1

Maintaining Logical and Temporal Consistency

in RT Embedded Database Systems

Krithi Ramamritham

2

Overview• Motivation

• Problems in real-time database systems (RTDBs)

• Contributions

• Proposed solutions for Real-Time Databases: – Maintaining temporal consistency – A novel principle to assign deadlines & periods for real-time update

transactions

• Conclusions

• Other contributions & future work

3

Motivation

• Embedded applications need access to time varying data– e.g., temperature, pressure, etc.

• Need sufficiently fresh data for informed decisions, especially for real-time applications.

• A RTDB provides necessary support– executes transactions within deadlines– maintains temporal consistency of data

data values valid only for limited time

• A RTDB is a core component of real-time applications– Air traffic control– Avionics – Process control – Intelligent networks

4

RTDB Model for Maintaining Temporal Validity of Real-Time Data

Real-TimeDatabases

Network

Sensor 1

Sensor 2

Sensor N

. . . .

• A real-time object in RTDBs models a real world entity, e.g., position of an aircraft• Values are sampled by sensors, and propagated to RTDBs (propagation delay >= 0)• Real-time data in RTDBs must remain fresh in order to react to abnormal situations timely

• Transactions may be triggered to deal with abnormal situations

5

What is Data Temporal Consistency in RTDBs?

Temporal Consistency: keep data valid relative to real world

Time

Value

X

• Real-time data values change continuously• Data values are sampled periodically• A validity interval is associated with a data value• Within validity interval, a data value is temporally valid

– deviation from real world is acceptable

0 1 2 3 4 5

6

Applications with Temporal Consistency

in RTDBs• Air traffic control:

– Real-time data: aircraft position, speed, direction, altitude, etc.

• 20,000 data entities

• validity intervals of 1 ~ 10 seconds

• Process control:– Real-time data: pressure, temperature, flow rate, etc.

• Intelligent network: network services databases– Real-time data: network traffic management data

e.g., bandwidth utilization, buffer usage

7

• Our focus:

real-time data

& their impact on transactions

• Traditional RTDB work focuses on transactions with deadlines

• Commercial RTDBs– EagleSpeed: meeting timing constraints

– ClustRa: providing real-time response for telecom applications

8

Commercial RTDB Examples

• EagleSpeed TM – meeting timing constraints (Lockheed Martin Corp. Ocean, Radar & Sensor Systems)

– Quick, efficient and deterministic processing – Predictable response time– Concurrency control and deadlock prevention– Priority inheritance for transactions– Fault tolerance

• Applications – Navy’s AN/BSY-2 combat system– The AN/SQQ-89 (V) 14 and (V) 15 surface ship systems – The C130 AEWC mission systems

• For more info: http://www.lmco.com/orss/eaglespeed

9

Commercial RTDB Examples, Continued

• ClustRa -- supporting commercial telecom applications• Real-time requirements:

– Real-time response: <100 ms– High throughput: > 1000 transactions/second– High availability: < 2 minutes unavailable per year– High scalability: worldwide telephony network

• Applications:– Next generation network service (Telcordia)– Global mobility management (Inmarsat)– Intelligent telecom network (Oddvar Hesjedal)– Real-time network management

• For more info: http://www.clustra.com

10

Maintaining Temporal consistency-- Strict Transaction Correctness in

RTDBs• Observation: Validity intervals of accessed data may

expire if a transaction does not complete in time

• Transaction correctness criterion:

A transaction can commit if and only if

– it is logically consistent,

– it meets its deadline, and– at the commit time, the objects it has read are still

temporally consistent.

11

Data-Deadline in RTDBs

ValidXValidY

DeadlineTTime

T : Transaction

X,Y : Data

t0

BeginT

t1

RT(Y)

t2 t5

• Our novel concept: Data-Deadline (DDL)– derived from data validity and transaction deadline

• DDL t (T) = min (deadline(T), min(End_Valid(X)))

– T: a transaction – t: time – X: real-time data read by T by time t– End_Valid(X): end of validity interval of X

DDLt2(T) = t5

DDLt3(T) = t4

RT(X)

t3 t4

AbortT

12

Novel Transaction Scheduling Algorithms:

Data-Deadline based Scheduling • Data-Deadline based scheduling

– Data-Deadline based Least Slack First (DDLSF) slack t (T) = DDL t (T) - t - Estimated Remaining Execution Time t (T)

– Earliest Data-Deadline First (EDDF)

DDL t (T)t

slack t (T)E Time

13

Novel Transaction Scheduling Algorithms:Forced Wait

• If T can commit before X becomes invalid, T reads X.

• Otherwise, T waits for the next version of X.

BeginTWait ?

ValidX

DeadlineT Time

T : Transaction

X : Data

t1 t2 t4t3

ValidX

RT(X)

t5

CommitT

14

Novel Transaction Scheduling Algorithms:Similarity

BeginT

ValidX

DeadlineTTime

T : Transaction

X : Data

t1 t2 t3

ValidX

RT(X)

t5

Commit ?

t4

• If a version read is similar to the current version, the transaction can commit

15

Novel Transaction Scheduling Algorithms:

Forced Wait + Similarity

ValidX

ValidY

DeadlineTTime

T : Transaction

X,Y : Data

t0

BeginT

t1

RT(Y)

t2 t4

RT(X)

t5t3

Wait? Commit?

t6

ValidX

ValidY

• Forced wait is employed when a data is read• Similarity is employed at commit time

16

Summary of Experimental Results: Temporal Consistency Maintenance

• Data-Deadline based policies alone do not improve performance significantly

• Forced Wait improves performance significantly

(reduces missed deadline percentage up to 50% )

• Similarity improves performance

(reduces missed deadline percentage up to 30%)

• When combined with Similarity, Forced Wait plays a dominant role in enhancing performance

17

Proposed Solutions & Results

• Maintaining Temporal Consistency

• Assign Deadlines & Periods to RT Update Transactions

18

Assign Periods & Deadlines-- Problem & Goals

• Problem domain:

– maintaining temporal validity of real-time data by periodic update transactions

• Goals: assigning periods and deadlines s.t.

