Cost Minimization for Provisioning Virtual Servers in Amazon EC2


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It's about how we can optimally rent virtual server (i.e., EC2 instances) from Amazon.

Firstly presented in IEEE MASCOTS 2011 conference in Raffles Hotel, Singapore.

Published in: Education, Technology, Business
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Cost Minimization for Provisioning Virtual Servers in Amazon EC2

  1. 1. Cost Minimization for Provisioning Virtual Servers in Amazon Elastic Compute CloudSivadon Chaisiri1, Rakpong Kaewpuang1, Bu-Sung Lee1,2, and Dusit Niyato1 p g p g g y1 School of Computer Engineering, Nanyang Technological University, Singapore2 HP Labs SingaporeIn Proc. IEEE Int. Sym. On Modeling, Analysis and Simulation of Computer and Telecommunication Systems (MASCOTS) 2011, Raffles Hotel Singapore July 25 27 2011 Hotel, Singapore, 25-27 2011CeMNeT/PDCC MeetingOctober 13th, 2011
  2. 2. Agenda• Amazon Elastic Compute Cloud (EC2)• Research Challenges• System Model & A S t M d l Assumptions ti• Proposed Algorithms• Performance Evaluation• Summary 2
  3. 3. Amazon Elastic Compute Cloud• EC2 = virtual server hosting service – Dynamic & scalable provision of EC2 instances – Several types of servers (i e instance types) (i.e., – Pay-per-use basis – Selectable hosting zones (i e Regions and Zones) (i.e., – Integrated services e.g., EBS, S3, and SimpleDB• Ser er Server pro isioning provisioning options options: on-demand, d d reserved, and spot options 3
  4. 4. Amazon Elastic Compute Cloud (cont..)• Prices of Linux instance in Northern VirginiaInstance Type On-demand Reserved* Reserved* Spot** 1-Year 3-Yeart1.micro ¢2 / hour ¢0.7 / hour, ¢0.7 / hour, ¢0.9 / hour fee $54 fee $82m1.small ¢8.5 / hour ¢3 / hour, ¢3 / hour, ¢2.9 / hour fee $227.50 fee $350c1.xlarge ¢68 / hour ¢24 / hour, ¢24 / hour, ¢24 / hour fee $1 820 $1,820 fee $2 800 $2,800* The one-time fee is paid in advance. The usage price is discounted.** A spot price on December 11 2010 11, 4
  5. 5. Amazon Elastic Compute Cloud (cont..)• Spot option – Auction-based provisioning – User submits a request & bid price of spot instances to EC2 – Spot price set by Amazon is fluctuated based on supply- and-demand – Amazon makes a decision: IF bid price > spot price THEN the spot instances can run – Every running spot instance is charged with the same spot price – Running spot instances can be terminated without warning! 5
  6. 6. Amazon Elastic Compute Cloud (cont ) (cont..)• Server provision of Linux c1.xlarge instance Option Price Cost SaveOn-demand (1 Year) ¢68 / hour $5,956.80 BasedReserved (1 Year) ¢24 / hour, fee $1 820 hour $1,820 $3,922.40 $3 922 40 34.15% 34 15%Spot* (1 Year) ¢24 / hour $2,102.40 64.71% Option Price Cost SaveOn-demand (3 Years) ¢68 / hour $17,870.40 BasedReserved (3 Years) ¢24 / hour, fee $2,800 $9,107.20 49.04%Spot* (3 Years) ¢24 / hour $6,307.20 64.71%* Spot price is assumed to be fixed 6
  7. 7. Research Challenges• EC2 instances provisioning Only 1 server is required with prob 0.6 – How many instances ? and which instance types to be provisioned ? – How to achieve the optimal number of instances under uncertainties ? Cost minimization of server provision Demand uncertainty• Demand uncertainty of an application f li ti – Amount of server-hours required to Spot price > on-demand with prob 0.37 execute certain application pp Reservation price ($0.007)• Price uncertainty of spot instances On-demand price ($0.02) – Spot price < reservation, >= reservation, < on-demand, >= on demand on demand on-demand• Availability of spot instances Price uncertainty – Described by Bernoulli distribution of t1 micro t1.micro (% success bid or % failure bid) 7
  8. 8. System Model & Assumptions• Two algorithms based on stochastic programming – Long-term server provisioning algorithm considers reserved and on-demand – Short-term server provisioning algorithm considers spot and on-demand p g g p• Algorithm solution: the optimal number of instances of each instance type and provisioning option provisioned to each application• A EC2 i t An instance i provisioned t a certain application is i i d to t i li ti• Multiple instances can be grouped to serve the same application 8
