Stride Scheduling. Robert Grimm New York University
|
|
|
- Aron Perry
- 6 years ago
- Views:
Transcription
1 Stride Scheduling Robert Grimm New York University
2 The Three Questions What is the problem? What is new or different? What are the contributions and limitations?
3 Motivation Scheduling of scarce computer resources (e.g., CPU) Has major impact on throughput and response time Should be fair (scientific applications) But also needs to adjust rapidly (interactive applications) Priority-based schemes Rely on ad-hoc assignment of priorities Are poorly understood Do not provide encapsulation, modularity
4 A Probabilistic Solution: Lottery Scheduling
5 Lottery Scheduling Provides a randomized mechanism Not suitable for (hard) real-time systems Provides control over relative execution rates Can be implemented efficiently Supports modular resource management
6 The Basic Ingredients Tickets Abstract, relative, and uniform resource rights Lotteries Probabilistically fair selection of next resource holder Throughput proportional to client's ticket allocation Binomial distribution, accuracy improves with n Average response time inversely proportional to client's ticket allocation
7 Fun with Lottery Tickets Ticket transfers Useful for RPC-based systems Avoid priority inversion problem Ticket inflation Provides alternative to transfers (no communication!) Needs to be avoided/contained in general Ticket currencies Support flexible naming, sharing, and protecting of resource rights Compensation tickets Make up for underutilization
8 Wait a Minute! Lottery scheduling is probabilistic Mean error slows down, but still grows Fairly high distribution of response times Mean Error (quanta) Frequency (thousands) (a) Lottery 7: (a) Lottery
9 A Deterministic Solution: Stride Scheduling
10 The Basic Ingredients Tickets Abstract, relative, and uniform resource rights Strides (stride = stride1 / tickets) Intervals between selections Passes (pass += stride) Virtual time index for next selection Clients with smallest pass gets selected
11 Making Strides 3:2:1 Allocation 2 15 Pass Value Time (quanta)
12 Dynamic Client Participation Need to maintain aggregate information Global tickets: sum of all active clients tickets Global stride: stride1 / global tickets Global pass: incremented by global stride per quantum Need to maintain additional per-client information Remain: client s pass - global pass Added back in when rejoining the system
13 Dynamic Ticket Modification stride global_pass pass done remain remain global_pass pass stride Figure 4: Allocation Change. Modifying a client s allocation from tickets to tickets requires only a constant-time recomputation of its stride and pass. The new stride is inversely proportional to tickets. The new pass is determined by scaling remain, the remaining portion of the the current stride, by stride / stride.
14 One More Thing Throughput error Bounded to single quantum between any two clients But absolute error may still be O(# clients) Approach: combine clients into groups Larger ticket allocations, smaller strides Algorithm: balanced binary tree of groups Aggregate ticket, stride, and pass values Updates propagated to each of a client s ancestors
15 Simulations
16 Lotteries v Strides Lotteries Strides 5 A 5 A B C Cumulative Quanta B C Cumulative Quanta over Time
17 Throughput Accuracy 1 1 Mean Error (quanta) 5 (a) Lottery 7:3 Error (quanta) 5 (b) Stride 7: Mean Error (quanta) 5 (c) Lottery 19:1 Error (quanta) 5 (d) Stride 19: Time (quanta) Time (quanta)
18 More Throughput Accuracy 1 1 Mean Error (quanta) 5 (a) Lottery [2,12]:3 Error (quanta) 5 (b) Stride [2,12]: Mean Error (quanta) 5 (c) Lottery 19:[5,15] Error (quanta) 5 (d) Stride 19:[5,15] Time (quanta) Time (quanta)
19 Response Time Distribution 1 1 Frequency (thousands) (a) Lottery - 19 Frequency (thousands) (b) Stride Frequency (thousands) (c) Lottery - 1 Frequency (thousands) (d) Stride Response Time (quanta) Response Time (quanta)
20 Hierarchical Strides 5 Frequency (thousands) Hierarchical Frequency (thousands) Flat
21 In the Real World
22 CPU Scheduling 1 25 Observed Iteration Ratio Average Iterations (per sec) Allocated Ratio Time (sec) Figure 14: CPU Rate Accuracy. For each allocation ratio, the observed iteration ratio is plotted for each of three 3 second runs. The gray line indicates the ideal where the two ratios are identical. The observed ratios are within 1% of the ideal for all data points. Figure 15: CPU Fairness Over Time. Two processes executing the compute-bound arith benchmark with a 3 : 1 ticket allocation. Averaged over the entire run, the two processes executed and iterations/sec., for an actual ratio of 3.1:1.
