For Project Enquiry +91 9962 067 067

EXISTING SYSTEM

PROPOSED SYSTEM

EXISTING SYSTEM:-

The existing works fail to either meet the user’s Quality of Service (QoS) requirements or to incorporate some basic principles of Cloud computing such as the elasticity and heterogeneity of the computing resources.

Clusters and Grids is their focus on meeting application deadlines or minimizing the makespan of the workflow while ignoring the cost of the utilized infrastructure. Clouds are obliged to consider the pay-per-use model of the infrastructure in order to avoid prohibitive and unnecessary costs.

PROPOSED SYSTEM:-

The proposed approach incorporates basic IaaS Cloud principles such as a pay-as-you-go model, heterogeneity, elasticity, and dynamicity of the resources.

In every case in which IC-PCP fails to meet the application’s deadline, our approach succeeds. Furthermore, our heuristic is as successful in meeting deadlines as SCS, which is a dynamic algorithm. Also, in the best scenarios, when our heuristic, SCS and IC-PCP meet the deadlines, we are able to produce schedules with lower execution costs.

EXISTING ALGORITHM:-

.Genetic Algorithm(GA)

PROPOSED TECHNIQUE:-

Meta-heuristic optimization technique, Particle Swarm Optimization (PSO).

ALGORITHM DEFINITION:-

It is also meta-heuristic based scheduling technique such as GRASP. It allows a high quality solution to be derived from a large search space in polynomial time by applying the principles of evolution.

TECHNIQUE DEFINITION:-

There are two key steps when modeling a PSO problem. The first one is defining how the problem will be encoded, Defining how the solution will be represented. The second one is defining how the “goodness” of a particle will be measured, that is, defining the fitness function.

DRAWBACKS:-

Problem in different execution plan

Execution time is increased.

ADVANTAGES:-

It provides security and performance.

Efficient execution time.