Virtual Machine Management for Efficient Cloud Data Centers with Applications to Big Data Analytics

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School of Science | Doctoral thesis (article-based) | Defence date: 2016-08-31
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Date

2016

Department

Tietotekniikan laitos
Department of Computer Science

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Mcode

Degree programme

Language

en

Pages

154

Series

Aalto University publication series DOCTORAL DISSERTATIONS, 140/2016

Abstract

Infrastructure-as-a-Service (IaaS) cloud data centers offer computing resources in the form of virtual machine (VM) instances as a service over the Internet. This allows cloud users to lease and manage computing resources based on the pay-as-you-go model. In such a scenario, the cloud users run their applications on the most appropriate VM instances and pay for the actual resources that are used. To support the growing service demands of end users, cloud providers are now building an increasing number of large-scale IaaS cloud data centers, consisting of many thousands of heterogeneous servers. The ever increasing heterogeneity of both servers and VMs requires efficient management to balance the load in the data centers and, more importantly, to reduce the energy consumption due to underutilized physical servers. To achieve these goals, the key aspect is to eliminate inefficiencies while using computing resources. This dissertation investigates the VM management problem for efficient IaaS cloud data centers. In particular, it considers VM placement and VM consolidation to achieve effective load balancing and energy efficiency in cloud infrastructures. VM placement allows cloud providers to allocate a set of requested or migrating VMs onto physical servers with the goal to balance the load or minimize the number of active servers. While addressing the VM placement problem is important, VM consolidation is even more important to enable continuous reorganization of already-placed VMs on the least number of servers. It helps create idle servers during periods of low resource utilization by taking advantage of live VM migration provided by virtualization technologies. Energy consumption is then reduced by dynamically switching idle servers into a power saving state. As VM migrations and server switches consume additional energy, the frequency of VM migrations and server switches needs to be limited as well. This dissertation concludes with a sample application of distributed computing to big data analytics.

Description

Supervising professor

Di Francesco, Mario, Assistant Prof., Aalto University, Department of Computer Science, Finland

Thesis advisor

Di Francesco, Mario, Assistant Prof., Aalto University, Department of Computer Science, Finland

Keywords

Virtual Machine (VM) consolidation, VM placement, VM migration, multiple resource prediction, data centers, cloud computing, big data analytics

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Parts

  • [Publication 1]: Nguyen Trung Hieu, Mario Di Francesco and Antti Ylä-Jääski. A Virtual Machine Placement Algorithm for Balanced Resource Utilization in Cloud Data Centers. In Proceedings of the 7th IEEE International Conference on Cloud Computing (CLOUD), Anchorage, Alaska, USA, pages 474-481.
    DOI: 10.1109/CLOUD.2014.70, 27 June - 2 July 2014. View at publisher
  • [Publication 2]: Nguyen Trung Hieu, Mario Di Francesco and Antti Ylä-Jääski. A Multi–Resource Selection Scheme for Virtual Machine Consolidation in Cloud Data Centers. In Proceedings of the 6th IEEE International Conference on Cloud Computing Technology and Science (CloudCom), Singapore, pages 234-239.
    DOI: 10.1109/CloudCom.2014.130, September 15-18 2014. View at publisher
  • [Publication 3]: Nguyen Trung Hieu, Mario Di Francesco and Antti Ylä-Jääski. Virtual Machine Consolidation with Usage Prediction for Energy–Efficient Cloud Data Centers. In Proceedings of the 8th IEEE International Conference on Cloud Computing (CLOUD), New York, USA, pages 750-757.
    DOI: 10.1109/Cloud.2015.104, 27 June - 2 July 2015. View at publisher
  • [Publication 4]: Nguyen Trung Hieu, Mario Di Francesco and Antti Ylä-Jääski. Virtual Machine Consolidation with Multiple Usage Prediction for Energy–Efficient Cloud Data Centers. IEEE Transactions on Services Computing, Under review, 14 pages, March 2016.
  • [Publication 5]: Nguyen Trung Hieu, Mario Di Francesco and Antti Ylä-Jääski. Extracting Knowledge from Wikipedia Articles through Distributed Semantic Analysis. In Proceedings of the 13th ACM International Conference on Knowledge Management and Knowledge Technologies (i-KNOW), Graz, Austria, pages 188-195.
    DOI: 10.1145/2494188.2494195, September 04-06 2013. View at publisher

Citation

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https://urn.fi/URN:ISBN:978-952-60-6912-8

Collections

[diss] Perustieteiden korkeakoulu / SCI