23 Network Scheduling 1 Observed Throughput Ratio Allocated Ratio Figure 16: Ethernet UDP Rate Accuracy. For each allocation ratio, the observed data transmission ratio is plotted for each of three runs. The gray line indicates the ideal where the two ratios are identical. The observed ratios are within 5% of the ideal for all data points.
24 What Do You Think?
Advanced Operating Systems (CS 202) Scheduling (2)
Advanced Operating Systems (CS 202) Scheduling (2) Lottery Scheduling 2 2 2 Problems with Traditional schedulers Priority systems are ad hoc: highest priority always wins Try to support fair share by adjusting
Scheduling II. To do. q Proportional-share scheduling q Multilevel-feedback queue q Multiprocessor scheduling q Next Time: Memory management
Scheduling II To do q Proportional-share scheduling q Multilevel-feedback queue q Multiprocessor scheduling q Next Time: Memory management Scheduling with multiple goals What if you want both good turnaround
TCSS 422: OPERATING SYSTEMS
TCSS 422: OPERATING SYSTEMS OBJECTIVES Multi-level Feedback Queue Scheduling: Multi-level level Feedback Queue, Proportional Share Proportional Share Scheduler Wes J. Lloyd Institute of Technology University
CSC 1600: Chapter 5. CPU Scheduling. Review of Process States
CSC 1600: Chapter 5 CPU Scheduling Review of Process States 1 OS Queuing Model Enter Ready queue CPU Exit Disk Queue Network Queue Printer Queue Processes enter and leave the system CPU Scheduling Each
CPU scheduling. CPU Scheduling
EECS 3221 Operating System Fundamentals No.4 CPU scheduling Prof. Hui Jiang Dept of Electrical Engineering and Computer Science, York University CPU Scheduling CPU scheduling is the basis of multiprogramming
א א א א א א א א
א א א W א א א א א א א א א 2008 2007 1 Chapter 6: CPU Scheduling Basic Concept CPU-I/O Burst Cycle CPU Scheduler Preemptive Scheduling Dispatcher Scheduling Criteria Scheduling Algorithms First-Come, First-Served
Project 2 solution code
Project 2 solution code Project 2 solution code in files for project 3: Mutex solution in Synch.c But this code has several flaws! If you copied this, we will know! Producer/Consumer and Dining Philosophers
CS510 Operating System Foundations. Jonathan Walpole
CS510 Operating System Foundations Jonathan Walpole Project 3 Part 1: The Sleeping Barber problem - Use semaphores and mutex variables for thread synchronization - You decide how to test your code!! We
Probabilistic well cost and time modelling. See the unforeseen
Probabilistic well cost and time modelling See the unforeseen P1 is designed to generate time and cost estimates along with the risk profile of a well. The software is applicable for all different types
CPU SCHEDULING. Scheduling Objectives. Outline. Basic Concepts. Enforcement of fairness in allocating resources to processes
Scheduling Objectives CPU SCHEDULING Enforcement of fairness in allocating resources to processes Enforcement of priorities Make best use of available system resources Give preference to processes holding
OPERATING SYSTEMS. Systems and Models. CS 3502 Spring Chapter 03
OPERATING SYSTEMS CS 3502 Spring 2018 Systems and Models Chapter 03 Systems and Models A system is the part of the real world under study. It is composed of a set of entities interacting among themselves
SE350: Operating Systems. Lecture 6: Scheduling
SE350: Operating Systems Lecture 6: Scheduling Main Points Definitions Response time, throughput, scheduling policy, Uniprocessor policies FIFO, SJF, Round Robin, Multiprocessor policies Scheduling sequential
Homework 2: Comparing Scheduling Algorithms
Homework 2: Comparing Scheduling Algorithms The purpose of this homework is to build a scheduler to compare performance of a First In First Out (FIFO) versus Round Robin algorithms. The algorithms will
Extending Stride Scheduler with System Credit for Implicit Coscheduling
Extending Stride Scheduler with System Credit for Implicit Coscheduling Lei Chen (chenl@cs.wisc.edu) Ling Ni(nil@cs.wisc.edu) Abstract: This paper describes an extension to stride scheduling with system
Scheduling Processes 11/6/16. Processes (refresher) Scheduling Processes The OS has to decide: Scheduler. Scheduling Policies
Scheduling Processes Don Porter Portions courtesy Emmett Witchel Processes (refresher) Each process has state, that includes its text and data, procedure call stack, etc. This state resides in memory.
