Task is a schedulable entity, i.e., a thread



Similar documents
Multiprocessor Systems-on-Chips

Improvement of a TCP Incast Avoidance Method for Data Center Networks

Single-machine Scheduling with Periodic Maintenance and both Preemptive and. Non-preemptive jobs in Remanufacturing System 1

OPERATION MANUAL. Indoor unit for air to water heat pump system and options EKHBRD011ABV1 EKHBRD014ABV1 EKHBRD016ABV1

CALCULATION OF OMX TALLINN

Task-Execution Scheduling Schemes for Network Measurement and Monitoring

The Application of Multi Shifts and Break Windows in Employees Scheduling

A Note on Using the Svensson procedure to estimate the risk free rate in corporate valuation

Vector Autoregressions (VARs): Operational Perspectives

Optimal Investment and Consumption Decision of Family with Life Insurance

TSG-RAN Working Group 1 (Radio Layer 1) meeting #3 Nynashamn, Sweden 22 nd 26 th March 1999

AP Calculus AB 2013 Scoring Guidelines

Chapter 8: Regression with Lagged Explanatory Variables

Feedback-Feedforward Scheduling of Control Tasks

II.1. Debt reduction and fiscal multipliers. dbt da dpbal da dg. bal

Network Discovery: An Estimation Based Approach

A Re-examination of the Joint Mortality Functions

Dynamic programming models and algorithms for the mutual fund cash balance problem

Model-Based Monitoring in Large-Scale Distributed Systems

Ecotopia: An Ecological Framework for Change Management in Distributed Systems

PROFIT TEST MODELLING IN LIFE ASSURANCE USING SPREADSHEETS PART ONE

Forecasting Sales: A Model and Some Evidence from the Retail Industry. Russell Lundholm Sarah McVay Taylor Randall

Trends in TCP/IP Retransmissions and Resets

DYNAMIC MODELS FOR VALUATION OF WRONGFUL DEATH PAYMENTS

Real-time Particle Filters

The Journey. Roadmaps. 2 Architecture. 3 Innovation. Smart City

11/6/2013. Chapter 14: Dynamic AD-AS. Introduction. Introduction. Keeping track of time. The model s elements

Economics Honors Exam 2008 Solutions Question 5

Distributing Human Resources among Software Development Projects 1

Capacity Planning and Performance Benchmark Reference Guide v. 1.8

Performance Center Overview. Performance Center Overview 1

Chapter 8 Student Lecture Notes 8-1

Cointegration: The Engle and Granger approach

Markit Excess Return Credit Indices Guide for price based indices

Automatic measurement and detection of GSM interferences

Chapter 1.6 Financial Management

Towards Intrusion Detection in Wireless Sensor Networks

Journal Of Business & Economics Research September 2005 Volume 3, Number 9

Automated Allocation of ESA Ground Station Network Services

UNDERSTANDING THE DEATH BENEFIT SWITCH OPTION IN UNIVERSAL LIFE POLICIES. Nadine Gatzert

MODEL AND ALGORITHMS FOR THE REAL TIME MANAGEMENT OF RESIDENTIAL ELECTRICITY DEMAND. A. Barbato, G. Carpentieri

Chapter 7. Response of First-Order RL and RC Circuits

Option Put-Call Parity Relations When the Underlying Security Pays Dividends

The Grantor Retained Annuity Trust (GRAT)

Chapter 5. Aggregate Planning

Sampling Time-Based Sliding Windows in Bounded Space

Present Value Methodology

Nikkei Stock Average Volatility Index Real-time Version Index Guidebook

The Transport Equation

DDoS Attacks Detection Model and its Application

Making a Faster Cryptanalytic Time-Memory Trade-Off

Payment Plans of Reverse Mortgage System in the Korean. Housing Market. Deokho Cho a, Seungryul Ma b,

Mining Web User Behaviors to Detect Application Layer DDoS Attacks

Q-SAC: Toward QoS Optimized Service Automatic Composition *

Longevity 11 Lyon 7-9 September 2015

Strategic Optimization of a Transportation Distribution Network

The Greek financial crisis: growing imbalances and sovereign spreads. Heather D. Gibson, Stephan G. Hall and George S. Tavlas

