A0AII? 915 NAVAL POSTGRADUATE SCHOOL MONTEREY CA F/6 9/2DISTRIBUTED VS. CENTRALIZED DATABASE SYSTEMS - TRANSACTION EXEC-ETC(U)

I NP5 2- D Z BADALU L A S S I F I D 5 - O 0

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2 NAVAL POSTGRADUATE SCHOOLMonterey, California

DISTRIBUTED VS. CENTRALIZED DATABASE SYSTEMS -TRANSACTION EXECUTION COST AND PERFORMANCE ANALYSIS

*Dushan Z. Badal

July 1982

uJ

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NAVAL POSTGRADUATE SCHOOLMonterey, California

Rear Admiral J. J. Ekelund D. A. SchradySuperintendent Acting Provost

The work reported herein was supported in part by the Foundation ResearchProgram of the Naval Postgraduate School with funds provided by the Chief ofNaval Research.

Reproduction of all or part of this report is authorized.

This report was prepared by:

DUSHAN Z. BADALAssociate Professorof Computer Science

Reviewed by: Released by:

DAVID K. HSIAO, Chairman WILLIAM M. TOLLESDepartment of Computer Science Dean of Research

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Distributed databases, performance analysis, cost analysis, long-haul networkslocal area networks, distributed computing.

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--The purpose of this paper is twofold. First, we investigate the impact ofconcurrency control on transaction execution cost and system throughput incentralized and distributed data base systems (DBS) based on slow and fast(local) networks. Second, we show that in terms of transaction execution costand OBS throughput there are some applications for which any distributed DBScan be more effective than any centralized DBS and vice versa. We also arguethat for other applications the decision in favour of distributed or central-ized DBS should be based on the comoarison of specific DBS systems.

OD pm 1473 EDITION O I OV 65 IS OBSOLETE UNCLASSIFIEDN 0102- LF- 01.5601 SECURITY CLASSIFICATION OF THIS PAGE (fle. Date 111tnod)

Distributei vs. Centralized Database Systems

transaction execution cost and performance analysis

Cushan Z. Radal

Computer Science Department

Naval Postgraduate School

Monterey, CA 93940

Nbstract

Tne purpose of this paper is twofold. First, we investiate the

impact of concurrency zontrol on transaction execution cost anti system

throurhput in centralized and distributed Jata base systems (FAS) hasee

on slow and fast (local) networks. Second, we show that in terns if

transaction execution cost and D3S throughput there are some apolica-

tions for *hich any distributed CBS can he more affective than -ny cen-

tralized CBS and vice versa. t,;e also argue that for other applications

the -'ecision in favour of -istri'uted or centralized 2-S shoul , he 'ase

on the compnarison of specific OBS systems. Accession ForINTIS ($!A&IDTIC TABUnaunouico d

Dist ribiit ! on/Availnbility Codes

Avnil and/orDist Spccal

1. Introduction.

Distributed database systems (D-OBS) are alleged to provide numerous

advantages over centralized database systems (C-CBS). The usual argu-

ments in favour of the D-DBS are:

a. im-nproved user attitude - the distribution of data and process-

inq gives users greater control and autonomy over the Jata processing

b. improved reliability and availability - because of the parti-

tioning of data and replication of processors and data

c. improved extensibility and modularity

d. decreasing cost of hardware should make D-DPS cost effective

e. D-DBS could provide better performance and perhaps lower tran-

saction execution cost because they have inherent concurrent execution

capabilities not available in C-CBS

'Xe expect that in many applications the last consideration, i.e.

transaction execution cost and performance, will be the principal factor

when dec.ding between C-DBS and D-OBS. Therefore in this paper, we

analyze and compare the transaction execution cost and the performance

of C-CBS and D-0BS. We are here interested in two goals. First, -we

want to investigiate the importance or the impact of concurrency control

on transaction execution cost and system throughput in C-OBS and O-DBS

based on slow and fast (sometimes called local) networks. Second, we

ir3 interested in a simple and robust analysis which explains certain

intuitive notions about the preferability or suitability of D-DBS or C-

DBS for some applications. Our analysis is general , i.e. it is not

meant to represent any particular concurrency control mechanism, C-DS

2

or D-OBS. The analysis however can become specific by substituting

proper values, representing specific concurrency control mechanisms or

DBS systems, into the derived formulas.

