Real-Time Information Dissemination

1. ECE 555Real-Time Embedded Systems Real-Time Information Dissemination Presented by Ben Taylor

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. ECE 555Real-Time Embedded Systems Chronos: Feedback Control of a Real Database System Performance Presented by Ben Taylor

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