Distributed Systems Meet Economics: Pricing In The Cloud

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 optimizationStudies 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 computingtwo-party computation with pricing as the bridgePricing depends on two factorsSystem Design and OptimizationFairness and Competitive pricing

ContdPricing induced interplay between systems and economicsCost as an explicit and measurable system metricPricing fairnessEvolving system dynamicsCost of failuresExperiments 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

Background on PricingPricingPay-as-you-go model

PricingFairnessCompetitionPersonalSocialPay-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 serverFor experiment : File size 5 GB and number of transactions is 1000PARSEC (Princeton Application Repository for Shared Memory Computers)Benchmark suite for chip-multiprocessorsComposed of multithreaded programs

9 applications and 3 kernelsBlackscholes High performance computingDedup Storage archivalFor experiment: 184 MB input data for Dedup and 10 million options for Blackscholes

HadoopHadoop 0.20.0 for large scale data processingWordCount and StreamSortFor experiment: Input data set is 16 GB

MethodologiesAmazon EC2Charged according to the pricing scheme of Amazon Cost user = Price x tt : total running time of the task (Hours)Price : price per virtual machine hourExcluding storage and data transfer costsSpring SystemProvides virtual machines to the usersConsists of two modules VMM (Virtual machine monitor) and AuditorProvider Profit = Payment from users Total provider expense

Hamiltons 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 centerTotal provider cost = (Cost full + Cost amortized ) x Scale Scale = Estimated total cost -------------------------------- Cost full + Cost amortized Cost amortized = C amortizedUnit x t serverC 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 c1ucpu - CPU utilization(%)uio - I/O bandwidth (MB/sec)c0 and c1 - coefficients in the model

Experiment Setup Amazon EC22 virtual machine types Small and Medium instances

Experiment Setup SpringVirtual box is usedHost OS Windows Serer 2003 and guest OS is Fedora 10.

Eight core machine for evaluating single-machine benchmarksCluster consisting of 32 four-core machines for evaluating HadoopPower meter used for measuring power consumption of a serverTotal dollar cost is calculated based on Hamiltons 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 Providers investment ROI = Profit/Cost provider x 100 %

User Optimization on EC2Choosing suitable instance type is important for both performance and cost

Provider Optimization on SpringBased on varying the number of concurrent VMs 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 providers 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 providers profit of about 135% and 118% on ROI for WordCount and StreamSort respectively.Degradation of system throughput with a reduction of 12% and 350%

Pricing FairnessPersonal Fairness

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 EC2Each single machine benchmark is run ten timesAs more VMs 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 users cost by 120% and decreases providers 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 schemeSetting 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.

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