2. Previous work.

It appears that there are no published papers dealing with compara-

tive analysis of distributed and centralized DBS systems. However,

recently some work has been done on the performance analysis of distri-

buted DBS (BAD 90, mOL 79, RIE 79) and there are two publications (BUC

79, STE 79) somewhat related to this paper. The paper by Fucci anl

Streeter (BUC 79) deals with the cost and performance analysis of a sin-

gle processor with multiple remote terminals. The paper arso contains a

short sketch of a very simple comparative cost analysis of distributed

and cen, *',ized DBS. The paper by Stewart (STE 79) describes a discrete

simulation modelling tool developed for the performance analysis of IBM

SNA system configurations. It is obvious from the paper that this per-

formance analysis tool can be used for already implemented systems as

the simulator input requires detailed system implementation information

e.g., number of instructions per program, number of I/C, a priority

interrupt mechanism, a nriority dispatcher, CPIJ lefinition, etc. How-

ever, it is indicated in the paper that the simulator could be use. for

the performance analysis of distributed OBS - presumably once it has

been implemented.

3

3. D-PBS vs. C-DBS: Transaction Execution Cost Analysis.i

In our analysis, we consider C-DBS with remote users and a D-DBS

with similar capabilities in which data and processing power are distri-

buted to the remote users. Figure 1 shows both CBS configurations we

analyze in this paper. (RU is remote user and LU is local user).

Q.UQ

4

i .7

In this paper the cost is defined in terms of the number of exe-

cuted instructions, the amount of generated I/O and the number of mes-

sages, or any subset of these. !e model the average cost of one tran-

saction processing in the C--3S as consisting of three oarts as Follows:

Cc =Ccsys +Ccom +Csyn (I)

where

ecsys is the average cost of executing one transaction in the

C-CBS without a concurrency control (CC)

Ceom is the averaqe (communication) cost of submitting the tran-

saction from the remote user to the C-CDBS

is the average CC cost, i.e. the averaqe cost due to syn-

chronization of the transaction

Since any transaction in any OBS is either conflicting (i.e., try-

ing to acquire resources already acquired by some other transaction) or

nonconflicting then Csyn consists of two types of costs - the cost ((cc)

associated with nonconflicting transaction and the cost (Coonfl) when

the transactions interfere, i.e., conflict. weL can express Cs as fol-syn

lows:

1syn DI Cconf 1 ,7cc (2)

5

where

DI, the degree of interference, is the ratio of the number of

conflicting transactions to the number of all transactions

Cconf1 is the average CC conflict resolution cost per conflict-

ing transaction

DI*Cconf1 is the average conflict resolution cost per transac-

tion

Ccc is the average CC no-conflict cost per transaction

Transactions in D-DBS can oe divided not only into conflictinq and

nonconflicting (as in C-DBS) but they can be also divided into local and

nonlocal or global transactions r epending whether they execute at one

site only (local transaction) or more than one site (global transac-

tions). Thus the cost of transaction processing in the distributed DBS

(D-DBS) can be modeled as the cost of local and nonlocal (or global)

transaction executions weighted by the terms reflecting the number of

local and global transactions in the system. Then the cost CA of the

transaction processing in D-DBS is:

Cd = X*(Clsys + CIsyn) + (l-X)*(Cgsyn + Cgsys (3)

whereClsys is the average cost of local transaction execution without

considering concurrency control

clsyn is the average CC cost per local transaction, i.e., one

which needs to access data only at one site

Cgsys is the average cost of global transaction execution

without considering concurrency control

Cgsy n is the average CC cost per global transaction, i.e. one

which needs to access data at more than one site of the D-DBS

X is the ratio of the number of local transactions to all tran-

sactions

l-X is the ratio of the number of global transactions to all

transactions

Let

CgsYS = Clsys + Ceata (3a)

where

Cdata is the average (communication) cost of data transfers dur-

ing the global transaction execution

C1 syn can be further decomposed in a similar manner to Csy n in the case

of C-DBS:

C1Sy n = D11 * Clconfl + CIcc (4)

where

DI1 is the degree of interference of local transactions at each

site of the D-DBS, i.e. ratio of the number of conflicting local tran-

sactions (i.e., local transactions conflicting with local transactions

7

*--!~i

but not with global transactions) to the total number of local transac-

tions

Clcc is the average CC no-conflict cost per local transaction

Clconfl is the average CC conflict resolution cost per local

conflicting transaction.