Motivation. Types of Scheduling
Motivation 5.1 Scheduling defines the strategies used to allocate the processor. Successful scheduling tries to meet particular objectives such as fast response time, high throughput and high process efficiency.
Uniprocessor Scheduling
Chapter 9 Uniprocessor Scheduling In a multiprogramming system, multiple processes are kept in the main memory. Each process alternates between using the processor, and waiting for an I/O device or another
Planning the Capacity of a Web Server: An Experience Report D. Menascé. All Rights Reserved.
Planning the Capacity of a Web Server: An Experience Report Daniel A. Menascé George Mason University menasce@cs.gmu.edu Nikki Dinh SRA International, Inc. nikki_dinh@sra.com Robert Peraino George Mason
Intelligent Techniques Lesson 4 (Examples about Genetic Algorithm)
Intelligent Techniques Lesson 4 (Examples about Genetic Algorithm) Numerical Example A simple example will help us to understand how a GA works. Let us find the maximum value of the function (15x - x 2
Scheduling Algorithms. Jay Kothari CS 370: Operating Systems July 9, 2008
Scheduling Algorithms Jay Kothari (jayk@drexel.edu) CS 370: Operating Systems July 9, 2008 CPU Scheduling CPU Scheduling Earlier, we talked about the life-cycle of a thread Active threads work their way
Dynamic Fractional Resource Scheduling for HPC Workloads
Dynamic Fractional Resource Scheduling for HPC Workloads Mark Stillwell 1 Frédéric Vivien 2 Henri Casanova 1 1 Department of Information and Computer Sciences University of Hawai i at Mānoa 2 INRIA, France
Air-traffic Flow Management with ILOG CP Optimizer. Ulrich Junker ILOG
Air-traffic Flow Management with ILOG CP Optimizer Ulrich Junker ILOG 1 Outline Eurocontrol s Air Traffic Flow Management Problem How to develop a precise and accurate optimization model? How to find good
Balancing Risk and Economics for Chemical Supply Chain Optimization under Uncertainty
Balancing Risk and Economics for Chemical Supply Chain Optimization under Uncertainty Fengqi You and Ignacio E. Grossmann Dept. of Chemical Engineering, Carnegie Mellon University John M. Wassick The Dow
Why Measure Performance? Two Primary Measures of Performance
Why Measure Performance? Investors primarily measure performance to monitor progress toward goals. When investors utilize outside managers, performance is analyzed to evaluate manager success or failure.
Chapter 6: CPU Scheduling. Basic Concepts. Histogram of CPU-burst Times. CPU Scheduler. Dispatcher. Alternating Sequence of CPU And I/O Bursts
Chapter 6: CPU Scheduling Basic Concepts Basic Concepts Scheduling Criteria Scheduling Algorithms Multiple-Processor Scheduling Real-Time Scheduling Algorithm Evaluation Maximum CPU utilization obtained
Competitive Strategies for Online Cloud Resource Allocation with Discounts
Competitive Strategies for Online Cloud Resource Allocation with Discounts The 2-Dimensional Parking Permit Problem Xinhui Hu, Arne Ludwig, Andrea Richa, Stefan Schmid Motivation 30% CAGR cloud 2013-2018
College of information technology Department of software
University of Babylon Undergraduate: third class College of information technology Department of software Subj.: Application of AI lecture notes/2011-2012 ***************************************************************************
CPU Scheduling Minsoo Ryu Real-Time Computing and Communications Lab. Hanyang University
CPU Scheduling Minsoo Ryu Real-Time Computing and Communications Lab. Hanyang University msryu@hanyang.ac.kr Topics Covered Basic Concepts Scheduling Criteria Scheduling Algorithms 2 CPU Scheduler Selects
Best Suitable Cogeneration Power for Factories from Multi-Objective Data Analysis
BULGARIAN ACADEMY OF SCIENCES CYBERNETICS AND INFORMATION TECHNOLOGIES Volume 14, No 4 Sofia 2014 Print ISSN: 1311-9702; Online ISSN: 1314-4081 DOI: 10.1515/cait-2014-0009 Best Suitable Cogeneration Power
