Optimization of Resource Provisioning Cost in Cloud Computing
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Transcript of Optimization of Resource Provisioning Cost in Cloud Computing
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