Dynamic Optimization of Heterogeneous Resource Provisioning in Cloud Computing

Masoumeh Tajvidi

Supervisors: A/Prof. Michael MaherDr. Daryl Essam

School of Engineering and Information Technology (SEIT)

• Introduction

- Cloud computing and its potential benefits

- Challenges!

• Research question

• Related Work

• Research Plan

• Preliminary Work

Outline

1/16

What is Cloud Computing? A broad and deep platform that helps customers build

sophisticated, scalable applications

2/16

Why are companies adopting cloud computing so quickly?

Agility

3/16

Main benefits of utility-like computing

Convert CAPEX to OPEX

Lower Total Cost

No Need to Guess Capacity

Lower Maintenance Cost

4/16

Effectively using of cloud computing

Over-provisioning: purchased resources are not fully utilized cost more than necessary

Under-provisioning: purchased resources are not sufficient to meet the actual demand hurts application performance,

5/16

Efficient resource provisioning: challenge #1 : Various cloud providers offering

multiple Virtual Machine (VM) types

• Virtualization technologies help providers pack their hardware resources into different type of Virtual Machines

• The end-users encounter a complicated decision making problem for choosing the right mix of VMs!

6/16

Efficient resource provisioning: challenge #2 : Multiple pricing models

7/16

Efficient resource provisioning: challenge #3 : Deal with COST and DEMAND

uncertainty The application’s demand is not

known in advance• e.g. online video streaming

applications, like YouTube Channel

The cost of instances in both on-demand and spot is varying!

8/16

Efficient resource provisioning: challenge #4 : Multi-objective problem

Cost is not the only objective for this problem

The trade-off among cost and QoS must also be

handled

For example response time is very critical in latency

sensitive applications , online gaming services, as

the users tend to be very impatient

9/16

Research Question ?

How to dynamically optimize cloud resource provisioning plan as a multi-objective problem in

the real-world?

10/16

Literature review

11/16

stochastic optimization

The time at which parameters become known, divide the problem into stages

Main Question: what to decide in the first stage? Main question: how many VM to reserve Recourse: how to deal with under or over provisioning

Objective function is to minimize the expected cost

12/16

Add complexities of the real-world problem into our model.

Take into account the heterogeneity of VM types

Take into account all three pricing schemes into our model

Solve the problem as a multi-objective optimization problem

Dealing with uncertainty by solving the problem with both

stochastic and approximate programming approaches

enhance the performance of the optimization problem by

using Machine Learning techniques

Research Plan : Big Picture

Challenge #1

Challenge #2

Challenge #4

Challenge #3

13/16

Preliminary results

Replicated one of the available multi stage

programming approach

Modelled the problem in Stochastic MiniZinc

modelling language

Added spot-instance pricing model into our model

14/16

Stochastic MiniZinc Model Results Before and After Adding Spot Instances

15/16

Thank you .