Priority-Based Scheduling (Periodic Tasks) RMS: Rate Monotonic Scheduling. Example Priority Assignment

Similar documents
Rate Monotonic (RM) Disadvantages of cyclic. TDDB47 Real Time Systems. Lecture 2: RM & EDF. Priority-based scheduling. States of a process

Real-Time Process Scheduling

Figure 1. Inventory Level vs. Time - EOQ Problem

A New Quality of Service Metric for Hard/Soft Real-Time Applications

Series Solutions of ODEs 2 the Frobenius method. The basic idea of the Frobenius method is to look for solutions of the form 3

Schedulability Bound of Weighted Round Robin Schedulers for Hard Real-Time Systems

Case Study: Load Balancing

An Analysis of Task Scheduling for a Generic Avionics Mission Computer

Energy-Efficient Design in Wireless OFDMA

Modern Problem Solving Techniques in Engineering with POLYMATH, Excel and MATLAB. Introduction

Modeling and Analysis of 2D Service Differentiation on e-commerce Servers

Period and Deadline Selection for Schedulability in Real-Time Systems

benefit is 2, paid if the policyholder dies within the year, and probability of death within the year is ).

Optimal Adaptive Voice Smoother with Lagrangian Multiplier Method for VoIP Service

Power Low Modified Dual Priority in Hard Real Time Systems with Resource Requirements

1. Fundamentals of probability theory 2. Emergence of communication traffic 3. Stochastic & Markovian Processes (SP & MP)

1. Math 210 Finite Mathematics

An Analysis of Central Processor Scheduling in Multiprogrammed Computer Systems

Credit Limit Optimization (CLO) for Credit Cards

Checkng and Testng in Nokia RMS Process

Thursday, December 10, 2009 Noon - 1:50 pm Faraday 143

This article appeared in a journal published by Elsevier. The attached copy is furnished to the author for internal non-commercial research and

Level Annuities with Payments Less Frequent than Each Interest Period

Chapter 6. Demand Relationships Among Goods

An Adaptive Cross-layer Bandwidth Scheduling Strategy for the Speed-Sensitive Strategy in Hierarchical Cellular Networks

Performance attribution for multi-layered investment decisions

THE METHOD OF LEAST SQUARES THE METHOD OF LEAST SQUARES

Study on Model of Risks Assessment of Standard Operation in Rural Power Network

A generalized hierarchical fair service curve algorithm for high network utilization and link-sharing

Module 2 LOSSLESS IMAGE COMPRESSION SYSTEMS. Version 2 ECE IIT, Kharagpur

Forecasting the Direction and Strength of Stock Market Movement

Compiling for Parallelism & Locality. Dependence Testing in General. Algorithms for Solving the Dependence Problem. Dependence Testing

The Greedy Method. Introduction. 0/1 Knapsack Problem

Multiple stage amplifiers

Dynamic Scheduling of Emergency Department Resources

A New Paradigm for Load Balancing in Wireless Mesh Networks

DECOMPOSITION ALGORITHM FOR OPTIMAL SECURITY-CONSTRAINED POWER SCHEDULING

A Replication-Based and Fault Tolerant Allocation Algorithm for Cloud Computing


Enabling P2P One-view Multi-party Video Conferencing

Peer-to-peer systems have attracted considerable attention

Finite Math Chapter 10: Study Guide and Solution to Problems

A Generic and Compositional Framework for Multicore Response Time Analysis

Evaluation of Delay Performance in Valiant Load-balancing Network

Adaptive Voice Smoother with Optimal Playback Delay for New Generation VoIP Services

MAC Layer Service Time Distribution of a Fixed Priority Real Time Scheduler over

Realistic Image Synthesis

2008/8. An integrated model for warehouse and inventory planning. Géraldine Strack and Yves Pochet

Resource Scheduling Scheme Based on Improved Frog Leaping Algorithm in Cloud Environment