A CASE STUDY OF SCENARIO EARTHQUAKE-BASED RISK ASSESSMENT FOR NUCLEAR POWER PLANTS
Transactions, SMiRT-23, Paper ID 373 A CASE STUDY OF SCENARIO EARTHQUAKE-BASED RISK ASSESSMENT FOR NUCLEAR POWER PLANTS Mohamed M. Talaat 1, David K. Nakaki 2, Philip S. Hashimoto 3, Gregory S. Hardy 3,
Optimisation and Operations Research
Optimisation and Operations Research Lecture 17: Genetic Algorithms and Evolutionary Computing Matthew Roughan http://www.maths.adelaide.edu.au/matthew.roughan/ Lecture_notes/OORII/
Viridity Energy, Inc. A customer baseline serves as an estimate of how much electricity a customer would
Using an Engineering Model to Determine a Customer s Baseline Load Viridity Energy, Inc. A customer baseline serves as an estimate of how much electricity a customer would consume during a given period,
CS 143A - Principles of Operating Systems
CS 143A - Principles of Operating Systems Lecture 4 - CPU Scheduling Prof. Nalini Venkatasubramanian nalini@ics.uci.edu CPU Scheduling 1 Outline Basic Concepts Scheduling Objectives Levels of Scheduling
- Title: Value of Upstream Information in a Two-Stage Supply Chain with Random
- Abstract Number: 002-0365 - Title: Value of Upstream Information in a Two-Stage Supply Chain with Random Yields - Name of the Conference: Second World Conference on POM and 15 th Annual POM Conference,
Integrated Stochastic Network Model for a Reliability Assessment of the NEES. Esteban Gil Iowa State University August 30 th, 2005
Integrated Stochastic Network Model for a Reliability Assessment of the NEES Esteban Gil Iowa State University August 30 th, 2005 Outline Motivation. Integrated stochastic network model Modeling challenges.
Near-Balanced Incomplete Block Designs with An Application to Poster Competitions
Near-Balanced Incomplete Block Designs with An Application to Poster Competitions arxiv:1806.00034v1 [stat.ap] 31 May 2018 Xiaoyue Niu and James L. Rosenberger Department of Statistics, The Pennsylvania
Extensive Experimental Validation of a Personalized Approach for Coping with Unfair Ratings in Reputation Systems
Extensive Experimental Validation of a Personalized Approach for Coping with Unfair Ratings in Reputation Systems Nanyang Technological University, School of Computer Engineering, zhangj@ntu.edu.sg Received
Background: ERCOT studies in 80 s and 90 s using NARP (N Area Reliability Program): o Small model in which each node represented a major load center
A Direct High Speed Calculation Procedure For Determining LOLE, LOLH, and EUE For Fossil, Wind, and Solar Generation With A Suggested Procedure For Also Including Transmission Constraints a presentation
Random Drug Testing Under Constraints on Subsample Sizes
TRANSPORTATION RESEARCH RECORD 1322 29 Random Drug Testing Under Constraints on Subsample Sizes STEVEN R. LERMAN Recent federal regulations requiring random drug testing of all transit agency employees
Architecture-Aware Cost Modelling for Parallel Performance Portability
Architecture-Aware Cost Modelling for Parallel Performance Portability Evgenij Belikov Diploma Thesis Defence August 31, 2011 E. Belikov (HU Berlin) Parallel Performance Portability August 31, 2011 1 /
IJCSC VOLUME 5 NUMBER 2 JULY-SEPT 2014 PP ISSN
IJCSC VOLUME 5 NUMBER 2 JULY-SEPT 214 PP. 98-13 ISSN-973-7391 Adaptive Round Robin Scheduling using Shortest Burst Approach Rashmi Dhruv Deptt. of Computer Science, Guru Premsukh Memorial College of Engineering
Case Study. Effort = (Size X Complexity) Productivity. A Case for Software Estimation
Case Study A Case for Software Estimation A recent search of the World Wide Web identified over 2100 sites that describe over 5000 reasons that software projects fail, ranging from the poor use of technology
Q1. What is Real time system? Explain the basic model of Real time system. (8)
B. TECH VIII SEM I MID TERM EXAMINATION BRANCH: CSE SUB. CODE: 8CS4.2A SUBJECT: REAL TIME SYSTEM TIME ALLOWED: 2 Hours MAX. MARKS: 40 Q1. What is Real time system? Explain the basic model of Real time
