Distributed Systems Meet Economics: Pricing In The Cloud Authors: Hongyi Wang, Qingfeng Jing, Rishan Chen, Bingsheng He, Zhengping He, Lidong Zhou Presenter: Sajala Rajendran

Abstract Pricing Scheme in cloud computing – bridge that decouples users from cloud providers Relationship 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

Introduction Pay-as-you-go model: Cloud Providers have a pricing scheme for their users. Users utilize cloud at a very low cost Profit for providers Variety of applications – storage backup, e- commerce, high performance computing “two-party” computation with pricing as the bridge Pricing depends on two factors System Design and Optimization Fairness and Competitive pricing

Contd… Pricing induced interplay between systems and economics Cost as an explicit and measurable system metric Pricing fairness Evolving system dynamics Cost of failures Experiments conducted on Amazon EC2 and Spring have the following results: Optimization for cost is hard for user Pricing unfairness Different system configuration significantly imapcts cost and profit Failure occurrences

Background on Pricing Pricing Pay-as-you-go model Pricing Fairness Competition Personal Social

Pay-as-you-go Model Pricing helps to shape how systems are used Amazon charges $0.095/virtual machine hour Many pricing schemes are introduced Several alternative pricing schemes have been proposed E.g. Gurmeet Singh and Carl Kesselman suggested dynamic pricing on resource consumption.

Workloads Postmark  I/O intensive benchmark  Measures transaction rates for a workload approximating an Internet server  For experiment : File size 5 GB and number of transactions is 1000 PARSEC (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 for large scale data processing  WordCount and StreamSort  For experiment: Input data set is 16 GB

Methodologies Amazon 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 costs Spring 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 Estimations Total cost of full burdened power consumption Cost full = p x P raw x PUE  p - Electricity price (dollars/KWh)  P raw - 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 P raw For a server, the energy consumption is calculated based on resource utilization P server = P idle + u cpu x c 0 + u io x c 1  u cpu - CPU utilization(%)  u io - I/O bandwidth (MB/sec)  c 0 and c 1 - coefficients in the model

Experiment Setup – Amazon EC2 2 virtual machine types – Small and Medium instances

Experiment Setup – Spring Virtual box is used Host OS – Windows Serer 2003 and guest OS is Fedora 10.

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 %

User Optimization on EC2 Choosing suitable instance type is important for both performance and cost

Provider Optimization on Spring Based on varying the number of concurrent VM’s from one to four on the same physical machine.

Observations Consolidation reduces power consumption of 150% and 21% on P raw 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 Hadoop Increase 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%

Pricing Fairness Personal Fairness

Social Fairness Coefficient 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 times As 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 Configurations Elapsed 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%

Failures Executing 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

Conclusion Cloud 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.

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