Activity Scheduling for Cost-Time Investment Optimization in Project Management

Polling Cycle Time Analysis for Waited-based DBA in GPONs

Session-Based Overload Control in QoS-Aware Web Servers

Vasicek s Model of Distribution of Losses in a Large, Homogeneous Portfolio

Optimal outpatient appointment scheduling

AD-SHARE: AN ADVERTISING METHOD IN P2P SYSTEMS BASED ON REPUTATION MANAGEMENT

Lecture 2: Single Layer Perceptrons Kevin Swingler

Online Advertisement, Optimization and Stochastic Networks

8.4. Annuities: Future Value. INVESTIGATE the Math Annuities: Future Value

End-to-end measurements of GPRS-EDGE networks have

Mining Feature Importance: Applying Evolutionary Algorithms within a Web-based Educational System

Medium and long term. Equilibrium models approach

A Load-Balancing Algorithm for Cluster-based Multi-core Web Servers

Determination of Integrated Risk Degrees in Product Development Project

Economic Models for Cloud Service Markets Pricing and Capacity Planning

Distributed Optimal Contention Window Control for Elastic Traffic in Wireless LANs

Logical Development Of Vogel s Approximation Method (LD-VAM): An Approach To Find Basic Feasible Solution Of Transportation Problem

Feature selection for intrusion detection. Slobodan Petrović NISlab, Gjøvik University College

Statistical algorithms in Review Manager 5

WAN Network Design. David Tipper Graduate Telecommunications and Networking Program. Slides 10 Telcom 2110 Network Design. WAN Network Design

Revenue Management Games

J. Parallel Distrib. Comput. Environment-conscious scheduling of HPC applications on distributed Cloud-oriented data centers

Efficient On-Demand Data Service Delivery to High-Speed Trains in Cellular/Infostation Integrated Networks

Valuing Customer Portfolios under Risk-Return-Aspects: A Model-based Approach and its Application in the Financial Services Industry

Sciences Shenyang, Shenyang, China.

Retailers must constantly strive for excellence in operations; extremely narrow profit margins

The Development of Web Log Mining Based on Improve-K-Means Clustering Analysis

DEFINING %COMPLETE IN MICROSOFT PROJECT

Little s Law & Bottleneck Law

A Priority Queue Algorithm for the Replication Task in HBase

An Optimal Model for Priority based Service Scheduling Policy for Cloud Computing Environment

2. SYSTEM MODEL. the SLA (unlike the only other related mechanism [15] we can compare it is never able to meet the SLA).

What is Candidate Sampling

Bargaining at Divorce: The Allocation of Custody

INVESTIGATION OF VEHICULAR USERS FAIRNESS IN CDMA-HDR NETWORKS

The Load Balancing of Database Allocation in the Cloud

Conferencing protocols and Petri net analysis

IWFMS: An Internal Workflow Management System/Optimizer for Hadoop

Evaluating the Effects of FUNDEF on Wages and Test Scores in Brazil *

This article appeared in a journal published by Elsevier. The attached copy is furnished to the author for internal non-commercial research and

NONLINEAR OPTIMIZATION FOR PROJECT SCHEDULING AND RESOURCE ALLOCATION UNDER UNCERTAINTY

Analysis of Energy-Conserving Access Protocols for Wireless Identification Networks

A hybrid global optimization algorithm based on parallel chaos optimization and outlook algorithm

QBox: Guaranteeing I/O Performance on Black Box Storage Systems

Economic Models for Cloud Service Markets

ivoip: an Intelligent Bandwidth Management Scheme for VoIP in WLANs

Trade Adjustment and Productivity in Large Crises. Online Appendix May Appendix A: Derivation of Equations for Productivity

Project Networks With Mixed-Time Constraints

Latent Class Regression. Statistics for Psychosocial Research II: Structural Models December 4 and 6, 2006

Proceedings of the Annual Meeting of the American Statistical Association, August 5-9, 2001

Answer: A). There is a flatter IS curve in the high MPC economy. Original LM LM after increase in M. IS curve for low MPC economy

Transcription:

Prorty-Based Shedulng (Perod Tasks) A preemptve method here the prorty of the proess determnes hether t ontnues to run or s dsrupted (most mportant proess frst) On-lne sheduler (does not preompute shedule) Fxed prortes: same prorty to all os n a task Dynam prortes: dfferent prortes to ndvdual os n eah task task-level dynam prortes o-level fxed prortes MS: ate Monoton Shedulng On-lne Preemptve Prorty-ased th stat prortes Perod T that s the shortest nterval eteen ts arrval tmes Proesses are assgned prortes dependent on length of T, the shorter t s, the hgher the prorty (or the hgher the rate, the hgher the prorty) T < T P > P M algorthm or MS Example Prorty Assgnment Proess Perod Prorty a 0 4 d 0 e