ProRenaTa: Proactive and Reactive Tuning to Scale a Distributed Storage System
ProRenaTa: Proactive and Reactive Tuning to Scale a Distributed Storage System Ying Liu, Navaneeth Rameshan, Enric Monte, Vladimir Vlassov and Leandro Navarro 2015 15th IEEE/ACM International Symposium
CHAPTER 1 INTRODUCTION
1 CHAPTER 1 INTRODUCTION 1.1 MANUFACTURING SYSTEM Manufacturing, a branch of industry, is the application of tools and processes for the transformation of raw materials into finished products. The manufacturing
COMP9334 Capacity Planning for Computer Systems and Networks
COMP9334 Capacity Planning for Computer Systems and Networks Week 7: Discrete event simulation (1) COMP9334 1 Week 3: Queues with Poisson arrivals Single-server M/M/1 Arrivals Departures Exponential inter-arrivals
Chapter 5. Job-Based Structures and Job Evaluation
Chapter 5 Job-Based Structures and Job Evaluation Learning Objectives Job-based structures: Job evaluation Defining job evaluation: Content, value, and external market links How-to : Major decisions Job
Strategic Planning in Air Traffic Control as a Multi-Objective Stochastic Optimization Problem
www.thalesgroup.com Strategic Planning in Air Traffic Control as a Multi-Objective Stochastic Optimization Problem Tenth USA/Europe Seminar on ATM R&D Chicago, IL, USA Gaétan Marceau Caron Marc Schoenauer
A Parametric Bootstrapping Approach to Forecast Intermittent Demand
Proceedings of the 2008 Industrial Engineering Research Conference J. Fowler and S. Mason, eds. A Parametric Bootstrapping Approach to Forecast Intermittent Demand Vijith Varghese, Manuel Rossetti Department
CPU Scheduling. Disclaimer: some slides are adopted from book authors and Dr. Kulkarni s slides with permission
CPU Scheduling Disclaimer: some slides are adopted from book authors and Dr. Kulkarni s slides with permission 1 Recap Deadlock prevention Break any of four deadlock conditions Mutual exclusion, no preemption,
Salisu Aliyu Department of Mathematics, Ahmadu Bello University, Zaria, Nigeria
Volume 4, Issue 2, February 14 ISSN: 2277 128X International Journal of Advanced Research in Computer Science and Software Engineering Research Paper Available online at: www.ijarcsse.com An Additional
CHAPTER 7 SHORT TERM HYDROTHERMAL SCHEDULING WITH PROHIBITED OPERATING ZONES
135 CHAPTER 7 SHORT TERM HYDROTHERMAL SCHEDULING WITH PROHIBITED OPERATING ZONES 7.1 INTRODUCTION A modern power system consists of a large number of thermal and hydel plants connected at various load
Configuring Performance Routing Cost Policies
This module describes how to configure and apply Cisco IOS Performance Routing (PfR) cost policies. A PfR policy can be configured to optimize traffic based on the monetary cost of the links. The PfR Cost
Reading Reference: Textbook: Chapter 7. UNIX PROCESS SCHEDULING Tanzir Ahmed CSCE 313 Fall 2018
Reading Reference: Textbook: Chapter 7 UNIX PROCESS SCHEDULING Tanzir Ahmed CSCE 313 Fall 2018 Process Scheduling Today we will ask how does a Kernel juggle the (often) competing requirements of Performance,
Lecture 6: CPU Scheduling. CMPUT 379, Section A1, Winter 2014 February 5
Lecture 6: CPU Scheduling CMPUT 379, Section A1, Winter 2014 February 5 Objectives Introduce CPU scheduling: the basis for multiprogrammed operating systems Describe various CPU scheduling algorithms Discuss
CPU Scheduling. Basic Concepts Scheduling Criteria Scheduling Algorithms. Unix Scheduler
CPU Scheduling Basic Concepts Scheduling Criteria Scheduling Algorithms FCFS SJF RR Priority Multilevel Queue Multilevel Queue with Feedback Unix Scheduler 1 Scheduling Processes can be in one of several
Comp 204: Computer Systems and Their Implementation. Lecture 10: Process Scheduling
Comp 204: Computer Systems and Their Implementation Lecture 10: Process Scheduling 1 Today Deadlock Wait-for graphs Detection and recovery Process scheduling Scheduling algorithms First-come, first-served