Cgsy n can also be decomposed in similar manner as follows:

Cgsyn = gcc + 01g * Cgconf1

where

Cgcc is the average CC no-conflict cost per global transactio

"gconfl is the average CC conflict resolution cost oer q1obal

transaction

DI is ratio of the number of conflicting transactions (i.e.,

global transactions conflicting with global and local transactions) to

the number of global transactions

Substituting into (1) and (3) from (2), (3a), (4), and (5) we obtain

Cc =csys +Ccom +DI* CconfI + Ccc ( )

= C!Sys X*(DI*lconfl + Clc c ) (l-X)*(CI *Cgconf+ C+cc +

Clata) (7)

i3

In order to simplify (f) and (7) we assume that the cost of synchroniz-

ing local transactions in O-CBS is proportional to the cost of synchron-

izing the transactions in C-DBS, i.e., we assume that:

Clconfl 1 * C conf

Clc c K 1 * Ccc)

where K, # 1 if the following, applies:

a) processors in D-DBS can not support the same CC mechanism at

the same cost as C-CBS, e.g., number of I/O is different, etc.

b) D-OBS uses CC mechanism different from the one used in C-7PS

c) when both of the above apply

We can also assume that the cost of local transaction execution

without synchronization in 2-rBS is proportional to the cost of transac-

tion execution without synchronization in C-CBS, i.e., we assume:

C2sys 2 csys (?)

where '2 # I if processors in D-DBS execute local transactions (without

CC) at a different cost compared to C-CBS.

'We also assume, somewhat arbitrarily, that the legree of global and

local transaction interference in C-DBS is proportional to the leoree of

transaction interference in C-CRS as follows:

DII =(1K1"01

I.1 <3

07

where large X3 reflects either good D-DBS design anl/or applications

with strong locality. We feel that tne above assumption, whether right

or wrong, becomes irrelevant for applications featuring very low d!egrees

of transaction interference. We note here that most of present applica-

tions seem to be in that class.

Finally we assume that the cost of synchronizing global transactions in

D-OBS is proportional to the cost of synchronizing the transactions in

C-DSS, i.e., we assume that:

Cqcc = Ko *Ccc (I!)

Cgconf1 = .o*Cconf1

We will refer to a D-DBS which uses slow network (slow compared to

secondary memory channels) as a slow D-CPS for which I< >, 1. We will

refer to a D-CSS which uses fast network (comparable to seconrlary neory

channels ) as a fast (local) D-7DBS for which go l. Substitutin, (9),

(9), (10) an , (11) into (:) and (7) we aet

Cc =csys +Ccom +D*confl +Ccc (12)

Cd = 2*Ccsys + X*(<3*DI*R.**Ccon fl + l*Ccc) +

(l-X)*((l-K3)*DI*,)*Cconfl + Ko*Ccc + C,-ata) (13)

We would like to know when the cost of transaction execution is

larger in C-DeS compared to the cost of transaction execution in O-DBS.

Let

10

.... • . 1- 7 _ . i . , .. . e '

CC> CdjCA

Substituting from (12) and (13) into (14) we obtain

(I-K2)*Ccsys +Cco m + (1-K1 *K3*X)*DI*Cconfl + (1-KI*X)*Cc >

(1-X)*(2*Ko*(I-K 3)*DI*Ccon fI + Ko*Ccc + Cdata) (15)

If X.l, i.e. when almost all transaction in D-IBS are local

then (15) reduces to

(l-K2)*Ccsys + Ccom + (l-Kl*K3)*DI*Cconfl + (l-Kl)*Ccc > 0 (!q)

When almost all transactions are local (or equivalently when D-CDBS is

well designed) we introduce only negligible error by assuming that 1-3 =

1, i.e., assuming that the degree of interference in C-DBS is the same

as in C-DBS. Then (16) can be rewritten as

ccom > (K2-1)*Cc s ys + (Kl-1)*Csy n (17)

Let's assume that K1 = 1, i.e. C-OBS and D-09S execute under the same C

mechanism and the CC cost is the same in both. Tnen (17) reduces to

Ccom >/ (K-l)*Ccsys (18)

If we also assume that K2 = 1, i.e. the cost of transaction execution

without CC in O-DBS and C-DBS is the same then

11

"co >0c (19)'com

which is always true.