Example P P P Perod (T) 0 0 WET (e) 0 0 Prorty hgh lo medum arrval tme proess t0 P,P,P t P t0 P t40 P t0 P t0 P,P 0 0 0 40 0 0 0 preempton tme Shedulalty Test For n proesses, MS ll guarantee ther shedulalty f the total utlzaton U does not exeed the guarantee level G n * ( /n - ) When test fals: try th the orst ase: assume that all proesses are released smultaneously at tme 0, and then arrve aordng to ther perods hek hether eah proess meets ts deadlne for all releases efore the frst deadlne for the proess th the loest prorty Otherse: hange U y redung (ode optmzaton, faster proessor, ) or nrease T for some proess (possle?) Proess Set A Proess Perod omputatontme Prorty Utlzaton T P U a 0 0.4 40 0 0. 0 0 0. The omned utlzaton s 0.8 (or 8%) Ths s aove the threshold for three proesses (0.8) and, hene, ths proess set fals the utlzaton test

Tmelne for Proess Set A Proess a Proess elease Tme Proess ompleton Tme Deadlne Met Proess ompleton Tme Deadlne Mssed Preempted Exeutng 0 0 0 40 0 0 Tme Proess Set B Proess Perod omputatontme Prorty Utlzaton T P U a 80 0.400 40 0. 4 0. The omned utlzaton s 0. Ths s elo the threshold for three proesses (0.8) and, hene, ths proess set ll meet all ts deadlnes Proess Set Proess Perod omputatontme Prorty Utlzaton T P U a 80 40 0.0 40 0 0. 0. The omned utlzaton s.0 Ths s aove the threshold for three proesses (0.8) ut the proess set ll meet all ts deadlnes

Tmelne for Proess Set Proess a 0 0 0 40 0 0 Tme 0 80 esponse Tme Analyss Here task 's orst-ase response tme,, s alulated frst and then heked (trvally) th ts deadlne D I Where I s the nterferene from hgher prorty tasks alulatng Durng, eah hgher prorty task ll exeute a numer of tmes: Numer of eleases T The elng funton gves the smallest nteger greater than the fratonal numer on hh t ats. So the elng of / s, of / s, and of / s. Total nterferene s gven y: T 4

eponse Tme Equaton hp T % & ) ( Where hp() s the set of tasks th prorty hgher than task Solve y formng a reurrene relatonshp: hp n n T % & ) ( The set of values s monotonally non dereasng When the soluton to the equaton has een found, must not e greater than (e.g. 0 or ) n n,..,...,,, 0 n 0 Proess Set D Proess Perod omputatontme Prorty T P a a 0 4 4 4 0 4

evst: Proess Set Proess Perod omputatontme Prorty esponse tme T P a 80 40 80 40 0 The omned utlzaton s.0 Ths as aove the ullzaton threshold for three proesses (0.8), therefore t faled the test The response tme analyss shos that the proess set ll meet all ts deadlnes TA s neessary and suffent esponse Tme Analyss Is suffent and neessary If the proess set passes the test they ll meet all ther deadlnes; f they fal the test then, at run-tme, a proess ll mss ts deadlne (unless the omputaton tme estmatons themselves turn out to e pessmst) Exat Shedulalty Test for (eah task T ) { I 0; do { } I f ( > d ) return UNSHEDULABLE; I /p k k ; /* k..- */ } hle (I > ) return SHEDULABLE;

Deadlne-Monoton Algorthm (DM) Fxed-prorty Uses relatve deadlnes: the shorter the relatve deadlne, the hgher the prorty M and DM are dental f the relatve deadlne s proportonal to ts perod Otherse DM performs etter n the sense that t an sometmes produe a feasle shedule hen M fals, hle M alays fals hen DM fals Example T T T 0 0 00 0 0 0. 8. T T T 0 0 00 0 0 0. 8.