Towards Usage-Based Dynamic Overbooking in IaaS Clouds

IaaS Cloud systems enable the Cloud provider to overbook his data centre by selling more virtual resources than physical resources available. This approach works if on average the resource utilisation of a virtual machine is lower than the virtual machine boundaries. If this assumption is violated only locally, Cloud users will experience performance degradation and poor quality of service. This paper proposes the introduction of dynamic overbooking in the sense that the overbooking factors are not equal for all physical resources, but vary dynamically depending on the resource demands of the virtual resources they host. It allows new pricing models that are dependent on the overbooking a Cloud customer is willing to accept. Additionally, we discuss prerequisites for supporting its realisation in an OpenStack private Cloud, including a monitoring system, dedicated metrics to be monitored, as well as performance models that predict the performance degradation depending on the overbooking.

[1]  Johan Tordsson,et al.  The CACTOS Vision of Context-Aware Cloud Topology Optimization and Simulation , 2014, 2014 IEEE 6th International Conference on Cloud Computing Technology and Science.

[2]  Antonio Pescapè,et al.  Cloud monitoring: A survey , 2013, Comput. Networks.

[3]  Abhishek Chandra,et al.  Virtual putty , 2009, CloudCom 2009.

[4]  Hairong Kuang,et al.  The Hadoop Distributed File System , 2010, 2010 IEEE 26th Symposium on Mass Storage Systems and Technologies (MSST).

[5]  Prashant J. Shenoy,et al.  Resource overbooking and application profiling in a shared Internet hosting platform , 2009, TOIT.

[6]  Vyas Sekar,et al.  Towards verifiable resource accounting for outsourced computation , 2013, VEE '13.

[7]  Mario Macías,et al.  Resource-Level QoS Metric for CPU-Based Guarantees in Cloud Providers , 2010, GECON.

[8]  A. Kivity,et al.  kvm : the Linux Virtual Machine Monitor , 2007 .

[9]  Paul Reeser,et al.  Quantifying the performance impact of overbooking virtualized resources , 2012, 2012 IEEE International Conference on Communications (ICC).

[10]  Gang Sun,et al.  A new technique for efficient live migration of multiple virtual machines , 2016, Future Gener. Comput. Syst..

[11]  Xiaowei Li,et al.  EcoUp: Towards Economical Datacenter Upgrading , 2016, IEEE Transactions on Parallel and Distributed Systems.

[12]  Achim Streit,et al.  Energy-Aware Cloud Management Through Progressive SLA Specification , 2014, GECON.

[13]  P. Berndt,et al.  Towards Sustainable IaaS Pricing , 2013, GECON.

[14]  Janakiram Subramanian,et al.  Airline Yield Management with Overbooking, Cancellations, and No-Shows , 1999, Transp. Sci..

[15]  Marco Aurélio Stelmar Netto,et al.  Impact of user patience on auto-scaling resource capacity for cloud services , 2016, Future Gener. Comput. Syst..

[16]  Stefan Wesner,et al.  Storage Systems for I/O-Intensive Applications in Computational Chemistry , 2015 .

[17]  Eugenio Zimeo,et al.  Capacity-driven utility model for service level agreement negotiation of cloud services , 2016, Future Gener. Comput. Syst..

[18]  Johan Tordsson,et al.  Improving cloud infrastructure utilization through overbooking , 2013, CAC.

[19]  Jeanna Neefe Matthews,et al.  Quantifying the performance isolation properties of virtualization systems , 2007, ExpCS '07.

[20]  Christos Doulkeridis,et al.  A survey of large-scale analytical query processing in MapReduce , 2013, The VLDB Journal.

[21]  Tomasz Wiktor Wlodarczyk Overview of Time Series Storage and Processing in a Cloud Environment , 2012, 4th IEEE International Conference on Cloud Computing Technology and Science Proceedings.