Hadoop Fair Scheduler Design Document
Hadoop Fair Scheduler Design Document August 15, 2009 Contents 1 Introduction The Hadoop Fair Scheduler started as a simple means to share MapReduce clusters. Over time, it has grown in functionality to
Cycle Time Forecasting. Fast #NoEstimate Forecasting
Cycle Time Forecasting Fast #NoEstimate Forecasting 15 th August 2013 Forecasts are attempts to answer questions about future events $1,234,000 Staff 2 Commercial in confidence We estimated every task
Ensemble Learning for Large-Scale Workload Prediction
Received 2 July 2013; revised 3 January 2014; accepted 10 January 2014. Date of publication 7 April 2014; date of current version 30 July 2014. Digital Object Identifier 10.1109/TETC.2014.2310455 Ensemble
Bottleneck Detection of Manufacturing Systems Using Data Driven Method
Proceedings of the 2007 IEEE International Symposium on Assembly and Manufacturing Ann Arbor, Michigan, USA, July 22-25, 2007 MoB2.1 Bottleneck Detection of Manufacturing Systems Using Data Driven Method
TASK SCHEDULING BASED ON EFFICIENT OPTIMAL ALGORITHM IN CLOUD COMPUTING ENVIRONMENT
TASK SCHEDULING BASED ON EFFICIENT OPTIMAL ALGORITHM IN CLOUD COMPUTING ENVIRONMENT M. Lawanya Shri 1, M.B.Benjula Anbumalar 2, K. Santhi 3, Deepa.M 4 1,2,3,4 SITE, VIT University, Vellore, Tamil Nadu
A Profile Guided Approach to Scheduling in Cluster and Multi-cluster Systems
A Profile Guided Approach to Scheduling in Cluster and Multi-cluster Systems Arvind Sridhar and Dan Stanzione Ph. D. {ahsridha, dstanzi}@asu.edu Fulton High Performance Computing, Arizona State University
Simulation of Process Scheduling Algorithms
International Journal of Engineering Science Invention ISSN (Online): 2319 6734, ISSN (Print): 2319 6726 Volume 6 Issue 11 November 2017 PP. 67-71 Simulation of Process Scheduling Algorithms Akanksha Verma
Genetic Algorithms and Genetic Programming Lecture 14
Genetic Algorithms and Genetic Programming Lecture 14 Gillian Hayes 13th November 2007 GAs: Representation, Fitness, Searching the Space Representation binary, real, plans, trees Operators for non-binary
Chapter 24. Time/Job. Work Measurement. Goal: determine standard time for a task. Techniques
Chapter 24 Time/Job 1 Work Measurement Goal: determine standard time for a task. Techniques Direct Time Study Predetermined Time Systems Standard Data Systems Work Sampling 2 Determining Time Standards
High-Volume Web Site Performance Simulator for WebSphere
High-Volume Web Site Performance Simulator for WebSphere Authors: High -Volume Web Site Team Web address: ibm.com/websphere/developer/zones/hvws Technical Contact: Noshir Wadia noshir@us.ibm.com Management
Assessment of Marginal and Long-term Surplus Power in Orissa A Case Study
1 Chandra 16th NATIONAL POWER SYSTEMS CONFERENCE, 15th-17th DECEMBER, 2010 103 Assessment of Marginal and Long-term in Orissa A Case Study Chandra Shekhar Reddy Atla, A.C. Mallik, Dr. Balaraman K and Dr.
HTCondor at the RACF. William Strecker-Kellogg RHIC/ATLAS Computing Facility Brookhaven National Laboratory April 2014
HTCondor at the RACF SEAMLESSLY INTEGRATING MULTICORE JOBS AND OTHER DEVELOPMENTS William Strecker-Kellogg RHIC/ATLAS Computing Facility Brookhaven National Laboratory April 2014 RACF Overview Main HTCondor
Computational Intelligence Lecture 20:Intorcution to Genetic Algorithm
Computational Intelligence Lecture 20:Intorcution to Genetic Algorithm Farzaneh Abdollahi Department of Electrical Engineering Amirkabir University of Technology Fall 2012 Farzaneh Abdollahi Computational
Asia Pacific Journal of Engineering Science and Technology
Asia Pacific Journal of Engineering Science and Technology 3 (2) (2017) 76-85 Asia Pacific Journal of Engineering Science and Technology journal homepage: www.apjest.com Full length article Analysis, implementation
Joel Castaneda. January Tecolote Research, Inc.
