A Cost Model for IaaS Clouds Based on Virtual Machine Energy Consumption

Cloud Computing has revolutionized the software, platform and infrastructure provisioning. Infrastructure-as-a-Service (IaaS) providers offer on-demand and configurable Virtual Machine (VMs) to tenants of cloud computing services. A key consolidation force that widespread IaaS deployment is the use of pay-as-you-go and pay-as-you-use cost models. In these models, a service price can be composed of two dimensions: the individual consumption, and a proportional value charged for service maintenance. A common practice for public providers is to dilute both capital and operational costs on predefined pricing sheets. In this context, we propose PSVE (Proportional-Shared Virtual Energy), a cost model for IaaS providers based on CPU energy consumption. Aligned with traditional commodity prices, PSVE is composed of two key elements: an individualized cost accounted from CPU usage of VMs (e.g., processing and networking), and a shared cost from common hypervisor management operations, proportionally distributed among VMs.

[1]  Victor Avelar,et al.  PUE™: A COMPREHENSIVE EXAMINATION OF THE METRIC , 2012 .

[2]  Saira Begum,et al.  Potential of cloud computing architecture , 2011, 2011 International Conference on Information and Communication Technologies.

[3]  Djamshid Tavangarian,et al.  Power consumption estimation of CPU and peripheral components in virtual machines , 2013, SIAP.

[4]  Imtiaz Ahmad,et al.  Cloud Computing Pricing Models: A Survey , 2013 .

[5]  Nick McKeown,et al.  Virtualized Congestion Control , 2016, SIGCOMM.

[6]  Thomas F. Wenisch,et al.  PowerNap: eliminating server idle power , 2009, ASPLOS.

[7]  Andrés García-García,et al.  Cloud Services Representation using SLA Composition , 2014, Journal of Grid Computing.

[8]  Alexander Schill,et al.  Power Consumption Estimation Models for Processors, Virtual Machines, and Servers , 2014, IEEE Transactions on Parallel and Distributed Systems.

[9]  Dimitrios Katsaros,et al.  Architectural Requirements for Cloud Computing Systems: An Enterprise Cloud Approach , 2011, Journal of Grid Computing.

[10]  Robert P. Goldberg,et al.  Formal requirements for virtualizable third generation architectures , 1973, SOSP 1973.

[11]  Yan Han,et al.  Cloud Computing: Case Studies and Total Cost of Ownership , 2011 .

[12]  Jan Broeckhove,et al.  Cost-Optimal Scheduling in Hybrid IaaS Clouds for Deadline Constrained Workloads , 2010, 2010 IEEE 3rd International Conference on Cloud Computing.

[13]  Bu-Sung Lee,et al.  Long-term resource fairness: towards economic fairness on pay-as-you-use computing systems , 2014, ICS '14.

[14]  Norman P. Jouppi,et al.  System-level integrated server architectures for scale-out datacenters , 2011, 2011 44th Annual IEEE/ACM International Symposium on Microarchitecture (MICRO).

[15]  Yonggang Wen,et al.  Data Center Energy Consumption Modeling: A Survey , 2016, IEEE Communications Surveys & Tutorials.

[16]  Junaid Shuja,et al.  Data center energy efficient resource scheduling , 2014, Cluster Computing.

[17]  Inderveer Chana,et al.  A Survey on Resource Scheduling in Cloud Computing: Issues and Challenges , 2016, Journal of Grid Computing.

[18]  Roger Schmidt,et al.  Reducing energy usage in data centers through control of Room Air Conditioning units , 2010, 2010 12th IEEE Intersociety Conference on Thermal and Thermomechanical Phenomena in Electronic Systems.

[19]  Paul Rodrigues,et al.  State-of-the-art cloud computing security taxonomies: a classification of security challenges in the present cloud computing environment , 2012, ICACCI '12.

[20]  Guilherme Piegas Koslovski,et al.  EAVIRA: Energy-Aware Virtual Infrastructure Reallocation Algorithm , 2017, 2017 VII Brazilian Symposium on Computing Systems Engineering (SBESC).

