distributed systems meet economics: pricing in the cloud authors: hongyi wang, qingfeng jing, rishan...
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Distributed Systems Meet Economics: Pricing In The Cloud
Authors: Hongyi Wang, Qingfeng Jing, Rishan Chen, Bingsheng He, Zhengping He, Lidong Zhou
Presenter: Sajala Rajendran
AbstractPricing Scheme in cloud computing – bridge
that decouples users from cloud providersRelationship between Cloud computing and
pricing has brought a significant change to the system design and optimization
Studies conducted on Amazon EC2 and on a local cloud computing testbed
IntroductionPay-as-you-go model:
Cloud Providers have a pricing scheme for their users.
Users utilize cloud at a very low cost Profit for providersVariety of applications – storage backup, e-
commerce, high performance computing“two-party” computation with pricing as the bridgePricing depends on two factors
System Design and Optimization Fairness and Competitive pricing
Contd…Pricing induced interplay between systems and
economicsCost as an explicit and measurable system metricPricing fairnessEvolving system dynamicsCost of failures
Experiments conducted on Amazon EC2 and Spring have the following results:Optimization for cost is hard for userPricing unfairnessDifferent system configuration significantly imapcts cost
and profitFailure occurrences
Pay-as-you-go ModelPricing helps to shape how systems are usedAmazon charges $0.095/virtual machine hourMany pricing schemes are introducedSeveral alternative pricing schemes have
been proposedE.g. Gurmeet Singh and Carl Kesselman
suggested dynamic pricing on resource consumption.
WorkloadsPostmark
I/O intensive benchmark Measures transaction rates for a workload
approximating an Internet email server For experiment : File size 5 GB and number
of transactions is 1000PARSEC (Princeton Application Repository
for Shared Memory Computers) Benchmark suite for chip-multiprocessors Composed of multithreaded programs
9 applications and 3 kernels Blackscholes – High performance computing Dedup – Storage archival
For experiment: 184 MB input data for Dedup and 10 million options for Blackscholes
Hadoop Hadoop 0.20.0 for large scale data processing WordCount and StreamSort For experiment: Input data set is 16 GB
MethodologiesAmazon EC2
Charged according to the pricing scheme of Amazon Cost user = Price x t
t : total running time of the task (Hours) Price : price per virtual machine hour
Excluding storage and data transfer costsSpring System
Provides virtual machines to the users Consists of two modules – VMM (Virtual machine
monitor) and Auditor Provider Profit = Payment from users – Total
provider expense
Hamilton’s EstimationsTotal cost of full burdened power consumption Cost full = p x Praw x PUE
p - Electricity price (dollars/KWh)
Praw - Total energy consumption of servers and
routers PUE – PUE value of the data center
Total provider cost = (Cost full + Cost amortized ) x Scale Scale = Estimated total cost -------------------------------- Cost full + Cost amortized Cost amortized = C amortizedUnit x t server
C amortizedUnit - Amortized cost per hour per server t server - Elapsed time on the server (hours)
Estimation of Praw
For a server, the energy consumption is calculated based on resource utilization
Pserver = Pidle + ucpu x c0 + uio x c1
ucpu - CPU utilization(%) uio - I/O bandwidth (MB/sec) c0 and c1 - coefficients in the model
Eight core machine for evaluating single-machine benchmarks
Cluster consisting of 32 four-core machines for evaluating Hadoop
Power meter used for measuring power consumption of a server
Total dollar cost is calculated based on Hamilton’s estimations on a data center of 50,000 servers. (PUE =1.7 , Scale = 2.24 , Energy price = $0.07/kWh , C amortized
Unit = $0.08/hr
Contd..An Intel 80 GB X25-M SSD is used to replace
a SATA hard drive adjusting the amortized cost in the machine with an SSD to $0.09/hr.
System throughput = Number of tasks finished/hr + user costs + provider profits.
Efficiency of Provider’s investment ROI = Profit/Cost provider x 100 %
Provider Optimization on SpringBased on varying the number of concurrent
VM’s from one to four on the same physical machine.
ObservationsConsolidation reduces power consumption of
150% and 21% on Praw for Blackscholes and Postmark respectively
Decrease of power cost and increase of user cost, increases provider’s profit significantly.
ROI increases to 180% on Postmark and 340% on Blackscholes.
Suitable consolidation strategy is necessary
Flaw : degradation of system throughput upto
64%.
Multi-machine Benchmarks on HadoopIncrease in provider’s profit of about 135% and
118% on ROI for WordCount and StreamSort respectively.
Degradation of system throughput with a reduction of 12% and 350%
Social FairnessCoefficient of variation , cv= stdev -------------- X 100% mean
Maximum Difference = Hi- Lo
------------- X 100% Lo
Variations of different runs on the same instances in Amazon EC2
Each single machine benchmark is run ten timesAs more VM’s are consolidated onto the same physical
machine users need to pay more money.
Postmark incurs 40% more cost than its best case
Cost of running Postmark ten times on three different instances on EC2
Different Hardware ConfigurationsElapsed times of Postmark are 180 and 400
seconds on SSD and hard disk respectively.SSD reduces user’s cost by 120% and
decreases provider’s ROI from 40% to -44%
FailuresExecuting Hadoop in Spring was successful
but resulted in one exception with a message “ Address already in use “ on Amazon EC2.
Transient failures also occur. Running StreamSort using Hadoop on eight VMs in Spring, resulted in a eight time increase in the total elapsed time.
All these could lead to higher user costs
ConclusionCloud computing bridges distributed systems
and economics by using a pricing scheme that connects providers with users.
Experiments conducted on Amazon EC2 and spring have shown that cost variations on both result in social unfairness of the current pricing scheme
Setting that achieves minimum cost differ from that of the best performance.
Providers need to fine-tune its pricing structure to balance between their profits and the users.