An Advance Algorithm for Task Management On Activity Based Costing in Cloud Computing

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An Advance Algorithm for Task Management On

Activity Based Costing in Cloud Computing

By :

Rajesh Singh

Sinhgad Institute of Technology ,LONAVALA19th Apr 2012

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Cloud Computing

ArchitectureProblem StatementProblems

Overall ScenarioFlow of Algorithm

Cost calculation and SchedulingFeedback Mechanism

Conclusion and References

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Sinhgad Institute of Technology ,LONAVALA

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What is Cloud Computing?

Technical Key Points

•User interaction interface: How users of cloud interface with the cloud•Services catalog: Services a user can request

•System management: Manages the resources available

•Provisioning tool: Carves out the systems from the cloud to deliver on the

requested service

•Monitoring and metering: Tracks the usage of the cloud (optional)•Servers: Virtual or physical servers managed by System management

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Cloud Computing Architecture

19th Apr 2012Sinhgad Institute of Technology ,LONAVALA

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Problem Statement

To reduce the total time required for task scheduling in Cloud Computing using Activity Based Costing Algorithm

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Overall Scenario

Dynamic Priority Allocator

Queue 1

Queue 2

Queue 3

Cost Calculation

Decide Priority According

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Flow of Algorithm

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Dependencies and DAG

•Get the Activity dependencies form Task divider module.•Calculate the DAG from the given information.

•According to the DAG fill up the Task queue.

•An Activity only starts after all its immediate predecessors finish.•Activities with no immediate predecessor are entry-activities, and

activities without immediate successors are exit-activities.

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Cost calculation of Task

Parameters considered

•Resource cost•Arrival Time

•Predicted Execution Time•Dependency (DAG)

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Continued… (Formulae)

1)Resource Cost Evaluation

2)Estimated Execution Time

Tij: estimated execution time for ith activity of jth taskTj: estimated execution time for complete jth taskKj: total number of activities in jth task

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Continued… (Formulae)

Total cost of Activity =

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∑ (∑ Resource cost for a single activity) * execution time

Total cost of Task =∑ (Activity Cost)

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Concept of Waiting Queue

• In case of dependent activities, if a certain activity say A11 needs server1 and its dependent activity say A12 needs to be on server2, then without complete execution of A11, we cant forward A12 from ready queue to server2

• In such a case, we also cant keep a2 in ready queue for a long time and hence we use waiting queue to store these kind of dependent activities

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Feedback and Update

•When we get feedback from server that a particular activity is

completed then we can schedule its dependent activity from waiting queue to its destination server.

•After that each resource table and universal resource table gets

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• Queue Overflow

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An ABC-system of low complexity, such as a system with a small number of cost drivers, is not only easier to handle but also easier to understand

It seems to be an interesting next step to analyze how ABC decision rules perform when several approximations apply.

Thus ABC reduces the scheduling cost and reduces delays.

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Sandeep Tayal, University School of Information Technology, Guru Gobind Singh,

Indraprastha University, Delhi- 10006, India “Tasks Scheduling optimization for the Cloud Computing System2011, IJAEST,2011

Archana Ganapathi, Yanpei Chen, Armando Fox, Randy Katz, David Patterson Computer Science Division, University of California at Berkeley,” Statistics-Driven Workload Modeling for the Cloud”, IEEE 2011

QI CAO, ZHI-BO WEI, WEN-MAO GONG International School of Software Wuhan University Wuhan, China, “An Optimized Algorithm for Task Scheduling Based On Activity Based

Costing in Cloud Computing”, IEEE 2009

Jinhua Hu, Jianhua Gu, Guofei Sun, Tianhai Zhao, NPU HPC Center Xi‟an, China “A

Scheduling Strategy on Load Balancing of Virtual Machine Resources in Cloud Computing Environment”, IEEE 2010

Jiahui Jin, Junzhou Luo, Aibo Song, Fang Dong and Runqun Xiong School of Computer Science and Engineering, Southeast University Nanjing, P.R China “BAR: An Efficient Data Locality Driven Task Scheduling Algorithm for Cloud Computing” , IEEE 2011

GAN Guo-ning, HUANG Ting-Iei, GAO Shuai School of Computer science and engineering Guilin University of Electronic Technology Guilin, China “Genetic Simulated Annealing Algorithm for Task Scheduling based on Cloud Computing Environment”, IEEE 2010 [1]

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Any Questions…???

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Thank You…!!!

19th Apr 2012