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Evaluating the Application Scheduling Algorithms in CloudSim
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Application Scheduling in CloudSim
Presented by: Pradeeban Kathiravelu
Supervised by: Prof. Luís Veiga
Implementation of Distributed Systems
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Application Scheduling
Scheduling an application– to be executed– using a resource– in a cloud environment
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Aim
Evaluating the Scheduling algorithms– Strict matchmaking-based– Utility-driven
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Aim
Evaluating the Scheduling algorithms– Strict matchmaking-based– Utility-driven
Criteria– Mean execution time– Mean user submission time– Average resource utilization– Job Scheduling Success Ratio
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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)
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Utility Algorithm→
User Satisfaction Partial Requirement Satisfaction.
– Number of metrics– Are they equally important?
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Evaluation
CloudSim– Simulation tool for cloud
computing Representing by objects.
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CloudSim
Cloudlets– The applications/tasks
Processing Elements (Pe:s)– The CPU
Hosts Virtual Machines Datacenters
– Infrastructure Provider
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DatacenterBroker
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Experiments
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
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E1: VM and Host Level Scheduling
200 users 5 VMs
– 200, 400, 600, 800, 1000 MIPS 4000 Cloudlets
– 100 → 4000 MI Change the VM and Host level
scheduling. {FCFS, RR}
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Start Time
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Finish Time
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E2: Application Scheduling Algorithms
RR and FCFS– With and without over-subscription
Maximum Resource Utility Dynamic Allocation
– With partial requirement satisfaction
– OS, VM, MCT
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Completion Time and Execution Time
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
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Mean Submission Time and Mean Execution Time
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Summary
Each algorithm performs better for– different criteria– different tasks
Utility-driven algorithms with Partial requirement satisfaction take the lead.
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Summary
Each algorithm performs better for– different criteria– different tasks
Utility-driven algorithms with Partial requirement satisfaction take the lead.
Thank you!
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Summary
Each algorithm performs better for– different criteria– different tasks
Utility-driven algorithms with Partial requirement satisfaction take the lead.
Thank you!
Questions?
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