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Resource provisioning optimization in cloud computing
- 1. Dynamic Optimization of Heterogeneous Resource Provisioning in Cloud Computing Masoumeh Tajvidi Supervisors: A/Prof. Michael Maher Dr. Daryl Essam School of Engineering and Information Technology (SEIT)
- 2. • Introduction - Cloud computing and its potential benefits - Challenges! • Research question • Related Work • Research Plan • Preliminary Work Outline 1/1 6
- 3. What is Cloud Computing? A broad and deep platform that helps customers build sophisticated, scalable applications 2/1 6
- 4. Why are companies adopting cloud computing so quickly? Agility 3/1 6
- 5. Main benefits of utility-like computing Convert CAPEX to OPEX Lower Total Cost No Need to Guess Capacity Lower Maintenance Cost 4/1 6
- 6. 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, 5/1 6
- 7. 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! 6/1 6
- 8. Efficient resource provisioning: challenge #2 : Multiple pricing models 7/1 6
- 9. 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! 8/1 6
- 10. 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 9/1 6
- 11. Research Question ? How to dynamically optimize cloud resource provisioning plan as a multi-objective problem in the real-world? 10/1 6
- 13. 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 12/1 6
- 14. 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 13/1 6
- 15. 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 14/1 6
- 16. Stochastic MiniZinc Model Results Before and After Adding Spot Instances 15/1 6
- 17. Thank you .
Editor's Notes
- Today, What I am going to talk about is as follows:
- In the simplest term: cloud computing is the provision of IT resources on-demand using a pay as you go model over the internet
- So why are companies adopting cloud computing so quickly? There is a very simple answer to this, which is that by using the cloud you can inject some much-needed agility into your organization. So resource will be available to you when you need them and you can control them through a simple interface and when you don’t need those resources you no longer have to pay for them. 3. You can simply Plug in to the cloud and pay as you use, which reminds us of traditional utilities such as water and electricity!!
- 1. Breaking that down a little more, the four areas the customers get most benefit from when they are using cloud computing are as follows: 2. firstly they can convert CAPEX to OPEX, so you no longer have this upfront investment required to buy and build your IT infrastructure rather than that you pay as you go for the services you consume 3. And also you would normally find that by using the cloud your total cost of operation over ownership will be lower. 4. You no longer need to guess the amount of capacity but you need to run your application since the environment is elastic in nature and you can access as much or as little resources you need in order to support your application , you can scale up and down 5. The cloud provider is responsible for maintenance of the infrastructure and security, that can remove the need for you to a lot of staff and help you run a more lean organization which is more focused meeting the needs of your customers (business) 6. There are all very fascinating for the small growing companies or start ups: because they arent heavily invensted on IT infrustruvture , if you want to try something new, go ahead and try it, if it works fantastic , scale it up and improve it! It doesn’t work you can stop your experiment Capital expenditures (CAPEX) and operating expenses (OPEX) represent two basic categories of business expenses
- Lets have a look at the traditional infrastructure model and its problems: Explin over provisioning and under provisioning 3. But With cloud compuitng we can match resources to demand as this figure 4. But for getting to such a idealistic resource provisioingn model, the cloud user needs to smartly plan the resource provisioning problem. Which is not that much easy due to available challenges that I am going to talk about them in next slides
- Choosing the right mix
- Reserved instances the cheapest On-demand instances the highest price Spot Instances allow you to specify the maximum hourly price that you are willing to pay to run a particular instance type, usually lower than the On-Demand rate. 4. (the spot instances are the unused on-demand instances) The Spot Price fluctuates based on supply and demand for instances, but customers will never pay more than the maximum price they have specified. If the Spot Price moves higher than a customer’s maximum price, the customer’s instance will be shut down by the cloud provider
- Therefore the customer is not aware of future price of resources. This uncertainty should be considered, since it can affect the final decision on resource provisioning problem.
- Last but not the least challenge is that we can not minimize the cost and neglect all other QoS!
- These are challenges in order to answer my research question
- Little research has been conducted from the cloud end-user view. Most of them focused on deterministic formulation over fixed parametrs where the problem has a perfect foresight. Those that have considered uncertainty in their problem typically just considered one objective function or only one or two pricing models. The techniques : for cloud resource provisionig and explain the difference between stochastic and approximate programming and why we have chosen stochastic? THE DIFFERENCES?
- Stochastic optimization consider the problem as some parameters are unknown The problem is divided by some stages, based on the time at which parameters become known 2-stage problem is something like this: we have first stage decision and second stage decision which also called recourse .. The main question is…. We want to know what to do before an stochastic event occurs The objective function is minimizing the expected cost considering all uncertain parameters as different scenarios
- Two of the main contenders in the methodology of optimization under uncertainty are “stochastic programming” and “Approximate dynamic programming.” Both take a probabilistic approach to uncertainty and contemplate not just one decision and one observation, but a possible interplay between decisions and observations in stages. Therefore we will solve the problem with these two approaches and compare their results to find out which one is more appropriate for our problem. In order to enhance the performance of our optimization problem, a machine learning technique integrates into this complex optimization problem, such as reinforcement learning technique, to help it learn while optimizing..
- What results we get so far?