Job scheduling is the key feature of any computing environment and the efficiency of computing depends largely on the scheduling technique used. Intelligence 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 resource 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 employs 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 learn from previous experiences and an effective job scheduling is achieved as time progresses. Predictive Job Scheduling in a Connection Limited System using Parallel Genetic Algorithm
EXISTING SYSTEM:
The Data mining Algorithms can be categorized into the following :
▪Association Algorithm
▪Classification
▪Clustering
Classification:The process of dividing a datasets 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 specific 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.”
Clustering:The process of dividing a dateset 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.Given databases of sufficient size and quality, data mining technology can generate new business opportunities by providing these capabilities:
•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 from 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 include 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 previously 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 detecting fraudulent credit card transactions 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. Popular 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 scheduling 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 algorithm is shown in this proposed system