Application scheduling in cloud sim

6,746 views

Published on

Evaluating the Application Scheduling Algorithms in CloudSim

Published in: Technology, Business
15 Comments
4 Likes
Statistics
Notes
No Downloads
Views
Total views
6,746
On SlideShare
0
From Embeds
0
Number of Embeds
767
Actions
Shares
0
Downloads
482
Comments
15
Likes
4
Embeds 0
No embeds

No notes for slide

Application scheduling in cloud sim

  1. 1. Application Scheduling in CloudSim Presented by: Pradeeban Kathiravelu Supervised by: Prof. Luís Veiga Implementation of Distributed Systems 1
  2. 2. Application Scheduling  2 Scheduling an application – to be executed – using a resource – in a cloud environment
  3. 3. Aim  3 Evaluating the Scheduling algorithms – Strict matchmaking-based – Utility-driven
  4. 4. Aim   4 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. 5. Objective Function → Algorithm  Strict matchmaking-based – Minimum Execution Time (MET) – Minimum Completion Time (MCT) – Maximum Resource Utilization – Matchmaking – First-come first-served (FCFS) – Round Robin (RR) 5
  6. 6. Utility → Algorithm   6 User Satisfaction Partial Requirement Satisfaction. – Number of metrics – Are they equally important?
  7. 7. Evaluation   7 CloudSim – Simulation tool for cloud computing Representing by objects.
  8. 8. CloudSim      8 Cloudlets – The applications/tasks Processing Elements (Pe:s) – The CPU Hosts Virtual Machines Datacenters – Infrastructure Provider
  9. 9. DatacenterBroker 9
  10. 10. Experiments     10 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. 11. E1: VM and Host Level Scheduling      11 200 users 5 VMs – 200, 400, 600, 800, 1000 MIPS 4000 Cloudlets – 100 → 4000 MI Change the VM and Host level scheduling. {FCFS, RR}
  12. 12. Start Time 12
  13. 13. Finish Time 13
  14. 14. E2: Application Scheduling Algorithms    14 RR and FCFS – With and without over-subscription Maximum Resource Utility Dynamic Allocation – With partial requirement satisfaction – OS, VM, MCT
  15. 15. Completion Time and Execution Time      15 200 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. 16. Mean Submission Time and Mean Execution Time 16
  17. 17. Summary   17 Each algorithm performs better for – different criteria – different tasks Utility-driven algorithms with Partial requirement satisfaction take the lead.
  18. 18. Summary   18 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. 19. Summary   19 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

×