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A Robust Tabu Search Heuristic for VM Consolidation under Demand Uncertainity in Virtualized Datacenters
Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science. (Distributed Systems and Communications Research Group (DISCO))ORCID iD: 0000-0002-6221-3875
Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science.ORCID iD: 0000-0002-9446-8143
2017 (English)Conference paper, Published paper (Refereed)
Abstract [en]

In virtualized datacenters (vDCs), dynamic consolidation of virtual machines (VMs) is used as one of the most common techniques to achieve both energy- and resource- utilization efficiency. Live migrations of VMs are used for dynamic consolidation but due to dynamic resource demand variation of VMs may lead to frequent and non-optimal migrations. Assuming deterministic workload of the VMs may ensure the most energy/resource efficient VM allocations but eventually may lead to significant resource contention or under-utilization if the workload varies significantly over time. On the other hand, adopting a conservative approach by allocating VMs depending on their peak demand may lead to low utilization, if the peak occurs infrequently or for a short period of time. Therefore, in this work we design a robust VM migration scheme that strikes a balance between protection for resource contention and additional energy costs due to powering on more servers while considering uncertainties on VMs resource demands. We use the theory of Gamma-robustness and derive a robust Mixed Integer Linear programming (MILP) formulation. Due to the complexity, the problem is hard to solve for online optimization and we propose a novel heuristic based on Tabu search. Using several scenarios, we show that that the proposed heuristic can achieve near optimal solution qualities in a short time and scales well with the instance sizes. Moreover, we quantitatively analyze the trade-off between energy cost versus protection level and robustness.

Place, publisher, year, edition, pages
IEEE, 2017. 170-180 p.
National Category
Communication Systems
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:kau:diva-48479DOI: 10.1109/CCGRID.2017.35ISBN: 978-1-5090-6610-0 (print)OAI: oai:DiVA.org:kau-48479DiVA: diva2:1092835
Conference
17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid 2017)
Projects
READY
Funder
Knowledge Foundation
Note

The paper is publihsed online now: http://dl.acm.org/citation.cfm?id=3101134

Available from: 2017-05-04 Created: 2017-05-04 Last updated: 2017-08-07
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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