Proactive dynamic resource management in virtualized data centers

ABSTRACT

Dynamically reassigning virtual machines (VMs) to servers is a widely addressed idea to save energy in data centers. VMs are consolidated in times of low overall resource demand. Unused servers are switched off to save energy. Mainly two major challenges must be addressed to realize this approach. First, the resource demand of VMs expected in the future must be estimated to take care of delays caused by VM migrations and server startups. An upcoming resource shortage must have been resolved right before it actually occurs. Second, a scheduling algorithm is needed that, based on a current distribution of VMs to servers, can guarantee to find a sequence of operations that resolves any upcoming resource shortage right in time. Within this paper, we present a novel approach that addresses both of these challenges. In contrast to previous work, this approach can guarantee not to cause any resource shortages, if the actual resource demand of the VMs meets the expected one. We performed a simulation based evaluation with a set of VMs. The underlying resource demand time series were measured in a data center operated by a medium-sized IT service provider. A data center model was used to estimate the energy consumption. Overall energy savings of about 23% could be achieved compared to a static approach. Resource shortages occurred in less than 0.1% of time. They could be resolved by the approach in less then 20 minutes.

References

1. W. Vogels, Beyond Server Consolidation, ACM Queue, vol. 6, no. 1, Jan/Feb 2008, pp. 20--26.
2. IBM, Advanced POWER Virtualization on IBM System P5, http://www.redbooks.ibm.com/abstracts/sg247940.html.
3. P. R. Barham et al., Xen 2002, Technical Report UCAM-CL-TR-535, Jan 2003.
4. P. R. Barham et al., Xen and the Art of Virtualization, ACM Symposium on Operating Systems Principles (SOSP), Oct 2003.
5. VMware EMC Corporation, Virtualization Overview, http://www.vmware.com/pdf/virtualization.pdf.
6. G. Collier, Virtualization's Next Frontier: Security, ACM SIGUCCS, Oct 2007.
7. M. Hoyer et al., Statistical static capacity management in virtualized data centers supporting fine grained QoS specification, in Proceedings of the International Conference on Energy-Efficient Computing and Networking (e-Energy), 2010
8. B. Urgaonkar et al., Resource Overbooking and Application Profiling in Shared Hosting Platforms, Proceedings of the 5th symposium on Operating systems design and implementation (OSDI), Vol.36, 2002, pp. 239--254.
9. G. Even et al., Competitive and deterministic embeddings of virtual networks. in ArXiv Technical Report 1101.5221 (2011)
10. N. Bobroff et al., Dynamic Placement of Virtual Machines for Managing SLA Violations, in Symposium on Integrated Network Management, May 2007, pp. 119--128.
11. G. Khanna et al., Application Performance Management in Virtualized Server Environments, in Network Operations and Management Symposium (NOMS), Apr. 2006, pp. 373--381.
12. F. Hermenier et al., Entropy: a Consolidation Manager for Clusters, in Proceedings of the international conference on Virtual execution environments (VEE'09), Mar 2009
13. Christopher Clark et al., Live migration of virtual machines, in Proceedings of the 2nd Symposium on Networked Systems Design & Implementation (NSDI'05), 2005.
14. J. S. Chase et al., Managing energy and server resources in hosting centers, in Proceedings of the eighteenth ACM symposium on Operating systems principles (SOSP), 2001, pp. 103--116.
15. B. Lubin et al., Expressive power-based resource allocation for data centers, in Proceedings of the 21st international joint conference on Artifical intelligence (IJCAI'09), 2009
16. L. Bertini et al., Statistical QoS Guarantee and Energy-Efficiency in Web Server Clusters, in Proceedings of Euromicro Conference on Real-Time Systems (ECRTS'07), Jul 2007
17. V. Sharma et al., Power-aware QoS management in web servers, in 24th IEEE Real-Time Systems Symp., Dec 2003
18. VMware DRS, http://www.vmware.com/products/drs/
19. T. Wood et al, Black-box and Gray-box Strategies for Virtual Machine Migration, 4th USENIX Symposium on Networked Systems Design & Implementation (NSDI'07), 2007
20. D. Shen and J. L. Hellerstein, Predictive models for proactive network management: application to a production Web server, in Network Operations and Management Symposium (NOMS), 2000, pp. 833--846.
21. C. Hyser et al., Autonomic Virtual Machine Placement in the Data Center, Technical Reports HP, 2007
22. J. D. Cryer et al., Time Series Analysis With Applications in R, Springer, Second Edition, 2008
23. A. V. Aho and J. D. Ullman., Foundations of Computer Science: C Edition, chapter 9 The Graph Data Model, pages 484--497, W. H. Freemann, 1994
24. F. Belik, An efficient deadlock avoidance technique. IEEE Transactions on Computers, 39(7):882--888, 1990
25. W. Nebel et al., Untersuchung des Potentials von rechenzentrenübergreifendem Lastmanagement zur Reduzierung des Energieverbrauchs in der IKT, Survey, OFFIS, 2009.

Index Terms

• Computer systems organization

  • Dependable and fault-tolerant systems and networks

    • Availability

    • Maintainability and maintenance

    • Reliability

• General and reference

  • Cross-computing tools and techniques

    • Performance

    • Reliability

• Social and professional topics

  • Professional topics

    • Management of computing and information systems

      • Implementation management

        • Pricing and resource allocation

      • Information system economics

      • System management

        • Quality assurance

• Software and its engineering

  • Software organization and properties

    • Contextual software domains

      • Operating systems