Qos based scheduling algorithm for workflow based application in utility grid
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QOS-based scheduling algorithm for workflow-based application in utility grid. Lingzhi Zhang 2011.6.1. background. Many grid applications such as bioinformatics and astronomy require workflow processing in which tasks are executed based on their control or data dependencies.

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Qos based scheduling algorithm for workflow based application in utility grid

QOS-based scheduling algorithm for workflow-based application in utility grid

Lingzhi Zhang

2011.6.1


Background
background application

  • Many grid applications such as bioinformatics and astronomy require workflow processing in which tasks are executed based on their control or data dependencies



  • in utility grid, services are pricing based on the level of QOS offered. And usually the users do not need to complete the task earlier than they require, they prefer to choose cheaper services which are sufficient to meet their requirement.

  • So execution cost must be considered when scheduling tasks on resources in utility grid.


  • We model workflow application as a directed acyclic graph (DAG)

  • We assume that a child task can not be executed until all of its parent tasks are completed. In a given task graph, a task without any parent task is called entry task, and a task without any child task is called exit task. Here we add two dummy tasks and


MCCP (DAG)

  • Utility grid is modeled as a graph , where R stands for a set of grid resources, S is a set of optical switch nodes and L is a set of links including optical links and access links. In this model, we use (0<=i<= the total number of links) to stand for the available bandwidth of link li at time t.


MCCP (DAG)

  • Firstly, we will sort the tasks using list-scheduling algorithm. We can get a sequence of tasks.

  • Then we will compute the scheduled start time and end time of each task


MCCP (DAG)

  • to each task, there is a critical path from the entry task to exit task and pass through task i.


MCCP (DAG)

  • For the first task, find the resource with the minimum execution cost while meet the time interval computed above. Then set the actual end time

  • For the following task, if the resource number is equal to the resource number of the father task, compute whether (t.getTaskLength()/res.getMIPS())<(t.getScheduleET()-ft.getActualET())

  • If not, compute whether

    (t.getTaskLength()/res.getMIPS())<(t.getScheduleET()-t.getActualET())



MCCP required.

  • If we give resource i to the task, find the minimum cost, including execution cost and communication cost.

  • In this way, we can find the best resource .


Simulation
simulation required.

  • In simulation, we compare the proposed algorithm with the MDP and greedy cost.

  • the greedy cost do not take the link cost into consider. The MDP divides the tasks into different levels.


谢 谢! required.


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