The document presents a project on task scheduling in cloud datacenters using a genetic algorithm, highlighting the importance of scheduling and the drawbacks of existing algorithms like the round robin approach. It proposes a new system based on genetic algorithms, which utilize natural selection principles for improved scheduling efficiency and reduced energy consumption. The overview includes the mechanics of genetic algorithms and their application in optimizing task scheduling.

1. 1
Task Scheduling in Cloud
Datacenter using genetic algorithm
Presented by: Project Guide:
Swathi R(1cg11is092) Mrs.Thara Dk
Manasa V(1cg11is044) Asst. Professor
Rekha M(1cg11is072) CIT, Gubbi
Yashoda DN(1cg11is102)

2. Overview
 Introduction
 What is important in a Scheduling Algorithm?
 Need of scheduling
 Existing scheduling Algorithms
 Drawbacks of existing Algorithm
 Proposed system
 Genetic algorithm (GA)
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3. 3
Introduction

4. What is Important in a Scheduling
Algorithm?
4

5. Need of Scheduling in cloud
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6. Existing system
 Round robin algorithm
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7. Round Robin Scheduling
 Circular queue to store job.
 Task is based on slice of time.
 Context switch.
 Resources are utilized in balanced order.
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8. Drawbacks of Existing Algorithm
 Largest job takes enough time for completion.
 The power consumption will be high as many
nodes will be kept turned-on for a long time.
 There is an additional load on the scheduler to
decide the size of quantum.
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9. Proposed System
• Genetic Algorithm.
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10. Genetic algorithm (GA)
 Search algorithms based on the mechanics of
natural selection and natural genetics
 Based on the “survival of the fittest” concept
(Darwinian theory)
 Simulate the process of natural evolution
 Central theme of research on genetic algorithm
is “Robustness”
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11. Contd ..
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o Initial population
o Fitness function
o Selection
o Cross over
o Mutation

12. Data flow in genetic algorithm
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TASK ARRIVAL
NO YES
INITIAL POPULATION
SELECTION
CROSSOVER
MUTATION
VALUE >
FITNESS
STOP THE
PROCESS

13. Architecture
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14. Implementation
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Thank you. . . .
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