This document discusses using genetic algorithms for job scheduling in cloud computing environments. It begins with an introduction to cloud computing and genetic algorithms. It then discusses the challenges of genetic scheduling, including reducing makespan time, uniform load balancing, and minimizing user cost. It reviews various genetic algorithm approaches that have been proposed to address these challenges, such as approaches aimed at reducing makespan time alone, reducing cost alone, or reducing both cost and makespan time simultaneously. The document concludes that no single algorithm solves all problems, and that combining algorithms can better satisfy complex constraints in job scheduling.

1. Planning of Jobs
Scheduling in Clouds
by using Genetic
Algorithm
Amarjit Singh Dhillon,
Ranjit Singh Saini,
Yu Ni,
Karan Seth
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2. Overview
1. Introduction to Clouds
2. Basics of Genetic Algorithm
3. Need to implement Genetic Scheduling
4. Challenges / Solutions
5. Scrutinize approaches/ forte-foibles
6. Conclusion
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3. Part-I Introduction to Cloud computing
o Proposed and initiated by Google CEO Eric Schmidt in 2006
o Pay-as-you-go model
o On-demand procurement / AR
o Automatic Resource provisioning
o Hardware Virtualization
o Scalability– horizontal/ vertical
o No upgradation required - cost cutting/ faster Implementation
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4. Cloud Services
4
o Gamut of services
o Yahoo / Large Hadron collider
o How to analyze data ?
1. Mapreduce
2. Hadoop
o How to schedule ?
1. Auto scaling
2. Ant colony/ Particle Swarm/Genetic

5. Services and Service Providers
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Categories of services
• IaaS
• PaaS
• SaaS

6. Part -II Basics of Genetic Algorithm
o Search based Optimization Method
o Heuristics/Meta-heuristic approach.
o Theory of Natural Selection
o Sub – optimal solution
o David Goldberg- perfect human being ?
o Find better solution
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7. Part-III Why GA …..
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Meeting Constraints
Auto Scaling in Scheduling
1. Static
2. Dynamic

8. Part-III Why GA ?
• Dynamic auto scaling
a) Reactive – Gos such as B/ CPU Utilization
b) Proactive
• M/L Algorithms like Control Theory,
Reinforcement Theory or Queuing Model.
• NP- Hard problem
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9. Part-IV Challenges in Genetic Scheduling
1. Reducing makespan time
2. Uniform load balancing of user jobs on resources
3. Minimizing user cost
4. Attaining Diversity in Population Space
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10. 1. Reducing Makespan Time
Cause
o Initial Population Selection
Solutions
o Roulette Wheel Selection –High fitness
o Rank Selection – fair selection/ slow convergence
o Elitism – keep best strategy / used in JLGA [2]
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11. 2. Uniform Load Balancing
o Uneven load among nodes
o User can renounce / time factor
Examples : -
Google - Map-reduce
Facebook -Fair-share
 Variance of jobs is less in JLGA [2]
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12. 3. Minimizing User cost
Non-Genetic Algorithm
o By performing Auto scaling
a. Static – Instances will remain same
b. Dynamic
1. Reactive – GoS like Blocking ratio, CPU
utilization
2. Proactive- uses M/L like Queuing theory,
Reinforcement model
Using Genetic Algorithm
1. Fast convergence rate
2. Maximizing diversity
3. Better Selection
4. Load balancing
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13. 4. Attaining Diversity in population
o Crossover – multipoint
o Selection - Elicit method
 Best solutions quarantined
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14. Part-V Related work
Constrict a performance metric to
1. Subside makespan time.
2. Abate user cost.
3. Dwindle both cost and makespan time.
4. Multi-faceted approach : Accelerate convergence, balance load, augment/
preserve diversity
• Adept use of Genetic operators - hinged upon problem domain
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15. 1. Subside makespan time.
 In Literature [4] Keep-the-best-strategy is
employed.
 In 2012, P. Kumar and A. Verma proposed
Improved GA [1]
o Max-Min or Min-Min yardstick
o Alike results unveiled, when Cloudlets
incremented while keeping VM # fixed.
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16. 1. Subside makespan time....
o In 2014 , Job Spanning tree and Load
Balancing Genetic Algorithm (JLGA) [2]
• selection based on λ1, λ2
• λ1 -total time, λ2 - inter-nodal balance
• λ1 > λ2 , λ1+ λ2 =1
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17. 1. Subside makespan time.......
Using load balance to Minimize cost
o 4 Jobs to 4 Nodes
o Jobs are split into 2, 3, 4, 5 tasks
o Load Balancing
o {1, 3, 2, 4, 3, 1, 4, 2, 3, 1, 3, 2, 4, 1}
o Node1 {1,6,10,14} Node2 {3,8,12}
o Node3 {2, 5, 9, 11} Node4 {4, 7, 13}
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18. 2. Abate user cost ……
o In 2011, M. Humphrey & M. Mao proposed a Dynamic approach [12]
• cost-efficient mechanism by grouping various tasks into one.
• cheap but not sub-optimal.
o W.M. Zou and J. Y proposed a Consumer satisfaction based Genetic Algorithm CFGA [13] which
goals at dwindling costs due incurred by data transmissions.
o In 2012, A. Verma at el [3] exhibited low Cost as compared to Standard Genetic Algorithm (SGA)
when substantial load was applied.
• Elect Initial Population based upon fusion of Shortest Cloudlet to Fastest Processor (SCFP) and Longest
Cloudlet to fastest Processor (LCFP) Algorithms in conjunction to controlling stochastic operators of GA.
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19. 2. Abate user cost ……….
o In 2016, Jian-Wen Li and Chi-Wen Qu [11]
proposed Cultural Genetic Algorithm (CGA)
o Belief Space
o Population Space
o Knowledge domain
o Influence()
o Fitness(i) = ω1* FinishTime (i) + ω2 *Finishcost (i)
o ω1 and ω2 are weights such that ω1+ ω2 = 1.
o Tradeoff between time and cost are made in this
method
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20. 3. Dwindle cost and makespan time.
o Zong-Gan Chen and Zhi-Hui Zhan proposed deadline
constrained and cost optimization based dynamic
scheduling algorithm which is Dynamic Objective
Strategy based GA (DOGA) [4].
• Minimize TEC
• TET < deadline
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21. 4. Accelerate convergence, balance load, augment diversity
o Job spanning time and Load Variance Genetic Algorithm (JLGA) [2] that was focused to
improve various metrics.
o Diversity - Greedy Algorithm
o Convergence - SLA
o Average Spanning Time (AST)
o Total Job span Time (TJT).
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22. 4. Accelerate convergence, balance load, augment diversity…..
Fitness1 = D1 / C1*TotalTime(i) + C2 *AvgTime(i)
Fitness2 = D2 / a * Total Time
o Function Fitness1 reducing of Make-span
o Fitness2 is used for Load Balancing.