AP Calculus AB 2007 Scoring Guidelines

How To Optimize Time For A Service In 4G Nework

CRISES AND THE FLEXIBLE PRICE MONETARY MODEL. Sarantis Kalyvitis

AP Calculus AB 2010 Scoring Guidelines

Fair Stateless Model Checking

Analogue and Digital Signal Processing. First Term Third Year CS Engineering By Dr Mukhtiar Ali Unar

Using Batteries to Reduce the Power Costs of Internet-scale Distributed Networks

A Load Balancing Method in Downlink LTE Network based on Load Vector Minimization

A Universal Pricing Framework for Guaranteed Minimum Benefits in Variable Annuities *

A Resource Management Strategy to Support VoIP across Ad hoc IEEE Networks

Efficient One-time Signature Schemes for Stream Authentication *

Niche Market or Mass Market?

Mathematics in Pharmacokinetics What and Why (A second attempt to make it clearer)

BALANCE OF PAYMENTS. First quarter Balance of payments

USE OF EDUCATION TECHNOLOGY IN ENGLISH CLASSES

Distributed and Secure Computation of Convex Programs over a Network of Connected Processors

The Interest Rate Risk of Mortgage Loan Portfolio of Banks

TEMPORAL PATTERN IDENTIFICATION OF TIME SERIES DATA USING PATTERN WAVELETS AND GENETIC ALGORITHMS

GoRA. For more information on genetics and on Rheumatoid Arthritis: Genetics of Rheumatoid Arthritis. Published work referred to in the results:

E0 370 Statistical Learning Theory Lecture 20 (Nov 17, 2011)

Research on Inventory Sharing and Pricing Strategy of Multichannel Retailer with Channel Preference in Internet Environment

Energy and Performance Management of Green Data Centers: A Profit Maximization Approach

What does the Bank of Russia target?

IMPLICIT OPTIONS IN LIFE INSURANCE CONTRACTS FROM OPTION PRICING TO THE PRICE OF THE OPTION. Tobias Dillmann * and Jochen Ruß **

CHARGE AND DISCHARGE OF A CAPACITOR

The naive method discussed in Lecture 1 uses the most recent observations to forecast future values. That is, Y ˆ t + 1

Adaptive Optics PSF reconstruction at ALFA

MACROECONOMIC FORECASTS AT THE MOF A LOOK INTO THE REAR VIEW MIRROR

SELF-EVALUATION FOR VIDEO TRACKING SYSTEMS

CLOCK SKEW CAUSES CLOCK SKEW DUE TO THE DRIVER EROSION OF THE CLOCK PERIOD

Transcription:

Real-Time Scheduling Sysem Model Task is a schedulable eniy, i.e., a hread Time consrains of periodic ask T: - s: saring poin - e: processing ime of T - d: deadline of T - p: period of T Periodic ask T - r: rae of T (r = 1/p) e d p 0 s - 0 e d p - s: firs ime he periodic ask requires processing - a s + (k - 1) p ask T is ready for k processing - processing of T in period k mus be finished a s + (k - 1) p + d - for coninuous media asks i is assumed ha he deadline of period (k - 1) is he ready ime of period k 1 Classificaion of RT Scheduling - I Real-ime: he resul of operaions and he response ime are of imporance -> ime-dependen daa Real-Time Scheduling Sof Hard Dynamic Saic Pre-empive Non-pre-empive Pre-empive Non-pre-empive according o: S. Mullender: Disribued Sysems, Addison Wesley, 2nd Ediion, 1993, pp.491 Hard real-ime: - missing a deadline is disasrous - he deadlines of all criical asks mus be guaraneed a priori Sof real-ime: - missing a deadline is no disasrous - resul is sill of some ineres afer deadline is expired 2

Classificaion of RT Scheduling - II Dynamic scheduling - makes scheduling decisions a run-ime on he basis of curren requess - flexible o adap o an evolving ask scenario - considers only acual ask requess and execuion ime parameers - subsanial effor a run-ime o find a schedule Saic scheduling - also called pre-run-ime - makes scheduling decisions off-line - generaes a dispaching able for run-ime dispacher a compile ime - complee prior knowledge abou ask se characerisics necessary - small run-ime overhead 3 Classificaion of RT Scheduling - IV Dynamic scheduling of independen asks: - Rae Monoonic - Earlies-Deadline-Firs (EDF) - Leas Laxiy Dynamic scheduling of dependen asks: - kernelized monior - prioriy-ceiling proocol 4

