1

Real-Time Information Dissemination

Presented by

Ben Taylor

ECE 555Real-Time Embedded Systems

2

Outline

• Introduction– What is information dissemination?

• Solutions– System model– Feedback Control Theory Solutions

• Results and Performance

• Summary

3

What is Information Dissemination?

• Publishers and Consumers of information, known as subscribers– Specify constraints on data, metadata– High subscriber count

• Sensor networks, surveillance systems, etc

• Controlled response time– Information is valuable in a specific time period

• Valuable Information at the Right Time (VIRT)

4

Metadata Matching

• Metadata matching of constraints– Can’t reevaluation all subscriptions in each control

period– High-Priority tasks reevaluated within bounded

response time– Number of low priority tasks maximized, QoS

• Cost to evaluate a subscription varies at runtime– Changing number of publishers and consumers– Complexity of constraint– Unpredictable update arrival time

• How to achieve bounded response time?

5

Feedback Controller

Set point for

average

response time

Job budget in kth

control period

6

System Modeling

• System identification approach– r(k) = Σair(k-i) + Σbin(k-i)

– i from 1 to na and nb

• Use Least Squared Method with white noise to validate models– na = 0

– nb = 1

• System model r(k) = b1n(k-1)

7

Root-Locus Design

• A PI controller– Integral is used to help eliminate steady-state

error– No derivative because it can amplify noise

• In the Z-domain F(z) = K1(z-K2)/(z-1)

– K1 = 1 / b1

– K2 = 0

• G(z) = z-1

8

Model Variation

• System model is not perfect. Need to handle variation• The system model is approximately linear between response time and subscription reevaluation.• Model system as r(k) = gb1n(k-1)

– Execution time factor g = b`1/b1

9

Stability

• Real system model, updated based on variation parameter

• G(z) = g / (z – (1 – g)– |1 – g| < 1

• Poles need to be within unit circle

• Stable as long as 0 < g < 2

10

Steady State Error

• The steady state of the system is derived– limz->1 (z - 1) G(z) Rref (z / (z - 1))

– limz->1 gz / (z – (1-g)) Rref

– Rref

• Thus the system is guaranteed to achieve the response time if the system is stable

11

Settling Time

• r(k) = (1 – g) r(k – 1) + gRref

• Settles when the systems converges to Rref ± 0.05

• The number of control periods required to settle is– k ≥ ln 0.05 / ln |1 - g|

12

Implementation

• Assumption that updates arrive in 2 – 5 second intervals– Current work to relax this assumption

• 1 second set point

13

Baselines

• OPEN– Fixed job budget– Can guarantee response time when estimated

execution time is correct– May violate timing when execution time is

underestimated

• Ad Hoc– Heuristic-based adaptive controller– Fixed step increments each control period based on

whether response time is above or below set point.

14

Control Accuracy

• Starts using design time execution estimates (ie g=1)

• At time 1000, execution time increases to g=1.4

• At time 2000, g=1.8• OPEN fails to handle

changes in execution time

• PI controller meets deadlines and settling time design

15

Comparison to Ad Hoc

• Starts out with g = 0.6• At 800s, g = 1• Ad Hoc takes 380s to settle vs 100s for PI controller

16

Quality of Service (QoS)

• Open only considered when g ≤ 1

• PI controller offers better QoS than both OPEN and Ad Hoc

17

Different Time Factors

• Relationship between response time and execution factor

• When g=2.6, the controller oscillates

• The response time stays close to the set point when the execution time factor is between 0 and 2

18

Settling TimeResults vs Theoretical

• The experimental results are very close to the theoretical values predicted

• Experiments validate the theoretical analysis

19

Summary

• Real-time information dissemination is used to share information in timely manner– Valuable Information at the Right Time (VIRT)

• PI controller maintains response time guarantees within settling time constraints with no steady state error

• Superior performance to OPEN and Ad Hoc (heuristic) controllers

20

Chronos: Feedback Control of a Real Database System

Performance

Presented by

Ben Taylor

ECE 555Real-Time Embedded Systems

21

Outline

• Introduction– What does a real-time database offer that

existing databases do not?

• Solutions– Feedback controller– Adaptive update policy

• Results and Performance

• Summary

22

Why real-time databases?

• Existing databases have no notion of data freshness or timing deadlines– Stock trading system needs to keep prices up

to date while supporting reasonable response times

• Need soft real-time constraints on transactions while maintaining up-to-date data

23

Architecture Overview

24

Controller Design

• If the system is overloaded the queue will tend toward unbounded growth

• If the system is underused, the queue size will tend to be small or empty

• The controlled variable is the service delay

• The manipulated variable is the ready queue size– If queue is full, transactions are not accepted

25

Feedback Controller Overview

• At kth sampling, calculate delay error e(k) = Ds – d(k)

• Compute δq(k) based on e(k)• If δq(k) < 0

– Adjust adaptive update policy by increasing the period of cold data and increase δq(k) by (p[i]new – p[i])/p[i] until δq(k) ≥ 0 or period max

• q(k) = q(k-1) + δq(k)– 0 ≤ q(k) ≤ max_qsize

26

Freshness Adaptation

• Control of data di, period of p[i]• Initially p[i] = 0.5 avi[i], absolute validity interval• AUR[i] = Access Frequency[i] / Update

Frequency[i]– di is hot if AUR[i] ≥ 1– Otherwise it is cold

• When increasing p[i]new = min(p[i]/AUR[i], Pmax)• After each update period, fvi[i]new = 2p[i]new,

where fvi[i] = avi[i] intially• avi[i] ≤ fvi[i]new ≤ 2Pmax

27

System Identification

• Used to model relationship between the service delay and the queue size

• PI controller in the z domain

• Root Locus method in Matlab, similar to EUCON, to show controller is stable

28

Performance

• Open - Pure Berkeley DB– Standard state-of-the-art database

• AC – Ad-hod Admission Control– Admission control in proportion to error

• FC-C – Feedback Control AC– Admission control with feedback loop

• FC-CU – Feedback Control AC + AUP– Adaptive temporal updates and admission

control with feedback loop

29

Performance Comparison

Ds = 2s, Do = 2.5s, Dt = 100s, Pmax = 5s

30

Summary

• Real-time databases need to balance timely response with fresh data

• Designed feedback controller to manage backlog in system

• Adaptive update policy to manage freshness based on temporal data access and update patterns

31

Comparisons

• Both use system identification for controller design, different models

• Chronos system maintains data freshness, a component not in the Information dissemination system

• Chronos system controller handles concurrency issues not present in Information dissemination system

32

Critiques

• Both assume inter-arrival times of a limited window (2s, 5s) and (1s, 3s)

• Chronos systems states that workloads outside of operating range is reserved for a future work

• Information Dissemination assumes a given number of subscriptions will have the same cost as a different set of subscriptions the same size

33

Questions