– update transactions can be guaranteed to complete by their deadlines

– the imposed workload is minimized

• Problems with traditional approaches

• Proposed solution and performance results

• Summary

19

Problem : Maintaining Temporal Validity of Real-Time

DataV

t+Vt

V : Validity length

t’+Vt’

V

• Real-time data refreshed by periodic update sensor transactions– X has to be refreshed before its validity interval expires– validity duration updated upon refresh

• How to maintain the validity of data while minimizing the workloads due to update transactions ?

20

Traditional Approach: Half-Half -- Sample at twice the rate of change: P = D =

V/2

Definition:

• X : Real-Time Data

• V : Validity Interval Length

• T : Trans Updating X

• P : Period of T

• D : Deadline of T

• C : Computation Time of T V

Problem : Imposes unnecessarily high workload

t

P=D

t+V/2 t +Vt

Observation : Data validity can be guaranteed if Period + Deadline <= Validity LengthWorkload : C / P = 2C / V

P=D

D

t t+V/2 t +Vt

P

P more than V/2 & D less than V/2 !

21

Deriving Deadlines and Periods:Intuition of More-Less Principle

• Data validity can be guaranteed if Period + Relative Deadline <= Validity Length (1)

• To reduce the workload (C/P) imposed by T without

violating (1) :

Increase period to be more than half of validity length

Decrease relative deadline to be less than half of validity length

• If relative deadline <= period,

deadline monotonic scheduling is an optimal fixed

priority scheduling alg

22

For a set of transactions {Ti} 1 <= i <= m • Validity Constraint (to ensure data validity) : Period + Deadline <= Validity Length

More-Less Principle: Definition

• Deadline Constraint (to reduce workload) : Computation Time <= Deadline <= Period

• Schedulability Constraint (by deadline monotonic) :

Response time of the 1st instance <= Deadline

Note: 1st instance response time is the longest response time if all transactions start at same time

Question: Is More-Less always better than Half-Half ?

23

More-Less Principle: P & D

T1T2

C V1 32 20

Parameters

Half-Half

T1

T2

D P

1.5 1.5

10 10

Utilization : 1/1.5 + 2/10 = 0.867

More-Less (priority order: T1 > T2)

T1

T2

D P

1 2

4 16

Utilization : 1/2 + 2/16 = 0.625 <

Determining deadline and period of a transaction in More-Less:Deadline: D = Response time of the 1st instance;

Period : P = Validity Length - Deadline;

Does the priority order T2 > T1 produce same P and D?

Is more-less always better than half-half ?

24

More-Less Better than Half-Half

• Theorem: {Ti} can be scheduled by

(Half-Half , any fixed priority scheduling alg)

{Ti} can be scheduled by

(More-Less, Deadline Monotonic scheduling)

• The reverse is not true

• Question: How to determine transaction priority order s.t. load is minimized under More-Less ?

25

Shortest Validity First• Shortest Validity First (SVF)

– assign orders to transactions

• in the inverse order of validity interval length

• resolve ties in favor of a transaction with less slack (V - C)

– is optimal under certain restrictions

26

Shortest Validity First: Summary

• Restrictions:

1) Ci <= min (Vj /2)

2) C2 - C1 <= 2(V2 - V1) (i.e., the increase of computation time is less than twice the increase in validity interval length),

• If restrictions (1) & (2) hold, SVF is optimal

• If only restriction (1) holds, SVF is near optimal

• In general, SVF is a good heuristic (shown in experiments)

27

Applications & Experimental Results• Application of More-Less: Avionics Systems

• Experimental Parameters : Ci = [5,15] ms & Vi = [4000,8000] ms

• Experimental results show that More-Less provides lower CPU workload and better schedulability than Half-Half

0

0.2

0.4

0.6

0.8

1

1.2

1.4

100 150 200 250 300 350 375

Half-HalfMore-Less

No. of Sensor Transactions

CP

U

Wor

kloa

d

More-Less can deal with 30% more load !!!

28

Experiments: Coscheduling of Mixed Workloads

-- Performance of Sensor Transactions

0

0.05

0.1

0.15

0.2

100 150 200 250 300 350

Half-Half(Sensor)

More-Less(Sensor)

No. of Sensor Transactions

Mis

sed

Dea

dlin

e R

atio

Mixed Workloads: Sensor Transaction Class &Triggered Transaction ClassTransaction Scheduling:

High priority class: Sensor -- Deadline monotonic schedulingLow priority class:Triggered -- Earliest deadline first scheduling

29

Experiments: Coscheduling of Mixed Workloads

-- Performance of Triggered Transactions

0

0.2

0.4

0.6

0.8

1

100 150 200 250 300 350

Half-Half(Trigger)

More-Less(Trigger)

No. of Sensor Transactions (Triggered Trans Arrival Rate = 10 trans/s)

Mis

sed

Dea

dlin

e R

atio

More-Less improves perf. of triggered transactions by 80%!

30

Conclusions

• Development and evaluation of efficient and novel approaches to temporal consistency maintenance

– concept of data-deadline and related scheduling algorithms

– forced-wait

– similarity

• Development, analysis, and evaluation of More-Less, an efficient principle for maintaining real-time data validity