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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?