  9. 9. System Model & Assumptions (cont..)• Performance factor – : performance of the based server – : performance of instance type f fi t t• Not consider how a bid price is defined for spot instances• Neglect data transfer costs• Additional product/service costs are neglected e.g., S3 and EBS• Checkpointing mechanism is available• Uncertainty – Uncertain parameter is described by scenarios associating with a probability distribution (a scenario occurs with a probability) – The set of all possible scenarios is obtained as follows: 9
  10. 10. Proposed Algorithms• Stochastic programming model of long term provisioning long-term• Robust optimization model • “Robust”: less sensitive to any occurrence of uncertainty • Solution-robustness : close to optimal solution • Model-robustness : avoidable on-demand & oversubscribed costs Expectation Variance Penalty 10
  11. 11. Proposed Algorithms (cont..)• St h ti programming model of short-term provisioning Stochastic i d l f h tt i i iwhere denotes the total provisioning cost 11
  12. 12. Proposed Algorithms (cont..)• S Sample-average approximation (SAA) i applied t short- l i ti is li d to h t term planning – Computational complexity reduction i.e., scenario set is too big p p y , g – Sampling technique e.g., Monte Carlo or Latin Hypercube – Scenario size is reduced while (near-) optimal solution is achieved• Solution of SAA (detailed in Section IV B) IV-B) – Lower bound estimates where and – Upper bound estimates where and 12
  13. 13. Performance Evaluation• Numerical studies are performed to evaluate long-and short- term provisioning algorithms p g g• The algorithms are implemented and solved by GAMS/CPLEX• Only single one application is considered• Obtain performance factors (λ) by HPL and GotoBLAS2• Real data from HPCC@NTU and Amazon EC2• E l t di t Evaluated instance ttypes Instance Type Price of Price of GFLOPS Performance reserved On-demand factor, λ c1.xlarge ¢24 / hour, fee $1,820 ¢68 / hour 57.20 1/2 m1.xlarge ¢24 / hour, fee $1,820 ¢68 / hour 24.44 1/3 t1.micro ¢0.7 / hour, fee $54 ¢2 / hour 4.96 1/15 13
  14. 14. Performance Evaluation (cont..)Evaluation of long term provisioning algorithm (1 year planning) long-term• Demand: discrete normal dist. with 16 scenarios, mean 8.5, var 2• Notations – : optimal solution of deterministic optimization problem – : solution of on-demand provisioning – : optimal solution of stochastic programming problem – : optimal solution of robust optimization problem – Optimality closeness, , for (od) is always 1.5 – odc : on-demand cost odc. – osc. : oversubscribed cost 14
  15. 15. Performance Evaluation (cont..)Evaluation of short-term provisioning algorithm p g g• Uncertainty parameters• Bid prices for c1.xlarge and m1.xlarge: $0.264 and $0.240• Probability of success bid for c1.xlarge and m1.xlarge: 0.899 and 0.467• Results: provision 100 server-hours for both instance types, but zero server-hour for t1.micro• Other experiment: spot p p p prices p1 and p2 occur with prob 0.95 and 0.05 p 15
  16. 16. Performance Evaluation (cont..)Evaluation of short-term provisioning algorithm short term • Evaluation of different sampling techniques using SAA approach Monte Carlo Latin Hypercube 16
  17. 17. Summary• Long- and short-term server provisioning algorithms based on stochastic programming have been proposed• The algorithms can minimize the total provisioning costs under demand and price uncertainty• The algorithms can be applied to other cloud providers (e.g., GoGrid and SpotCloud Market)• Future work: efficient strategies for bidding spot instances under price and demand fluctuation 17
  18. 18. THANK YOU 18
  19. 19. System Model & Assumptions (cont..)• Discrete probability distributions – Distribution of spot prices, EC2 publicly provides historical spot prices – Distribution of demand e.g., log files of server access demand, e g• How to achieve the amount of server-hours Number of business transaction BLACK BOX / computational jobs Amount of server-hours server hours 19