OPTIMIZATION OF RESOURCE PROVISIONING

COST IN CLOUD COMPUTING

Aswin K K

S8 CSE-A

MKAJECS009

Guide:

Mr. Sunil Kumar P V

Contents• Overview of Cloud Computing

• Challenge of Resource Provision in the Cloud

• Optimal Cloud Resource Provisioning

• OCRP Model

• Provisioning Phases

• Provisioning Stages

• Reservation Contracts

• Uncertainty

• Benders Decomposition

• Sample-Average Approximation

• Numerical Results: Provisioning Cost

Overview of Cloud Computing

• Large distributed system

• Large pool of resources

• Multiple provider

• Multiple data-centers

• Virtualization

• Internet access

• Pay-per-use basis

• Provisioning options/plans

• On-demand

• Reservation

• Example: Amazon

Overview of Cloud Computing: Provisioning

Plans• Reservation can reduce the total provisioning cost

• On-demand (Small Instance): 0.085 x 365 x 24 = $744.60 for 1yr contract

• Reservation: 227.50+(0.03x365x24) = $490.30 for 1yr contract or 34% cheaper but 49% cheaper for 3yr contract

Source: http://aws.amazon.com/ec2

Challenge of Resource Provision in the Cloud

• Resource provision = activity to provide / supply resource (to accommodate users/systems)

• Goal: How many VMs (i.e., how much resource) do we need to provision in advance (i.e., provision with reservation plan) ?

• Challenge

• Multiple cloud providers and Quality of Service(QoS) & Service Level Agreement(SLA)

• Multivariate uncertainty e.g., demand, price, availability

• Optimal solution under uncertainty

• Computational complexity

VM = Virtual Machine

Challenge of Resource Provision in the Cloud:

Uncertainty• Uncertainty of price

• On-demand price might be fluctuated

• Uncertainty of availability

• Free / cheap resources offered by a cloud provider might be provided based on weak SLAs

• Internet bandwidth is not reliable until cloud resources might not be accessible

Challenge of Resource Provision in the Cloud:

Uncertainty

• Uncertainty of demand

Optimal Cloud Resource Provisioning

• OCRP algorithm is proposed

• To minimize the expected resource provisioning cost in multiple provisioning stages e.g., 4 stages in quarter plan, 12 stages in 1-Y plan, 36 stages in 3-Y plan, etc.

• To consider multivariate uncertainty

• Optimal solution is obtained by formulating and solving stochastic integer programming with multi-stage recourse

• Techniques to solve OCRP: deterministic equivalence, benders decomposition, sample-average approximation

• Several experiments show that OCRP can minimize the cost under uncertainty

OCRP Model

• System model of cloud computing

Provisioning Phases

• Provisioning phase: time interval when resources need to be provisioned or utilized1. Reservation phase: reserve resources

2. Expending phase: utilized the reserved resources

3. On-demand phase: provision more resources on-demand

Provisioning Stages

• Provisioning stage: time epoch when cloud broker makes a decision

• Examples

• Two stages: current and future (e.g., now and next month)

• Twelve stages: Yearly plan = January to December

Reservation Contracts

• Reservation contract: signed contract stating the time duration of availability of reserved resource

• During the contract period, price is discounted

• Examples: 3-month (K1) and 6-month (K2) contracts

Uncertainty• Stochastic programming requires uncertainty

parameters, namely scenarios given by set Ω

• Scenarios can be described by a probability distribution

• Set Ω has finite support with probabilities p(ω) Є [0,1] where ω=(ω1,…, ω|T|) Є Ω

Ω = ∏ Ωt = Ω1 x Ω2 x…x Ω|T|tЄT

Benders Decomposition• Benders decomposition

breaks down an optimization problem into smaller problems which can be solved independently (parallel)

• Given the results obtained from master & sub-problems, the lower & upper bounds of solution can be calculated• Convergence bounds checked by zv

(ub) - zv(lb) < Є

where zv(ub) - zv

*(e) and zv(ub) = zv

*(e) – αv + zv*(r) + Σ

zv*(o) (ω)

Sample-Average Approximation• If the number of scenarios (Ω) is numerous, it

may not be efficient to obtain the solution of OCRP

• SAA addresses the problem by sampling N scenarios, then SAA-based OCRP formulation is solved given the N samples

• We modelled OCRP based on SAA approach to choose N that yields the acceptable approximated solution

• SAA can be parallelized as well

Numerical Results: Provisioning Cost

Conclusion• Resource provisioning algorithms based on

stochastic programming and robust optimization have been proposed

• The algorithms can be applied in real world to minimize provisioning costs

• Resource management framework for cloud computing will be composed

Reference• Paper on “Optimization of Resource

Provisioning Cost in Cloud Computing” presented by Sivadon Chaisiri in PDCC Seminar, Parallel & Distributed Computing Centre, Friday 21st, 2011

• Paper on “Cloud Computing for on-Demand Resource Provisioning” presented by Ignacio M Llorente in 7th NRENs and Grids Workshop at Trinity College, Dublin, September 2, 2008

Questions

THANK YOU