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23. 4. Accelerate convergence, balance load, augment diversity………
o In 2016, Jian-Wen Li and Chi-Wen Qu tackled
the problem of diversity in GA by Cultural
Genetic Algorithm [11].
1. Main Population Space
2. Belief Space
o 5% replacement.
o Belief space updates and Influences().
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24. Part-VI CONCLUSION
o No single algorithm was found that eliminate all the problems.
o Combination of algorithms can be used to attain complex constraints.
 Following algorithms work best in satisfying simple constraint
1. Makespan Time : DOGA perform fairly well in order to reducing.
2. User cost : CGA method subsided to greater extent.
3. Makespan time, Load balancing and Diversity: JLGA found effective in handling multiple
constraints such as reducing.
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25. References page 1
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International Conference on Advances in Computing, Communications and Informatics - ICACCI '12 (2012): pp. 137-142.
2. Wang, Tingting et al. "Load Balancing Task Scheduling Based On Genetic Algorithm In Cloud Computing". 2014 IEEE 12th International Conference on
Dependable, Autonomic and Secure Computing (2014): n. pp. 146-152.
3. Kaur, Shaminder, and Amandeep Verma. "An Efficient Approach To Genetic Algorithm For Task Scheduling In Cloud Computing Environment".
International Journal of Information Technology and Computer Science 4.10 (2012): pp. 74-79.
4. Chen, Zong-Gan et al. "Deadline Constrained Cloud Computing Resources Scheduling for Cost Optimization Based On Dynamic Objective Genetic
Algorithm". 2015 IEEE Congress on Evolutionary Computation (CEC) (2015): pp. 708-714.
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Communication and Conservation of Energy (ICGCE) (2013): pp. 883-888.
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26. References page 2
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2005. n. pp. 540-549
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11. Li, Jian-Wen, and Chi-Wen Qu. "Cloud Computing Task Scheduling Based On Cultural Genetic Algorithm". MATEC Web of Conferences 40 (2016): 09008
12. Mao, Ming, and Marty Humphrey. "Auto-Scaling To Minimize Cost And Meet Application Deadlines In Cloud Workflows". Proceedings of 2011
International Conference for High Performance Computing, Networking, Storage and Analysis on - SC '11 (2011): n. pag. Web. 7 Feb. 2016.
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27. References page 3
13. W.-m. Zou and J. Y, “Consumer satisfaction genetic algorithm in cloud computing,” Application Research of Computers, vol. 31, no. 1, pp. 85– 88, 2014.
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Computer Applications, vol. 31, no. 1, pp. 184–186.
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Computer Applications, vol. 31, no. 1, pp. 184–186.
15. S. Chaisiri, B. Lee, D. Niyato, “Optimization of Resource Provisioning Cost in Cloud Computing”, IEEE Trans. Services Computing, vol. 5, no. 2, April-June,
2012, pp. 164-177.
16. R. Buyya, S. K. Garg, R. N. Calheiros, “SLA-Oriented Resource Provisioning for Cloud Computing: Challenges, Architecture, and Solutions”, in Proc. Int.
Conf. Cloud and Service Computing, DC, 2011, pp. 1-10.
17. M. A. Rodriguez and R. Buyya, “Deadline based resource provisioning and scheduling algorithm for scientific workflows on clouds,” IEEE Transactions on
Cloud Computing, vol. 2, no. 2, pp. 222–235.
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