The above result says that in applications where (a) almost all

transactions are local, (b) the same CC mechanism is used in C-CBS and

D-DES, (c) local processors in D-DBS do not impose any processing cost

penalty compared to C-DBS processor, then the D-DBS regardless of the

speed of its network and regardless of the degree of interference will

result in lower transaction execution cost. This result which has been

derived from the analysis of our model is in a complete accord with our

intuition as it is to be expected if the model is realistic.

,'e come to the same conclusion when K2 = 1 and K1 < 1, (i.e. local

processors in D-CBS do not impose any transaction processing cost

penalty compareel to C-DES processor and D-D0S executes under lifferent

CC mechanism which has lower overhead cost).

Let's assune that V! = :-,' i.e. the local processors in C-2S

impose the same cost penalty on transactions and CC programs processing.

Then from (17) we get

Ccom > (K!-l)*(Csy + Ccsys) (2)

From (20) the only scenario on which we can make a general observa-

tion on is when '1 >> 1, i.e., when local orocessors in 7--AS i-nose

heavy processing cost penalty compared to C-CBS processor. Then from

(20) we obtain

12

Cco m >> (Csyn + Ccsys) (21)

The conclusion from (21) is that even if KI >> 1 C-OBS can still

result in lower transaction processing cost in applications where users

ship large amounts of data per transaction between remote terminals and

C-CBS processors, and terminal communication lines are slow and costly

and transactions are not computationally intensive.

we note here that our analysis could be made specific by substitut-

ing either measured or assumed values for the parameters in our formu-

las. We avoid loing so as no commercial D-DBS is operational today.

Therefore, we rather attempt to use a few reasonable assumptions so that

Ae can simplify our formulas and then make oeneral observations.

Let's assume that DI o0, i.e. very few transactions interfere

(that seems to be valid assumption for most applications). Also assume

that Xl = K2 = l,i.e. local processors in D-O8S Io not impose any cost

penalty on transactions and CC proarams processing compared to C-5BS

processor. Then from (15) we obtain

Ccom > (I-X)*(Cdata + Ccc*(O-l)) (221

For fast D-DBS Ko 1 and (22) reduces to

Ccom > (1-X)*Cdata (23)

For slow :--3S o >> 1 and (22) reduces to

Ccom > (!-X)*(Cdata + Ko*Ccc) (21)

13

The observations we offer on (23) and (?4) is that -when comparinq

transaction execution cost in C-08S and fast D-DBS then the CC mechanism

problem is not very important. However, it becomes very important when

comparing cost of transaction execution in C-DES and slow D-rBS.

4. C-CBS vs D-DBS: Performance Analysis.

The second issue we want to investiiate in this paper is the impact

of concurrency control on throuqhput of C-DRS and D-OBS based on slow

and fast networks. We are also interested in identifyinq applications

for which we can say that any D-DBS will likely outperform any C-BS and

vice versa.

The C-_BS throughput can be lerive& by considering the fact that

system transaction processing rate is 2ecreased by synchronization of

transactions. Thus we can express C-CBS throughput %c as follows:

Qc = ISC - Sccc + SCconfl)] 125)

where

SC is the basic transaction processingi rate of C-CBS which does

not have any concurrency control

Ccc is the fraction of basic transaction processing rate %

used for synchronization of transactions

Srconfl is the fraction of S. used for resolution of conflicts.

14

We model D-OSS as a set of n processors whose physical dependercy

due to an underlying network and whose logical and functional enendency

due to ilobal transactions are reflected only as a lecrease of the

throughput of every site in tD-DBS. Thus, the throughput of D-.rBS can be

,erived in terms of each node processing rate and its decrease ,ue to

the synchronization of local and global transactions as follows:

.d n*{SL-(SLcc + DIL*SLconfl) ] + (l-Y)*[CO*SL - (SOcc + DIG*Sconfl)]}

where

Y is the ratio of local transactions to all transactions

n is the number of sites or nodes in the D-t)BS

SL is the transaction processing rate of each of n nodes without

concurrency control

Skcc is the fraction of SL used for the synchronization of tran-

sactions local to one site

DIL is the -egree of local transaction interference, i.e., the

ratio of local interferring transactions to all local transactions

S -onf! is the fraction of SL used for the resolution of local

transaction conflicts

S#Cc is the fraction of SL used for the processing, of synchron-

ization messages of global transactions

3Gconfl is the fraction 3f SL used for the resolution :f 71obal

transaction conflicts

DIG is the degree of global transaction interference, i.e., the

ratio of interferring global transactions to all global transactions

I_ =

C. is the ratio of D-DBS average network Aelay to !'-DRS local

processor I/O time

We further assume that

SGcc = Col*SLcc

SGconfl = C02*SLconfl

where

C01 = C0*McC l 00 *Mcc

C0 2 C0 0 *Mconfl

where

is the ratio of average network delay to CC nessaoe set-up

time

Scc is CC no-conflict overhead, i.e., the average number of mes-

sages a iiven CC mechanism requires for synchronization of nonconflict-

inq transactions

'confl is CC conflict overhead, i.e., the averaoe number of mes-

sages a given CC mechanism requires for the resolution of transaction

conflicts. (An example of CC conflict and no-conflict overhead analysis

of several CC mechanisms can be found in (BAD ?1)).

We also assume that

SL = C1*SC

SL-C 1_ 2 cc

p1:-*S

5tconfl C2*SCconfl

DIL = C3*DI

DIG = (I-C 3 )*DI

We are interested when

Oc > (27)

Substituting into (27) from (25) and (26) and using the above

assumptions we obtain

Sc - (Scc + DI*Cconf ) > n*{Y*(C*S C - (C2*SCcc + C3*C2*DI*SCconfl)] +

(I-Y)*C0*C!*SC - (C01*C 2*SCcc + (I-C3)*Co2*C2*I*Scconfl)]} (28)

In order to simplify (28) we will assume that DI-O, C1 = C, and C

= 1. Thus we consicer applications which have very few conflictinr

transactions. We also assume that C-DBS and D-DS either use the same

CC -echanisms or if they are different then the .ecrease of local pro-

cessor througihout is the same as if they both use the same CC echah-

isms (C1 = C2 ). Finally we assume fast D-D8_S where D-LfBS local proces-

sor 1/O speed is the same as D-DES network speed (CO = 1). Sustitutina

these assumptions into (28) leads to

S(29)- *(l - n*C,) > 1 - n*r *(C01 + Y*(I - C1))3Ccc

We consider three cases 'when 1 > n*Cl, 1 = n*C1 and 1 < n*CI. When 1 >

n*CI, i.e., SC > n*SL then (29) can be rewritten is

17

SC 1 -n*C*(Y + C0 1 *(1 - Y)) (3")

SCcc 1 - n*C1

Powever, for any D-C8S

SC (31)> 1

and therefore

(1 - n*C1 *(Y + Cl*(I - Y)) (32.)

1 -n*C1

As we assumed 1 - n*C > 0 (32) reduces to

C01 . 1 (33)

.hus if SC > n*SL , then > 2, only if Cn, < 1.

'hen n*C1 = I then (29) corsiderina (31) reduces to

C'I >1 (34)

Thus if SC = n*SL then Z d > 9d only if Co1 > 1, else <

When 1 < n*C 1 , i.e., when S, < n*SL then (29) considerina (31) r-.ucesLI

to

.hus f S, < n*, en c > " Only if C1 > , se > "

From (33), (34) and (35) we can conclude that when comparing

18

J

throughput of C-DBS and 0-DBS based on fast network then the CC mechan-

ism (i.e. its CC overhead) and its efficient implementation (i.e. effi-

cient message processing) are quite important. This observation is not

entirely intuitive in the light of our assumption of very Few conflict-

ing transactions, i.e., DIDO. Of course if there are many conflictinq

transactions one would expect CC mechanism to be important for 7-DES

throughput.

An interesting observation can be made on (35). Even if D-DBS

transaction processing rate without CC mechanism is larger than transac-

tion processing rate without CC in C-D3BS, a D-DBS with CC mechanism can

perform worse than C-DRS with CC mechanism if Col < 1, i.e., if either

CC mechanism has high no-conflict overhead or it has slow CC message

processing.

Let's consider applications where YWl, i.e., there are very few

global transactions or equivalently almost all transactions are local.

In such case we can also assze that C, = 1. Substituting these assumtp-

tions into (27) we obtain

SC*(l - n*CI) > (Scc + DI*Ccconfl)*(l - n*C 2 ) (39

ie consider (36) when 1 > n*Cl, 1 = n*C1 and. 1 < n*C1

',hen 1 > n*C1 then from !39) and (31) we obtain:

C2 < Cl (37)

Thus when SC > n*SL then 2c > Td only when C2 < CI, i.e., when

19

O8S and C-OBS use different CC mechanism or they use the same one but

the D-DBS local processor throughput decrease due to synchronization of

nonconflicting transactions is smaller than the lecrease in local pro-

cessor transaction processing rate compared to C-DBS transaction oro-

cessinq rate.

