Cloud Flat Rates Enabled via Fair Multi-resource Consumption

Many companies rent Virtual Machines VM from cloud providers to meet their computational needs. While this option is also available to end-users, they do not always take advantage of this option. One reason may be that it is common to pay on a per-VM-basis, whereas the telecommunications sector has shown that customers prefer flat rates. A flat rate for cloud services needs to define utilization thresholds, to cap the usage of heavy customers and thereby limit their impact on the flat rate price and the cloud performance. Unfortunately, customers consume multiple heterogenous resources in clouds, e.g., CPU, RAM, disk I/O and space, or network access. This makes the definition of a customer's fair "cloud share" and according utilization thresholds complex. Backed by a questionnaire among more than 600 individuals, this paper designs the new Greediness Metric GM that formalizes an intuitive understanding of multi-resource fairness without access to consumers' utility functions. This GM enables the introduction of attractive cloud flat rates and fair sharing policies for private/commodity clouds and provides incentive to customers to wisely determine VM configurations.

[1]  Thomas Bonald,et al.  Enhanced cluster computing performance through proportional fairness , 2014, Perform. Evaluation.

[2]  Rajkumar Buyya,et al.  CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms , 2011, Softw. Pract. Exp..

[3]  Ashutosh Sabharwal,et al.  An Axiomatic Theory of Fairness in Network Resource Allocation , 2009, 2010 Proceedings IEEE INFOCOM.

[4]  Dalibor Klusácek,et al.  Multi Resource Fairness: Problems and Challenges , 2013, JSSPP.

[5]  Nathan Linial,et al.  No justified complaints: on fair sharing of multiple resources , 2011, ITCS '12.

[6]  Benjamin C. Lee,et al.  REF: resource elasticity fairness with sharing incentives for multiprocessors , 2014, ASPLOS.

[7]  Sally Floyd,et al.  Metrics for the Evaluation of Congestion Control Mechanisms , 2008, RFC.

[8]  Benjamin Hindman,et al.  Dominant Resource Fairness: Fair Allocation of Heterogeneous Resources in Datacenters , 2010 .

[9]  Ariel D. Procaccia,et al.  Beyond Dominant Resource Fairness , 2015, ACM Trans. Economics and Comput..

[10]  Naixue Xiong,et al.  A game-theoretic method of fair resource allocation for cloud computing services , 2010, The Journal of Supercomputing.

[11]  M. Zukerman,et al.  Efficiency-fairness tradeoff in telecommunications networks , 2005, IEEE Communications Letters.

[12]  B. Skiera,et al.  Paying Too Much and Being Happy about It: Existence, Causes, and Consequences of Tariff-Choice Biases , 2006 .

[13]  A. Odlyzko The History of Communications and its Implications for the Internet , 2000 .

[14]  Andrew Odlyzko,et al.  Too expensive to meter: the influence of transaction costs in transportation and communication , 2008, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[15]  Dror G. Feitelson,et al.  On-line fair allocations based on bottlenecks and global priorities , 2013, ICPE '13.

[16]  Benjamin Hindman,et al.  Dominant Resource Fairness: Fair Allocation of Multiple Resource Types , 2011, NSDI.

[17]  Anja Strunk Costs of Virtual Machine Live Migration: A Survey , 2012, 2012 IEEE Eighth World Congress on Services.

[18]  Noam Nisan,et al.  Fair allocation without trade , 2012, AAMAS.

[19]  Pravin Varaiya,et al.  Effects of pricing on Internet user behavior , 2001 .

[20]  Andrew M. Odlyzko,et al.  Internet Pricing and the History of Communications , 2001, Comput. Networks.

[21]  E. S. Pilli,et al.  Live virtual machine migration techniques: Survey and research challenges , 2013, 2013 3rd IEEE International Advance Computing Conference (IACC).

[22]  Prashant J. Shenoy,et al.  Surplus fair scheduling: a proportional-share CPU scheduling algorithm for symmetric multiprocessors , 2000, OSDI.

[23]  Raj Jain,et al.  A Quantitative Measure Of Fairness And Discrimination For Resource Allocation In Shared Computer Systems , 1998, ArXiv.

[24]  Gagan Goel,et al.  Mechanism design for fair division: allocating divisible items without payments , 2013, EC.

[25]  Randy H. Katz,et al.  Heterogeneity-Aware Resource Allocation and Scheduling in the Cloud , 2011, HotCloud.

[26]  Dror G. Feitelson,et al.  A global scheduling framework for virtualization environments , 2009, 2009 IEEE International Symposium on Parallel & Distributed Processing.

[27]  Mung Chiang,et al.  Multiresource allocation: fairness-efficiency tradeoffs in a unifying framework , 2013, TNET.

[28]  Cody Bunch,et al.  OpenStack Cloud Computing Cookbook , 2012 .

[29]  Chris Arney Mathematics and Democracy: The Case for Quantitative Literacy , 2002 .