Authors

Citation

Gr. Dattatreya et R. Venkatesh, STATIC AND DECENTRALIZED-ADAPTIVE LOAD BALANCING IN A STAR CONFIGUREDDISTRIBUTED COMPUTING SYSTEM, IEEE transactions on systems, man and cybernetics. Part A. Systems and humans, 26(1), 1996, pp. 91-104

Citations number

14

Language

INGLESE

art.tipo

Article

Categorie Soggetti

System Science",Ergonomics,"Computer Science Cybernetics

ISSN journal

1083-4427

Volume

26

Issue

1

Year of publication

1996

Pages

91 - 104

Database

ISI

SICI code

1083-4427(1996)26:1<91:SADLBI>2.0.ZU;2-W

Abstract

The problem of minimizing the average job response time (sojourn time)
in a star configured Distributed Computing System (DCS) with N periph
eral processors and one central processor is studied. Each peripheral
processor (satellite) receives a stream of Poisson arrivals, A job arr
iving at a satellite S-i will be processed with a probability (1 - P-i
) by itself and routed to the central site for remote processing with
probability P-i. The service time distribution at each processor is ar
bitrary with means 1/mu(i) and second moments E[Y-i(2)], i = 0,..., N.
We first develop an algorithm to compute the optimal routing probabil
ities when the arrival and the service parameters are known a priori.
For a special case, when all the professors have exponential service t
ime distributions, we obtain explicit closed form expressions for the
static optimal routing probabilities under the assumption that the der
ivative of the objective function vanishes inside or on the N-cube of
routing probabilities. If this assumption is violated, at least one of
the computed probability falls outside [0, 1]. We develop an O(N log
N) algorithm to compute the optimal probabilities within the valid ran
ge. The second and a major contribution of the paper is the developmen
t of an adaptive estimator to be used by each satellite to compute its
optimal routing probability, when the parameters (everywhere) are unk
nown a priori. A copy of the algorithm runs on each peripheral process
or and uses the sojourn time measurements that the peripheral processo
r can gather on its own from those jobs that are sent to the central p
rocessor and returned to the originating site after service. It is ass
umed that the central processor gives the processing time (of a remote
job) to a peripheral processor when returning the remote job submitte
d by that PP. The algorithm does not use any other message transmissio
n between processors. This makes our algorithm thoroughly decentralize
d. We prove the convergence, with probability 1 and in mean square, of
the proposed adaptive estimator to the optimum routing probabilities,
under the assumption that the arrival and service parameters are cons
tants. We present simulation results to demonstrate the performance of
the algorithm under several sets of conditions, including one in whic
h the parameters change with sudden jumps.