Fuzzy Middleware for Load Balancing in Grid Computing
Prakrati Agarwal
Dr. Sunita Yadav
Department of Computer Science & Engineering
Ajay Kumar Garg Engineering College
Ghaziabad, UP, India
Department of Computer Science & Engineering
Ajay Kumar Garg Engineering College
Ghaziabad, UP, India
finenature825@gmail.com
yadav.sunita104@gmail.com
Abstract: Grid Computing is kind of high performance computing which aims to complete any complex,
scientific task which is distributed over a network. Grid computing is aggregation of high computational
power and resources which spread on different location. Goal of grid computing is to use them and
produce good result. In grid load balancing problem occur. Some node become lightly loaded, some may
become heavily loaded. To get high throughput and efficiency we require some load balancing strategies.
This strategy based on various factors like network bandwidth, available bandwidth, preemptive or non
preemptive job, number of resource available, Cpu speed, response time and waiting time of job etc. To
select a better node to transfer application from heavily loaded node various approaches are there. This is
a good decision making type problem. This paper proposes Fuzzy analytical hierarchy process to be use
for decision making. Using Fuzzy AHP we are designing middleware. This model concern parameters
and decide on which node load should be transfer. This model emphasize on overall performance of grid.
Key words: Grid computing, load balancing, decision making, Fuzzy-AHP
I. INTRODUCTION
Grid computing is a type of distributed computing in which resource are scattered and motive is to work
with these resources to achieve a goal. Grid computing provides high performance computing like cluster
computing. Grid is different form cluster computing in which all nodes have same computational power,
memory and operating system but in grid computing environment is heterogeneous. Nodes which
communicate with each other may have different power, resource, OS etc. Grid computing is easily
parallelizable [1].
Grid computing can provide virtualization. Grid enables the Virtual Organizations (VO) to access the set
of services and resources provided by another organization or departments of the same organization. In
virtualization user think all the processing is happening on its work station [2]. User only have to submit
their application and no need to know about how processing is going on different location. Through
virtual organizing concept job can be execute by taking advantage of other organization resources.
In this paper, we are taking enterprise grid computing environment. Enterprise grid is managed by
individual or group. This is special kind of grid in which we achieve specific goal by control and
management.
Big issue in grid environment is of load balancing. Load balancing are of two types. One is static and
second is dynamic. Grid is dynamic in nature so it emphasize on dynamic load balancing strategy.
Through dynamic load balancing we are able to take decision at run time [3][4].
Decision making is important task in grid computing. Load balancers have to take decisions on which
node application should be transfer when lots of alternatives are there. While working with grid
computing decision making face lot of confusion, ambiguity, uncertainty etc. To deal with such kind of
problem researchers are focusing on “decision making in fuzzy environment”.
Many researchers are working on F-AHP technique for decision making process like risk assessment,
college teacher performance evaluation. F-AHP can be integrated with middleware of grid computing.
This middleware can be work with uncertain or vague information of load in proper manner [11][12][13].
The paper is organized as follows: Sec. I present an overview of the paper. Sec. II presents all the
terminologies and related work. Sec. III presents the proposed model and its working. Finally we
conclude in Sec. IV.
II. Related Work
In this section, we describe the related work and terminologies used in the proposed work.
A. Grid environment
To understand grid computing we can refer figure 1. Application from user workstation goes to grid
middleware. Middleware is backbone of grid computing. It provides core services like job submission. It
hides from work station how application is solving in distributed environment. Middleware attach with
Grid Information service which provide updated and dynamic knowledge of grid [5].
Figure 1: Grid Computing Environment
B. Dynamic load balancing
Load balancing is essential in grid computing. Load is basically number of job available in ready queue of
computing nodes. When load crosses particular threshold of queue then we require load balancing [6].
Load balancing can be done by two types. First is static load balancing and second is dynamic load
balancing. In static load balancing all jobs have predefine computing nodes. At run time jobs can’t change
their ready queue if performance goes down but in dynamic load balancing middleware can preempt the
job send assign them to suitable nodes.
Figure 2: Dynamic load balancing
Figure-2 is depicting dynamic load balancing. Scheduler schedule applications based on pre knowledge
base and dynamic grid information to available resource.