Exploiting ACE to Create Build-Up Estimates Joel Castaneda ACEIT Users Conference, Santa Barbara, CA January 2009 Tecolote Research, Inc. Copyright Tecolote Research, Inc. January 2009 Abstract Bottom-up
Analysis of RES production diagram, a comparison of different approaches
POSTER 2015, PRAGUE MAY 14 1 Analysis of RES production diagram, a comparison of different approaches Michaela Hrochová 1 1 Dept. of Economics, Management and Humanities, Czech Technical University, Technická
TOWARD A QUANTIFIED RISK ASSESSMENT APPROACH. Mari Shironishi Pipeline Integrity Manager
TOWARD A QUANTIFIED RISK ASSESSMENT APPROACH Mari Shironishi Pipeline Integrity Manager Overview 1. What is Risk? 2. Our Background Story 3. Reliability Based Design & Assessment 4. Lessons Learned / Road
Standards. The framework for the award of the PASA accreditation for quality pension administration. October PASA Standards Version 1.
s The framework for the award of the PASA accreditation for quality pension administration October 2012 1 PASA s Version 1.11 1. Service Agreement 1.1 Agreement to provide administration services An appropriate
Human Resources and Job Design. Learning Objectives. Human Resource Strategy. Dr. Richard Jerz
Human Resources and Job Design Dr. Richard Jerz 1 Learning Objectives Explain the importance of work design. Describe the two basic approaches to job design. Discuss the advantages and disadvantages of
A Systematic Approach to Performance Evaluation
A Systematic Approach to Performance evaluation is the process of determining how well an existing or future computer system meets a set of alternative performance objectives. Arbitrarily selecting performance
A Genetic Algorithm for Order Picking in Automated Storage and Retrieval Systems with Multiple Stock Locations
IEMS Vol. 4, No. 2, pp. 36-44, December 25. A Genetic Algorithm for Order Picing in Automated Storage and Retrieval Systems with Multiple Stoc Locations Yaghoub Khojasteh Ghamari Graduate School of Systems
Evolutionary Algorithms
Evolutionary Algorithms Evolutionary Algorithms What is Evolutionary Algorithms (EAs)? Evolutionary algorithms are iterative and stochastic search methods that mimic the natural biological evolution and/or
Deterministic Crowding, Recombination And Self-Similarity
Deterministic Crowding, Recombination And Self-Similarity Bo Yuan School of Information Technology and Electrical Engineering The University of Queensland Brisbane, Queensland 4072 Australia E-mail: s4002283@student.uq.edu.au
DYNAMIC RESOURCE PRICING ON FEDERATED CLOUDS
DYNAMIC RESOURCE PRICING ON FEDERATED CLOUDS CCGRID 2010 The 10th IEEE/ACM Intl. Symp. on Cluster, Cloud and Grid Computing Marian Mihailescu Yong Meng Teo Department of Computer Science National University
LOAD SHARING IN HETEROGENEOUS DISTRIBUTED SYSTEMS
Proceedings of the 2 Winter Simulation Conference E. Yücesan, C.-H. Chen, J. L. Snowdon, and J. M. Charnes, eds. LOAD SHARING IN HETEROGENEOUS DISTRIBUTED SYSTEMS Helen D. Karatza Department of Informatics
2. Genetic Algorithms - An Overview
2. Genetic Algorithms - An Overview 2.1 GA Terminology Genetic Algorithms (GAs), which are adaptive methods used to solve search and optimization problems, are based on the genetic processes of biological
Goodbye to Fixed Bandwidth Reservation: Job Scheduling with Elastic Bandwidth Reservation in Clouds
Goodbye to Fixed Bandwidth Reservation: Job Scheduling with Elastic Bandwidth Reservation in Clouds Haiying Shen *, Lei Yu, Liuhua Chen &, and Zhuozhao Li * * Department of Computer Science, University
CS 111. Operating Systems Peter Reiher
Operating System Principles: Scheduling Operating Systems Peter Reiher Page 1 Outline What is scheduling? What are our scheduling goals? What resources should we schedule? Example scheduling algorithms
Lecture 11: CPU Scheduling