'.Then 1 = n*C i , i.e., when SC = n*SL then (36) always holds and thus

> -

" hen 1 < n*C 1 then from (36) and (31) we get

C2 > C1 (30)

Thus when SC < n*SL then q. > Q. only if C2 > C1 . If C2 < C, then

> c. The implication here is that d >2. if either D-DBS uses more

efficient CC mechanism than C-CBS or D-BS uses the same CC mechanism

but its implementation is more efficient compared to C-OBS transaction

processing, i.e., if

SLcc SL

-CC(

As can be seen from (36), (37) and (38) CC nechanism is a signiFi-

cant issue when comraring performance of C-DBS and D-CBS systems. More-

over it is important even for the performance of D-DRCS based on Last

network and for applications where either there are few inter~errin'

transactions or where there are few ilobal transactions.

5. Conclusions.

20

In this paper we have investigated transaction execution cost and

system throughput in C-DBS compared to D-BS systems based on slow and

fast networks. The conclusions reached in this paper indicate that the

efficiency of CC mechanism is of great importance when comparing C-OS

and fast or slow D-CBS throughput. The same observation acplies when

comparing transaction execution cost in C-CBS and slow 0-DS. It seems

that CC mechanism is not important when comparing the cost of transac-

tion execution in C-DBS and fast D-DBS.

In this paper we have also indicated for which applications any D-

DBS is likely to be a better solution than any C-DBS and vice versa.

5. Acknowledgement.

The author would like to acknowledge support of the VPF Foundation

Research Programs for this work.

References

rBAD8O1

Badal, D. Z., "The Analysis of the !ffects if (.oncurrenc- Control

on Distributed 1atabse System Performance", Proceedings of the '-th

International Conference on Very Large 71ata lases, Montreal, October

1920, 37 -3- 2.

(BADK]

Badal, D. Z., "Concurrency Control Overhead or Closer Look at

21

Blocking vs. Yonblocking Concurrency Control Mechanisms", Proceedings of

the 5th Berkeley Conference on Distributed Data vanaoement and Com-uter

'etworks, San Francisco, February 19R1.

rBUC79]

Bucci , G. and Streeter, D. N., ' Methodoloqy for the 'esiqn of

jDistributed information Systems", CACm 22, 4 (April 19 9), 233-?5.

[MOL791Garcia-4olina, H., "Performance of U~ate Alcorithms for Repi-

cated rata in a Distributed Database," Ph.D. dissertation, Devnartnent of

Computer Science, Stanford University, June 1979.

rRIE79JJRies, D. R., 'The Effects of Concurrency Control on Tatahase

manaaement System Performance," Ph.D. dissertation, Computer Science

! epart. ent, University of California, :-erkeley, April l1'9.ESTE79 ]

Stewart, H. M. "Performance analysis of Complex Communications

Systems", IBM System Journal 12, 3 (1970), 35<-73.

2L

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Office of Research AdministrationCode 012ANaval Postgraduate SchoolMonterey, CA 93940

Chairman, Code 52Hq 2Department of Computer ScienceNaval Postgraduate SchoolMonterey, CA 93940

Dushan Badal, Code 52Zd 5Department of Computer ScienceNaval Postgraduate SchoolMonterey, CA 93940

Robert B. GraftonOffice of Naval ResearchCode 437800 N. Quincy StreetArlington, VA 22217

David W. MizellOffice of Naval Research1030 East Green StreetPasadena, CA 91106

Vinton G. CerfDARPA/IPTO1400 Wilson BlvdArlington, VA 22209

CDR R. OhlanderDARPA1400 Wilson BlvdArlington, VA 22209

ral. D. AdamsDARPA1400 Wilson BlvdArlington, VA 22209

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______

CAPT. W. PriceAFOSR/ NMBoiling AFB., D.C. 20332

Col. R. SchellNational Security AgencyC1Fort George Meade, MO 20755

Raymond A. LiuzziGriffiss AFB., N.Y. 13441

Thomas LawrenceRADC/ COTDGriffiss AFB., N.Y. 13441

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