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Optimizing Virtual Machine Consolidation in Virtualized Datacenters Using Resource Sensitivity
Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science. (DISCO[48]Network Centric Performance Improvement for Live VM Migration)ORCID iD: 0000-0002-6221-3875
Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science. (DISCO)ORCID iD: 0000-0001-9194-010X
Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science. (DISCO)ORCID iD: 0000-0002-9446-8143
2016 (English)In: Cloud Computing Technology and Science (CloudCom), 2016 IEEE International Conference on, IEEE conference proceedings, 2016, 168-175 p.Conference paper, Published paper (Refereed)
Abstract [en]

In virtualized datacenters (vDCs), dynamic consolidation of virtual machines (VMs) is used to achieve both energy-efficiency and load balancing among different physical machines (PMs). Using VM live migrations, we can consolidate VMs on a smaller number of hosts to power down unused PMs and save energy. Most migration schemes are however oblivious to the characteristics of services that run inside VMs, and thus may lead to migrations where VMs competing for the same resource type are packed on the same PM. As a result, VMs may suffer from significant resource contention and noticeable degradation in their performance. Using resource sensitivity values of VMs (ie, quantitative measures to reflect how much a VM is sensitive to its requested resources such as CPU, Mem, and Disk), we have designed a novel VM consolidation approach to optimize placement of VMs on available PMs. We validated our approach using five well-known applications/benchmarks with various resource demand signatures: varying from pure CPU/Mem/Disk-intensive to mixtures of them. Our extensive numerical evaluation illustrates that, for the same power consumption, our approach improve the performance of cloud services by 9 - 12\%, on average, when compared with current sensitivity oblivious approaches.

Place, publisher, year, edition, pages
IEEE conference proceedings, 2016. 168-175 p.
Keyword [en]
Virtualized Datacenters; VM live migration; Optimization; Resource Contention; VM co-location
National Category
Communication Systems
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:kau:diva-45863DOI: 10.1109/CloudCom.2016.36ISI: 000398536300023ISBN: 978-1-5090-1445-3 (print)OAI: oai:DiVA.org:kau-45863DiVA: diva2:970454
Conference
8th IEEE International Conference on Cloud Computing Technology and Science (cloudCom2016), Luxembourg, 12-15 Dec. 2016
Available from: 2016-09-13 Created: 2016-09-13 Last updated: 2017-08-08Bibliographically approved
In thesis
1. Cost- and Performance-Aware Resource Management in Cloud Infrastructures
Open this publication in new window or tab >>Cost- and Performance-Aware Resource Management in Cloud Infrastructures
2017 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

High availability, cost effectiveness and ease of application deployment have accelerated the adoption rate of cloud computing. This fast proliferation of cloud computing promotes the rapid development of large-scale infrastructures. However, large cloud datacenters (DCs) require infrastructure, design, deployment, scalability and reliability and need better management techniques to achieve sustainable design benefits. Resources inside cloud infrastructures often operate at low utilization, rarely exceeding 20-30%, which increases the operational cost significantly, especially due to energy consumption. To reduce operational cost without affecting quality of service (QoS) requirements, cloud applications should be allocated just enough resources to minimize their completion time or to maximize utilization. 

The focus of this thesis is to enable resource-efficient and performance-aware cloud infrastructures by addressing above mentioned cost and performance related challenges. In particular, we propose algorithms, techniques, and deployment strategies for improving the dynamic allocation of virtual machines (VMs) into physical machines (PMs). 

For minimizing the operational cost, we mainly focus on optimizing energy consumption of PMs by applying dynamic VM consolidation methods. To make VM consolidation techniques more efficient, we propose to utilize multiple paths to spread traffic and deploy recent queue management schemes which can maximize network resource utilization and reduce both downtime and migration time for live migration techniques. In addition, a dynamic resource allocation scheme is presented to distribute workloads among geographically dispersed DCs considering their location based time varying costs due to e.g. carbon emission or bandwidth provision. For optimizing performance level objectives, we focus on interference among applications contending in shared resources and propose a novel VM consolidation scheme considering sensitivity of the VMs to their demanded resources. Further, to investigate the impact of uncertain parameters on cloud resource allocation and applications’ QoS such as unpredictable variations in demand, we develop an optimization model based on the theory of robust optimization. Furthermore, in order to handle the scalability issues in the context of large scale infrastructures, a robust and fast Tabu Search algorithm is designed and evaluated.

Abstract [en]

High availability, cost effectiveness and ease of application deployment have accelerated the adoption rate of cloud computing. This fast proliferation of cloud computing promotes the rapid development of large-scale infrastructures. However, large cloud datacenters (DCs) require infrastructure, design, deployment, scalability and reliability and need better management techniques to achieve sustainable design benefits. Resources inside cloud infrastructures often operate at low utilization, rarely exceeding 20-30%, which increases the operational cost significantly, especially due to energy consumption. To reduce operational cost without affecting quality of service (QoS) requirements, cloud applications should be allocated just enough resources to minimize their completion time or to maximize utilization. 

The focus of this thesis is to enable resource-efficient and performance-aware cloud infrastructures by addressing above mentioned cost and performance related challenges. In particular, we propose algorithms, techniques, and deployment strategies for improving the dynamic allocation of virtual machines (VMs) into physical machines (PMs).

Place, publisher, year, edition, pages
Karlstad: Karlstads universitet, 2017. 252 p.
Series
Karlstad University Studies, ISSN 1403-8099 ; 2017:21
Keyword
Cloud Computing, OpenStack, Robust Optimization, Latency, Tabu Search, Resource Management, Resource Contention, QoS
National Category
Communication Systems
Research subject
Computer Science
Identifiers
urn:nbn:se:kau:diva-48482 (URN)978-91-7063-783-4 (ISBN)978-91-7063-784-1 (ISBN)
Public defence
2017-06-21, 21A342 (Eva Erikssonsalen), Universitetsgatan 2, 651 88 Karlstad, Karlstad, 10:30 (English)
Opponent
Supervisors
Available from: 2017-05-19 Created: 2017-05-04 Last updated: 2017-06-01Bibliographically approved

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