C. Parameter of load balancing
Performance of grid computing affected by various parameters. If middleware take care of parameters for
load balancing we can get higher performance. Different parameters of load balancing are given below
i. Network Parameters
Load balancing can be based on network parameter like communication link capacity, available
bandwidth of link, network latency [7].
ii. Computational node characteristics
Load balancing can be based on computational node characteristics. First is number of available resource
like I/O, memory, number of cpu etc. Waiting time, response time and turnaround time are parameter
based on node characteristics. Example let job is I/O oriented. If it is properly schedule on node which
have required I/O then response time will be low and performance will improve. Every computing node
have ready queue which have particular threshold. If load crosses this then we need to balance it so this is
also a characteristic of computational node [8].
iii. Application Characteristics
There are parameters related to application which affect load balancing. These are preemptive or not
preemptive, execution time of application, is application dependent on other application.
All the parameters have some limitations. When huge applications are in grid then frequently task
migration occur. At this time network bandwidth, CPU or resources become less available. This also
affect waiting time, response time and performance.
D. Fuzzy analytical hierarchy process
Before understanding F-AHP we will go through the fuzzy and ahp separately.
i. Fuzzy theory
Fuzzy logic was introduced in 1965 by Professor L. Zadeh at university of California, berkely. Fuzzy set
talk about degree of membership. According to that item may partially belong to some set or not. It means
item may or may not lie completely in one set or not. Fuzzy logic talks about partial truth. Fuzzy logic is
multivalued logic that allows defining middle values between conventional evaluations like yes/no,
true/false. In some domain we have to deal with imprecise or vague information. Fuzzy set theory like a
human reasoning because it can work with approximate information. Researches find that it is better to
give interval judgment rather than fixed value judgment. A decision processes in which rules and
constraints are define but not necessarily under control fuzzy in nature [9].
In grid computing information is dynamic. We have to deal with uncertainty. Information may become
outdated while transferring. In this kind of system fuzzy approaches are best to use.
ii. Analytical hierarchy process
AHP was developed by Thomas Satty. This is approach of decision making. It uses to determine rank of
alternatives present in the system. It gives hierarchical structure to the problem. But it does not deal with
dynamic information present in the system.
Fuzzy Analytical Hierarchy Process is one step above then ahp because it can deal with uncertainty [10].
It is combination of AHP and fuzzy approch.
III. Proposed model
Our proposed model is named as fuzzy model for load balancing in grid (FMLBG). FMLBG is designed
for grid computing model and decision is made by considering four parameters simultaneously with the
help of fuzzy AHP. FMLBG works in three steps.
1. Grid generation
2. Parameter values
3. Decision making (Fuzzy AHP)
Steps of proposed model:
1. Grid Generation:
GridSim Toolkit 5.0 is a simulation toolkit, with the help of which we have generated grid
environment having four computing nodes, named as N1, N2, N3, N4. GridSim interface is
shown in figure 3.
Figure 3: GridSim Interface
2. Parameters Values:
There are three main parameters which can affect the overall performance of grid. These three
parameters are network characteristics [14], computing node capacity [15] and application
characteristics [16]. We have selected two parameters namely network parameter and computing
node capacity. Sub-parameter of network parameters is Available bandwidth and Network
Latency. Processing speed and Number of available resources are the sub-parameter of
computing node capacity as shown in figure4.
Figure 4: parameter values by GridSim
3. Node Selection (Fuzzy AHP working):
Fuzzy AHP is used to make a decision or to select a best node for load transfer. Fuzzy AHP is
different with traditional AHP in working. Fuzzy AHP works in four steps.
 Enter the preference for criteria on the basis of which we have to make a decision. For
this a saaty’s scale between 1 to 9 is followed.
 Priority vector (P. V.) is calculated for each criterion.
P. V.= 3rd root of product of pairwise comparison/ sum of 3rd root of product.
 Comparison matrix and P.V. for each sub criterion.
 Final score is calculated. Best node will be the node that has highest score.
Final score = Ʃ (Criteria weight X Option Weight)
Figue 5: Working of Fuzzy AHP
All the steps of fuzzy AHP are shown in Figure 5. As Fuzzy AHP selects best node for load transfer, it
will provide the better performance then use a single parametric model. Single parametric model gives
less performance because other parameters may restrict the overall performance. As we have considered
four parameters in this work, the decision is made according to each parameter so any parameter restricts
the overall performance.
IV. Conclusion
In this paper, we have combined four parameters: Available bandwidth network latency, processing speed
and number of available resources. On the basis of these parameters, overall performance of grid
increases at a higher limit.
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