CS 422/522 Design & Implementation of Operating Systems Lecture 11: CPU Scheduling Zhong Shao Dept. of Computer Science Yale University Acknowledgement: some slides are taken from previous versions of
Use of PSA to Support the Safety Management of Nuclear Power Plants
S ON IMPLEMENTATION OF THE LEGAL REQUIREMENTS Use of PSA to Support the Safety Management of Nuclear Power Plants РР - 6/2010 ÀÃÅÍÖÈß ÇÀ ßÄÐÅÍÎ ÐÅÃÓËÈÐÀÍÅ BULGARIAN NUCLEAR REGULATORY AGENCY TABLE OF CONTENTS
Global Supply Chain Planning under Demand and Freight Rate Uncertainty
Global Supply Chain Planning under Demand and Freight Rate Uncertainty Fengqi You Ignacio E. Grossmann Nov. 13, 2007 Sponsored by The Dow Chemical Company (John Wassick) Page 1 Introduction Motivation
A few arguments in favor of a carbon tax and a new proposal for climate cooperation
A few arguments in favor of a carbon tax and a new proposal for climate cooperation Robert Schmidt Yale University May 27, 2015 Schmidt: Reasons for a carbon tax + new proposal 1 Part 1: Arguments in favor
A General Method for Fatigue Analysis of Vertical Axis Wind Turbine Blades*
SAND82-2543 Unlimited Release Printed October 1983 Distribution Category UC-60 A General Method for Fatigue Analysis of Vertical Axis Wind Turbine Blades* Paul S. Veers Applied Mechanics Division IV 1524
REVIEW OF POWER SYSTEM EXPANSION PLANNING IN VIETNAM
Public Disclosure Authorized Public Disclosure Authorized Public Disclosure Authorized Public Disclosure Authorized REVIEW OF POWER SYSTEM EXPANSION PLANNING IN VIETNAM Tasks 1 and 2 Report Prepared for
Machine Learning Models for Sales Time Series Forecasting
Article Machine Learning Models for Sales Time Series Forecasting Bohdan M. Pavlyshenko SoftServe, Inc., Ivan Franko National University of Lviv * Correspondence: bpavl@softserveinc.com, b.pavlyshenko@gmail.com
Tassc:Estimator technical briefing
Tassc:Estimator technical briefing Gillian Adens Tassc Limited www.tassc-solutions.com First Published: November 2002 Last Updated: April 2004 Tassc:Estimator arrives ready loaded with metric data to assist
Figure 15 Periodic annual increment (PAI) and gross mean annual increment (MAI) in basal area per acre by treatments for (A) Skykomish treatments 1,
Figure 15 Periodic annual increment () and gross mean annual increment () in basal area per acre by treatments for (A) Skykomish treatments 1, 2, 4, and 7; (B) Skykomish treatments 3, 5, 6, and 8. Each
The Design of SMART: A Scheduler for Multimedia Applications
The Design of SMART: A Scheduler for Multimedia Applications Jason Nieh and Monica S. Lam {nieh,lam}@cs.stanford.edu Technical Report CSL-TR-96-697 Computer Systems Laboratory Stanford University June
Traffic Shaping (Part 2)
Lab 2b Traffic Shaping (Part 2) Purpose of this lab: This lab uses the leaky bucket implementation (from Lab 2a) for experiments with traffic shaping. The traffic for testing the leaky bucket will be the
Software Performance Estimation in MPSoC Design
Software Performance Estimation in MPSoC Design Marcio Seiji Oyamada 1,2, Flávio Rech Wagner 1, Wander Cesario 2, Marius Bonaciu 2, Ahmed Jerraya 2 UFRGS 1 Instituto de Informática Porto Alegre, Brazil
Tutorial Segmentation and Classification
MARKETING ENGINEERING FOR EXCEL TUTORIAL VERSION v171025 Tutorial Segmentation and Classification Marketing Engineering for Excel is a Microsoft Excel add-in. The software runs from within Microsoft Excel
FUNDAMENTALS OF QUALITY CONTROL AND IMPROVEMENT. Fourth Edition. AMITAVA MITRA Auburn University College of Business Auburn, Alabama.
FUNDAMENTALS OF QUALITY CONTROL AND IMPROVEMENT Fourth Edition AMITAVA MITRA Auburn University College of Business Auburn, Alabama WlLEY CONTENTS PREFACE ABOUT THE COMPANION WEBSITE PART I PHILOSOPHY AND
CS 318 Principles of Operating Systems
CS 318 Principles of Operating Systems Fall 2017 Lecture 11: Page Replacement Ryan Huang Memory Management Final lecture on memory management: Goals of memory management - To provide a convenient abstraction