Load Balancing in Distributed
Computing Systems Using
Fuzzy Expert Systems
Author Dept. Comput. Eng., Alexandria Inst. of Technol.
Content Type Conferences
On Page 141 - 144
Appears Modern Problems of Radio Engineering,
Telecommunications and Computer Science, 2002. Proceedings of
the International Conference
Date 07 八月 2002
Speaker Jyue-Li Lu
Abstract

In this paper we present fuzzy functions to model
load balancing in distributed computing systems.
Due to lack of communication delays in message
passing, a complete and consistent view of the
entire system may never be available to a node of
the system.

Most of the previous work in load balancing and
distributed decision making in general, do not
effectively take into account the uncertainty and
inconsistency in state information.
Abstract

Also both the load patterns and task lengths
descriptions are based on crisp values which may be
inconvenient.

Most of the research that used fuzzy descriptions
considered fixed fuzzy function parameter values
that only can match in fixed load patterns.
Abstract

The aim of this paper is to solve the uncertainty
problem in state information and task selection for
migration using proposed fuzzy functions of variable
membership values, and to propose an expert
system that uses these fuzzy functions in decision
making.
INTRODUCTION

Distributed computing systems have become a
natural setting in many environments for business
and academia.

In a typical distributed system setting, tasks arrive at
the different nodes in a random fashion.

This causes a situation of non-uniform loading
across the system nodes to occur.
INTRODUCTION

Loading imbalance is observed by the existence of
nodes that are highly loaded while others are lightly
loaded or even idle.

Dynamic load balancing involves the reallocation of
tasks to processors after their initial assignments.

This is done by migrating tasks from the overloaded
nodes to other lightly loaded nodes to improve the
overall system performance.

Such an assignment problem has been shown to be
NP-complete.
System Model

Host Model


、 A communication costs table
Task Model



A load Table
Tasks are assumed to be independent.
The proposed approach requires the existence of a compile and
run-time libraries support for the estimation of execution times,
remaining times, penalties, communication costs, and the
unfinished work.
Assumptions

light load, moderate load or heavy load.
Load Description
Load Description

Previous work in dynamic load balancing using fuzzy
expert systems used constant fuzzy set parameters.

This might be inefficient because any load, that is
described to be high with a membership value of 1, may
not guaranteed to be the highest load over the network
all the time.

So the parameter describes the highest load must be
adaptive according to the load patterns over the network.
Load Description

figure 1, the region from a0 to p is divided into equal six
regions. Each region with length (p - a0)/6.
TASKL ENGTHD ESCRIPTION

The task length can be expressed as a member of one
of five fuzzy sets, very long length tasks set, long length
tasks set, intermediate length tasks set, short length
tasks set and very short length tasks set.
WORKING
MEMORY
INPUT
DECISIONS
INFERENCE
ENGINE
KNOWLEDGE
BASE
KNOWLEDGBEA S

Rule[l] IF the node is lightly loaded THEN the node is a receiver.

Rule[2] IF the node is moderately loaded AND the number of
heavily loaded nodes is nearly equal to the number of lightly
loaded nodes THEN the node is neutral.

Rule[3] IF the node is moderately loaded AND the number of
heavily loaded nodes is more than the number of lightly loaded
nodes THEN the node is receiver.

Rule[4] IF the node is moderately loaded AND the number of
heavily loaded nodes is less than the number of lightly loaded
nodes THEN the node is sender.
KNOWLEDGBEA S

Rule[5] IF the node is sender THEN select a receiver as a
migration partner.

Rule[6] IF the node fails to find a migration partner THEN the
node is neutral.

Rule[7] IF the node is a sender THEN select a suitable task to
transfer.

Rule[8] IF the node fails to select a suitable task to transfer
THEN select another migration partner.
CONCLUSION

The proposed approach has been studied by means of
simulation.

Performance measure in terms of the mean response
time is improved in all cases with different percentages.

The application of the proposed approach on hard real
time systems, using neural networks will be considered
in future work.
End