[21]  Inderveer Chana,et al.  Energy-aware Virtual Machine Migration for Cloud Computing - A Firefly Optimization Approach , 2016, Journal of Grid Computing.

[22]  Francis C. M. Lau,et al.  vScale: automatic and efficient processor scaling for SMP virtual machines , 2016, EuroSys.

[23]  Guilherme Piegas Koslovski,et al.  Exploring the virtual infrastructure service concept in Grid'5000 , 2009 .

[24]  Erich Schikuta,et al.  Toward an economic and energy‐aware cloud cost model , 2013, Concurr. Comput. Pract. Exp..

[25]  K. Djemame,et al.  Energy Consumption-based Pricing Model for Cloud Computing , 2016 .

[26]  P. Mell,et al.  The NIST Definition of Cloud Computing , 2011 .

[27]  Luiz André Barroso,et al.  The Case for Energy-Proportional Computing , 2007, Computer.

[28]  Feng Zhao,et al.  Energy aware consolidation for cloud computing , 2008, CLUSTER 2008.

[29]  Lorenz M. Hilty,et al.  Energy Consumed vs. Energy Saved by ICT - A Closer Look , 2009, EnviroInfo.

[30]  Keqiang He,et al.  AC/DC TCP: Virtual Congestion Control Enforcement for Datacenter Networks , 2016, SIGCOMM.

[31]  Bob Gill,et al.  Magic Quadrant for Cloud Infrastructure as a Service , Worldwide 03 , 2016 .

[32]  Roger R. Schmidt,et al.  Impact of ASHRAE environmental classes on data centers , 2014, Fourteenth Intersociety Conference on Thermal and Thermomechanical Phenomena in Electronic Systems (ITherm).

[33]  József Dániel Dombi,et al.  A Pliant-based Virtual Machine Scheduling Solution to Improve the Energy Efficiency of IaaS Clouds , 2015, Journal of Grid Computing.

[34]  Athanasios V. Vasilakos,et al.  Cloud Computing , 2014, ACM Comput. Surv..

[35]  Sakshi Kaushal,et al.  Pricing Models in Cloud Computing , 2014, ICTCS '14.

[36]  Jerome A. Rolia,et al.  Resource and virtualization costs up in the cloud: Models and design choices , 2011, 2011 IEEE/IFIP 41st International Conference on Dependable Systems & Networks (DSN).

[37]  Saurabh Kumar,et al.  Energy Efficient Utilization of Resources in Cloud Computing Systems , 2016 .

[38]  Christine Morin,et al.  Energy Management in IaaS Clouds: A Holistic Approach , 2012, 2012 IEEE Fifth International Conference on Cloud Computing.

[39]  Athanasios V. Vasilakos,et al.  Mobile Cloud Computing: A Survey, State of Art and Future Directions , 2013, Mobile Networks and Applications.

[40]  Luís Veiga,et al.  Energy Efficient Cloud Service Provisioning: Keeping Data Center Granularity in Perspective , 2015, Journal of Grid Computing.

[41]  J. Koomey Worldwide electricity used in data centers , 2008 .

[42]  Sakshi Kaushal,et al.  Cost-Time Efficient Scheduling Plan for Executing Workflows in the Cloud , 2015, Journal of Grid Computing.

[43]  Ian T. Foster,et al.  The Anatomy of the Grid: Enabling Scalable Virtual Organizations , 2001, Int. J. High Perform. Comput. Appl..

[44]  Raouf Boutaba,et al.  Cloud computing: state-of-the-art and research challenges , 2010, Journal of Internet Services and Applications.

[45]  Rajkumar Buyya,et al.  Energy Efficient Resource Management in Virtualized Cloud Data Centers , 2010, 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing.

[46]  Inderveer Chana,et al.  Strategy-proof Pricing Approach for Cloud Market , 2015, ArXiv.