Predictive Job Scheduling in a Connection Limited System using Parallel

bigskymanAI and Robotics

Oct 24, 2013 (3 years and 1 month ago)

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Predictive Job Scheduling in a Connection
Limited System using Parallel


ABSTRACT


Job scheduling is the key feature of any computing environment and the
efficiency of computing depends largely on the scheduling technique used. Inte
lligence is
the key factor which is lacking in the job scheduling techniques of today.
Genetic
algorithms are powerful search techniques based on the mechanisms of natural
selection and natural genetics.
Multiple jobs are handled by the scheduler and the
r
esource the job needs are in remote locations. Here we assume that the resource a job
needs are in a location and not split over nodes and each node that has a resource runs a
fixed number of jobs.


The existing algorithms used are non predictive and emplo
ys greedy based
algorithms or a variant of it. The efficiency of the job scheduling process would increase
if previous experience and the genetic algorithms are used.

In this paper, we propose a
model of the scheduling algorithm where the scheduler can le
arn from previous
experiences and an effective job scheduling is achieved as time progresses.







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EXISTING SYSTEM


The Data mining Algorithms can be categorized into the following :



Association Algorithm



Classification



Clustering Algorithm

Classification
:

The process of dividing a dataset into mutually exclusive groups
such that the members of each group are as "close" as possible to one another, and
different groups are as "far" as possible from one another, where distance is measured
with respect to spec
ific variable(s) you are trying to predict. For example, a typical
classification problem is to divide a database of companies into groups that are as
homogeneous as possible with respect to a creditworthiness variable with values "Good"
and "Bad."

Cluster
ing
:

The process of dividing a dataset into mutually exclusive groups such
that the members of each group are as "close" as possible to one another, and different
groups are as "far" as possible from one another, where distance is measured with respect
to
all available variables.

Automated prediction of trends and behaviors
. Data mining automates the
process of finding predictive information in large databases. Questions that traditionally
required extensive hands
-
on analysis can now be answered directly fr
om the data


quickly. A typical example of a predictive problem is targeted marketing. Data mining
uses data on past promotional mailings to identify the targets most likely to maximize
return on investment in future mailings. Other predictive problems in
clude forecasting
bankruptcy and other forms of default, and identifying segments of a population likely to
respond similarly to given events.

Automated discovery of previously unknown patterns
. Data mining tools
sweep through databases and identify previ
ously hidden patterns in one step. An example
of pattern discovery is the analysis of retail sales data to identify seemingly unrelated
products that are often purchased together. Other pattern discovery problems include



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detecting fraudulent credit card tr
ansactions and identifying anomalous data that could
represent data entry keying errors.

PROPOSED SYSTEM

Job scheduling is the key feature of any computing environment and the
efficiency of computing depends largely on the scheduling technique used. Popul
ar
algorithm called genetic concept is used in the systems across the network and scheduling
the job according to predicting the load.

Here the system will take care of the scheduling of data packets between the source and
destination

computers.



Job sched
uling to route the packets at all the ports in the router



Maintaining queue of data packets and scheduling algorithm is implemented



First Come First Serve scheduling and Genetic algorithm scheduling is called for
source and destination



Comparison of two al
gorithm is shown in this proposed system



SOFTWARE AND HARDWARE REQUIREMENT

Hardware requirements :



Processor





: Atleast P3 (1 GHZ clock)



RAM






: 128 MB



Hard size capacity




: Atleast 4 GB



Free Space in Hard disk



: Minimum 1MB


Software requirem
ents:



Programming language (Compiler )

:
JAVA (JDK1.6

)



Operating system




: Windows 98 or higher version