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

An Advance Algorithm for Task Management On Activity Based Costing in Cloud Computing By : Ashutosh Ingole Sumit Chavan Rajesh Singh Sinhgad Institute of Technology ,LONAVALA 19 th Apr

An Advance Algorithm for

Task Management On

Activity Based Costing in

Cloud Computing

By : Ashutosh Ingole

Sumit Chavan Rajesh Singh

Sinhgad Institute of Technology ,LONAVALA 19 th Apr 2012

Introduction

Cloud Computing Architecture Problem Statement

Problems Overall Scenario Flow of Algorithm Cost calculation and Scheduling Feedback Mechanism Advantages

Conclusion and References

19 th Apr 2012

Sinhgad Institute of Technology ,LONAVALA

Sinhgad Institute of Technology ,LONAVALA 19 th Apr 2012

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

Cloud Computing Architecture

19 th Apr 2012 Sinhgad Institute of Technology ,LONAVALA

Problem Statement

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To reduce the total time required for task scheduling in

Cloud Computing using Activity Based Costing Algorithm

19 th Apr 2012

Sinhgad Institute of Technology ,LONAVALA

19 th Apr 2012

Overall Scenario

$4

$1

$2

Dynamic Priority Allocator

Queue 1

Queue 2

Queue 3

Cost Calculation

Decide Priority According

To Cost

Virtual Machines

Scheduling Server

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Flow of Algorithm

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Dependencies and DAG

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• 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.

T1

T3

T2

T4

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Cost calculation of

Task

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Parameters considered

• Resource cost

• Arrival Time

• Predicted Execution Time

• Dependency (DAG)

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Continued… (Formulae)

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1)Resource Cost Evaluation

2)Estimated Execution Time

Where,

Tij: estimated execution time for ith activity of jth task Tj: estimated execution time for complete jth task

Kj: total number of activities in jth task

Continued… (Formulae)

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Total cost of Activity =

19 th Apr 2012

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

Total cost of Task = ∑ (Activity Cost)

Concept of Waiting Queue

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• 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

Waiting Queue

Ready Queue

19 th Apr 2012

Feedback and Update

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• 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

updated.

19 th Apr 2012

Waiting Queue

Failure Cases

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• Simulation parameters

• Average bounded slowdown and average per-processor slowdown

• Average waiting time & average weighted waiting time

• Network Error

• Computational Error

• Power Failure

• Queue Overflow

19 th Apr 2012

Sinhgad Institute of Technology ,LONAVALA

 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.

19 th Apr 2012

Sinhgad Institute of Technology ,LONAVALA 19 th Apr 2012

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

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Any Questions…???

19 th Apr 2012

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Thank You…!!!

19 th Apr 2012

Sinhgad Institute of Technology ,LONAVALA