Partitioned Scheduling of Multimode Systems on Multiprocessor Platforms: when to do the Mode Transition? José Marinho, Gurulingesh Raravi, Vincent Nélis.

Slides:



Advertisements
Similar presentations
Multiprocessor Scheduling
Advertisements

Real-Time Mutli-core Scheduling Moris Behnam. Introduction Single processor scheduling – E.g., t 1 (P=10,C=5), t 2 (10, 6) – U= >1 – Use a faster.
Bag-of-Tasks Scheduling under Budget Constraints Ana-Maria Oprescu, Thilo Kielman Presented by Bryan Rosander.
REAL-TIME COMMUNICATION ANALYSIS FOR NOCS WITH WORMHOLE SWITCHING Presented by Sina Gholamian, 1 09/11/2011.
DP-F AIR : A Simple Model for Understanding Optimal Multiprocessor Scheduling Greg Levin † Shelby Funk ‡ Caitlin Sadowski † Ian Pye † Scott Brandt † †
Trajectory-Directed Discrete State Space Modeling for Formal Verification of Nonlinear Analog Circuits Presented by Valeriy Balabanov.
RUN: Optimal Multiprocessor Real-Time Scheduling via Reduction to Uniprocessor Paul Regnier † George Lima † Ernesto Massa † Greg Levin ‡ Scott Brandt ‡
Task Allocation and Scheduling n Problem: How to assign tasks to processors and to schedule them in such a way that deadlines are met n Our initial focus:
Module 2 Priority Driven Scheduling of Periodic Task
Soft Real-Time Semi-Partitioned Scheduling with Restricted Migrations on Uniform Heterogeneous Multiprocessors Kecheng Yang James H. Anderson Dept. of.
How Many Boundaries Are Required to Ensure Optimality in Multiprocessor Scheduling? Geoffrey Nelissen Shelby Funk Dakai Zhu Joёl Goossens.
HASSO-PLATTNER-INSTITUT für Softwaresystemtechnik GmbH an der Universität Potsdam Multiprocessor Scheduling “Global Multiprocessor Scheduling of Aperiodic.
Chapter 2: Processes Topics –Processes –Threads –Process Scheduling –Inter Process Communication (IPC) Reference: Operating Systems Design and Implementation.
Chess Review May 11, 2005 Berkeley, CA Composable Code Generation for Distributed Giotto Tom Henzinger Christoph Kirsch Slobodan Matic.
CSE 421 Algorithms Richard Anderson Lecture 6 Greedy Algorithms.
1 Two-type Heterogeneous Multiprocessor Scheduling: Is there a Phase Transition? Gurulingesh Raravi, Björn Andersson and Konstantinos Bletsas CISTER-ISEP.
A processor is a person, machine, computer, or robot etc., which works on a task. To solve a scheduling problem typically the tasks are scheduled to minimize.
New Schedulability Tests for Real- Time task sets scheduled by Deadline Monotonic on Multiprocessors Marko Bertogna, Michele Cirinei, Giuseppe Lipari Scuola.
 Scheduling  Linux Scheduling  Linux Scheduling Policy  Classification Of Processes In Linux  Linux Scheduling Classes  Process States In Linux.
A Categorization of Real-Time Multiprocessor Scheduling Problems and Algorithms Presentation by Tony DeLuce CS 537 Scheduling Algorithms Spring Quarter.
Jim Anderson 1 Multiprocessor Fair Scheduling The Case for Multiprocessor Fair Scheduling James H. Anderson University of North Carolina at Chapel Hill.
The Design of an EDF- Scheduled Resource-Sharing Open Environment Nathan Fisher Wayne State University Marko Bertogna Scuola Superiore Santa’Anna of Pisa.
Multicore In Real-Time Systems – Temporal Isolation Challenges Due To Shared Resources Ondřej Kotaba, Jan Nowotsch, Michael Paulitsch, Stefan.
Quantifying the Sub-optimality of Non-preemptive Real-time Scheduling Abhilash Thekkilakattil, Radu Dobrin and Sasikumar Punnekkat.
Efficient Admission Control for Enforcing Arbitrary Real-Time Demand-Curve Interfaces Farhana Dewan and Nathan Fisher RTSS, December 6 th, 2012 Sponsors:
1 Reducing Queue Lock Pessimism in Multiprocessor Schedulability Analysis Yang Chang, Robert Davis and Andy Wellings Real-time Systems Research Group University.
Improved Human-Robot Team performance using Chaski Proceeding: HRI '11HRI '11 Proceedings of the 6th international conference on Human-robot interaction.
Analysis of Real-Time Multi-Modal FP-Scheduled Systems with Non-Preemptible Regions Authors: Masud Ahmed (presenting) Pradeep Hettiarachchi Nathan Fisher.
The Global Limited Preemptive Earliest Deadline First Feasibility of Sporadic Real-time Tasks Abhilash Thekkilakattil, Sanjoy Baruah, Radu Dobrin and Sasikumar.
Solving the Maximum Cardinality Bin Packing Problem with a Weight Annealing-Based Algorithm Kok-Hua Loh University of Maryland Bruce Golden University.
Scheduling Real-Time tasks on Symmetric Multiprocessor Platforms Real-Time Systems Laboratory RETIS Lab Marko Bertogna Research Area: Multiprocessor Systems.
CSCI1600: Embedded and Real Time Software Lecture 24: Real Time Scheduling II Steven Reiss, Fall 2015.
Multiprocessor Fixed Priority Scheduling with Limited Preemptions Abhilash Thekkilakattil, Rob Davis, Radu Dobrin, Sasikumar Punnekkat and Marko Bertogna.
Static Process Scheduling
Dynamic Priority Driven Scheduling of Periodic Task
Introduction to Real-Time Systems
Mok & friends. Resource partition for real- time systems (RTAS 2001)
Resource Augmentation for Fault-Tolerance Feasibility of Real-time Tasks under Error Bursts Abhilash Thekkilakattil, Radu Dobrin, Sasikumar Punnekkat and.
Introductory Seminar on Research CIS5935 Fall 2008 Ted Baker.
Planning and Scheduling.  A job can be made up of a number of smaller tasks that can be completed by a number of different “processors.”  The processors.
Distributed Process Scheduling- Real Time Scheduling Csc8320(Fall 2013)
Tardiness Bounds for Global EDF Scheduling on a Uniform Multiprocessor Kecheng Yang James H. Anderson Dept. of Computer Science UNC-Chapel Hill.
Multiprocessor Real-Time Scheduling
Processes and Threads Processes and their scheduling
Computer Structure Multi-Threading
Presented by: Suresh Vadlakonda Ramanjaneya Gupta Pasumarthi
Greedy Algorithms.
Richard Anderson Autumn 2015 Lecture 8 – Greedy Algorithms II
Energy Efficient Scheduling in IoT Networks
Sanjoy Baruah The University of North Carolina at Chapel Hill
TDC 311 Process Scheduling.
Lecture 11 Overview Self-Reducibility.
Limited-Preemption Scheduling of Sporadic Tasks Systems
Richard Anderson Lecture 6 Greedy Algorithms
Multiprocessor and Real-Time Scheduling
Overview of AIGA platform
Multithreaded Programming
Richard Anderson Autumn 2016 Lecture 7
CSCI1600: Embedded and Real Time Software
Planning and Scheduling
Richard Anderson Lecture 7 Greedy Algorithms
Planning and Scheduling
CS703 - Advanced Operating Systems
Richard Anderson Autumn 2016 Lecture 8 – Greedy Algorithms II
Richard Anderson Winter 2019 Lecture 7
Anand Srinivasan Department of Computer Science
Richard Anderson Autumn 2015 Lecture 7
Richard Anderson Autumn 2019 Lecture 7
Richard Anderson Autumn 2019 Lecture 8 – Greedy Algorithms II
Presentation transcript:

Partitioned Scheduling of Multimode Systems on Multiprocessor Platforms: when to do the Mode Transition? José Marinho, Gurulingesh Raravi, Vincent Nélis and Stefan Petters CISTER – ISEP Research Unit

Multi-mode systems Mode 1 Mode 2 MCR D transition time bound System is composed of set of modes Each mode is composed of a set of tasks with timing requirements Mode transition triggered by an MCR Mode transitions have to complete in a bounded time interval The length of this interval has to smaller than a given value (mode transition deadline) Mode 1 Mode 2 MCR Define MCR, tau 4 collour is messy. Be more explicit on what is a mode D transition time bound 02-12-2018 José Marinho

Partitioned Multi-mode Each processor treated as a bin For each mode a partitioning table exists Cores operate mode transitions independently Mode transition can be handled with single-core protocols straightforwardly Idle time protocol is still valid (simplest protocol) Π1 Π2 Mode 1 Π1 Π2 Mode 2 02-12-2018 Jose Marinho

Mode Independent tasks (MIT) A task may belong to more than one mode The temporal behaviour can not be perturbed by a mode change between two modes for which the task is common Task may be in the same partition across common modes Single-core protocols may be used Mode transitions core- independent Π1 Π2 Mode 1 MIT Π1 Π2 Define MIT in the text and refer it Mode 2 02-12-2018 Jose Marinho

Partitioned Multi-mode with MIT MIT task may not be in the same partition Only feasible partition assignment may lead to this Bin packing algorithm may blindly create the situation mode changes will require task migration Π1 Π2 τ5 U=0.1 τ2 U=0.46 τ3 U=0.4 Mode 1 τ1 U=0.51 τ4 U=0.5 Π1 Π2 τ3 U=0.4 τ2 U=0.46 Mode 2 τ4 U=0.5 τ6 U=0.6 02-12-2018 Jose Marinho

Transition Example Mention why this is different from single core 02-12-2018 Jose Marinho

Discussion Global scheduling during the mode transition is required when task migration exist Some Multi-mode task-sets with MIT may not need migrations at all Create a sufficient test to check if migrations are not needed Derive resource augmentation bound Bin packing procedure system-wide that ensures no task migration or minimizes migrations Remove unfeasible mode transitions 02-12-2018 Jose Marinho

Discussion Global scheduling during the mode transition is required when task migration exist Some Multi-mode task-sets with MIT may not need migrations at all Create a sufficient test to check if migrations are not needed Track the maximum admissible interference of MIT’s Derive resource augmentation bound Is task migration sufficient or would job migration be more efficient Bin packing procedure system-wide that ensures no task migration or minimizes migrations Remove unfeasible mode transitions 02-12-2018 Jose Marinho