Evaluating the Application Scheduling Algorithms in CloudSim

1. Application Scheduling in CloudSim
Presented by: Pradeeban Kathiravelu
Supervised by: Prof. Luís Veiga
Implementation of Distributed Systems
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2. Application Scheduling
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Scheduling an application
– to be executed
– using a resource
– in a cloud environment

3. Aim
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Evaluating the Scheduling algorithms
– Strict matchmaking-based
– Utility-driven

4. Aim
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Evaluating the Scheduling algorithms
– Strict matchmaking-based
– Utility-driven
Criteria
– Mean execution time
– Mean user submission time
– Average resource utilization
– Job Scheduling Success Ratio

5. Objective Function → Algorithm
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Strict matchmaking-based
– Minimum Execution Time (MET)
– Minimum Completion Time (MCT)
– Maximum Resource Utilization
– Matchmaking
– First-come first-served (FCFS)
– Round Robin (RR)
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6. Utility → Algorithm
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User Satisfaction
Partial Requirement Satisfaction.
– Number of metrics
– Are they equally important?

7. Evaluation
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CloudSim
– Simulation tool for cloud
computing
Representing by objects.

8. CloudSim
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Cloudlets
– The applications/tasks
Processing Elements (Pe:s)
– The CPU
Hosts
Virtual Machines
Datacenters
– Infrastructure Provider

9. DatacenterBroker
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10. Experiments
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2 → 200 users
2 data centers
– 2 hosts each
– OS, Arch, VMM
5 → 20 VMs
– 200 → 1000 MIPS
20 → 40,000 Cloudlets
– With varying lengths
– 100 → 4000 MI

11. E1: VM and Host Level Scheduling
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200 users
5 VMs
– 200, 400, 600, 800, 1000 MIPS
4000 Cloudlets
– 100 → 4000 MI
Change the VM and Host level
scheduling.
{FCFS, RR}

12. Start Time
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13. Finish Time
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14. E2: Application Scheduling Algorithms
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RR and FCFS
– With and without over-subscription
Maximum Resource Utility
Dynamic Allocation
– With partial requirement
satisfaction
– OS, VM, MCT

15. Completion Time and
Execution Time
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200 users
5 VMs
– 200, 400, 600, 800, 1000 MIPS
4000 Cloudlets
– 100 → 4000 MI
– Varying requirements and utility
No time limitation
Maintain 100% Job Success Ratio

16. Mean Submission Time and
Mean Execution Time
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17. Summary
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Each algorithm performs better for
– different criteria
– different tasks
Utility-driven algorithms with Partial
requirement satisfaction take the lead.

18. Summary
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Each algorithm performs better for
u!
– different criteria
yo
– different tasks
nk
ha algorithms with Partial
T
Utility-driven
requirement satisfaction take the lead.

19. Summary
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Each algorithm performs better for
u!
yo ?
– different criteria
nk tasks s
– different ion
ha t
T es
Utility-driven algorithms with Partial
u
Q satisfaction take the lead.
requirement