NIPS 2011 Invited Talk: Real time data sketches by Alex Smola Alex is a Principal Researcher at Yahoo. Alex's current research focus is on nonparametric methods for estimation, in particular kernel methods
From playlist NIPS 2011 Big Learning: Algorithms, System & Tools Workshop
Algorithms to Live By | Brian Christian & Tom Griffiths | Talks at Google
Practical, everyday advice which will easily provoke an interest in computer science. In a dazzlingly interdisciplinary work, acclaimed author Brian Christian and cognitive scientist Tom Griffiths show how the algorithms used by computers can also untangle very human questions. They expla
From playlist Science Talks
Time Management Tutorial - Tips on scheduling meetings
Learn tips and best practices for scheduling a meeting. Explore more Time Management courses and advance your skills on LinkedIn Learning: https://www.linkedin.com/learning/topics/time-management-3?trk=sme-youtube_M140599-20-03_learning&src=yt-other This is an excerpt from "Time Managemen
From playlist Time Management