Schedulabiliy Tes Service User CONNECT.reques (QoS_requiremens) Service Provider Schedulabiliy Tes Admission Conrol Nework Bandwidh CPU Time Tes o deermine wheher a schedule exiss For periodic ask ses feasible, because he fuure is known Schedulabiliy es for ask se {T i } of periodic asks: - T i has period p i, deadline d i, and processing ime e i - he ask se is only schedulable if µ = e i --- n p i 5 Rae Monoonic - I Classic scheduling algorihm for hard real-ime sysems wih one CPU Dynamic pre-empive algorihm based on saic ask prioriies Assumpions on he ask se: 1. The requess for all asks of he ask se {T i } for which hard deadline exis are periodic 2. All asks are independen of each oher. There exiss no precedence consrains or muual exclusion consrains beween any pair of asks. 3. The deadline of every ask T i is equal o is periode p i. 4. The required maximum compuaion ime of each ask e i is known a priori and is consan. 5. The ime required for conex swiches can be ignored. 6. The sum of he uilizaion facors µ of he n asks µ = c i --- n( 2 1 n 1) p i 6

Rae Monoonic - II Scheduling is done a saring ime s Assigns saic prioriies on he basis of ask periods - ask wih shores period ges highes saic prioriy - ask wih longes period ges lowes saic prioriy Run-ime dispacher always selecs ask reques wih he highes saic prioriy Task 1 Task 2 Dispaching e1 s 1 { { Task 1 has highes prioriy p 1 p 2 e 2 s 2 Task 2 has highes prioriy Pre-empion of ask 1 7 Rae Monoonic - III If all assumpions are saisfied, rae monoonic guaranees ha all asks will mee heir deadline Proof is based on criical insance: - a criical insance for a ask is he poin in ime a which he reques will have he larges response ime - for he enire sysem he criical insance occurs, when requess for all asks Task 1 Task 2 Task 3 Dispaching are made simulaneously 8

Rae Monoonic - IV Assumpion 6 can be relaxed if he ask periods are muliples of he period of he highes prioriy ask In his case, uilizaion facor µ can approach 1: µ = e i --- 1 p i 9 Earlies-Deadline-Firs (EDF) - I Opimal dynamic pre-empive algorihm based on dynamic prioriies Assumpions (1) o (5) mus hold: 1. The requess for all asks of he ask se {T i } for which hard deadlines exis are periodic 2. All asks are independen of each oher. There exiss no precedence consrains or muual exclusion consrains beween any pair of asks. 3. The deadline of every ask T i is equal o is period p i. 4. The required maximum compuaion ime of each ask e i is known a priori and is consan. 5. The ime required for conex swiches can be ignored. Processor uilizaion µ can go up o 1 independen of he ask periods 10

Earlies-Deadline-Firs (EDF) - II Afer any significan even (e.g., new ask eners sysem, ransiion from sae blocked o sae ready) he ask wih he earlies deadline is assigned he highes dynamic prioriy Wih every arriving ask, prioriies migh have o be adjused Run-ime dispacher always selecs ask reques wih he highes prioriy 11 EDF versus Rae Monoonic - I Conex swiches Task I Task II EDF Rae Monoonic 1 2 3 4 5 6 7 8 A B C D 1 A 2 3 B 4 5 C 6 7 D 8 1 A 2 3 B 4 5 C 6 7 D 8 If more han one periodic ask is processed, i is very likely ha here migh be more conex swiches in rae monoonic scheduling han in EDF 12

EDF versus Rae Monoonic - II Processor uilizaion Task I Task II EDF Rae Monoonic 1 2 3 4 5 6 7 8 A B C 1 A 2 A 3 B 4 B 5 6 C 7 C 8 1 A 2 A 3 AB 4 B 5 B 6 C 7 C 8 C Deadline Violaions - EDF: maximal uilizaion 1 - Rae-monoonic: maximal uilizaion ln2 (in special siuaions 1) 13