DEADLINE BASED RESOURCE PROVISIONING AND SCHEDULING ALGORITHM FOR

SCIENTIFIC WORKFLOWS ON CLOUDS(BASED ON PSO)

Maria Alejandra Rodrigues and Rajkumar Buyya

*** This work was developed by Maria Alejandra Rodrigues and Rajkumar Buyya. I am just presenting slides on their work.

Objective:

To design a new PSO based resource provisioning and scheduling strategy for scientific workflows on cloud

Minimization of total execution cost Deadline constrained

Workflow:

Automation of process during which inputs or/and outputs are passed from one task to other task(s) according to some rules

Used to model large-scale scientific problems in areas such as bioinformatics, astronomy, physics, etc.

Scientific workflows have ever-growing data and computing requirements

Demand a high-performance computing environment.

Structure of some scientific workflows:

Montage(Used in

Astronomy)

LIGO(Gravitational

waves)

SIPHT(Gene encoding)

CyberShake(Earthquake hazard

characterization)

Stages of workflow execution:

1. Resource provisioning phase Computing resources that will be used to

run the tasks are selected and provisioned

2. Schedule generation phase A schedule is generated and tasks are

assigned to the best suited resource.

Optimization is done in both stages so that user defined QOS are met.

Problems:

Parent tasks and child tasks dependency rules.

Scheduling: NP-hard problem Impossible to find optimal solution in

polynomial time; focus on near optimal ones

Transfer time affects execution time

Problem Formulation:

Workflow: W = (T,E) T = {t1, t2, …, tn} E = {eij};

Edge eij : There is data dependency between task ti and tj; tj is child of ti

User defined deadline of the workflow: w Each VMi is associated with processing capacity (PVMi) and

cost per unit time (CVMi) VM usage cost is quantized.

Problem Definition:

To find a schedule to execute the workflow that minimizes TEC subject to TET ≤ w

Particle Swarm Optimization: (1)

Population based stochastic optimization technique

Inspired by social behaviour of birds and fish

Shares similarities between other evolutionary computing techniques like GA

Developed by Dr. Eberhart and Dr. Kennedy in 1995

Particle Swarm Optimization: (2)

Particle: An individual (candidate solution) that has the ability to move iteratively through the problem space

Each particle has an associated position and velocity pbest and gbest based on fitness function In each iteration, velocity of each particle updated

towards pbest and gbest locations

Iterated until some stopping criterion is met.

PSO Modelling: (1)

KEY STEPS1. Encoding of the problem; representation of

candidate solutions2. Defining the fitness function or objective

function

Particle: Workflow and its associated tasks Dimension of a particle: Number of tasks in the

workflow; n-dimensional Fitness function: TEC

PSO Modelling: (2)

Encoding Example:

Some other strategies are also used for convenience

Makespan, TEC and deadline are evaluated and analysed for different types of workflows

THE END