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Robust Optimization for Energy-Efficient Virtual Machine Consolidation in Modern Datacenters
Karlstads universitet, Fakulteten för hälsa, natur- och teknikvetenskap (from 2013), Institutionen för matematik och datavetenskap (from 2013). (Distributed Systems and Communications Research Group (DISCO))ORCID-id: 0000-0002-6221-3875
Universitat Politècnica de Catalunya (UPC), Spain..
Karlstads universitet, Fakulteten för hälsa, natur- och teknikvetenskap (from 2013), Institutionen för matematik och datavetenskap (from 2013).ORCID-id: 0000-0002-9446-8143
2018 (Engelska)Ingår i: Cluster Computing, ISSN 1386-7857, E-ISSN 1573-7543, Vol. 21, nr 3, s. 1681-1709Artikel i tidskrift (Refereegranskat) Published
Abstract [en]

Energy efficient Virtual Machine (VM) consolidation in modern data centers is typically optimized using methods such as Mixed Integer Programming, which typically require precise input to the model. Unfortunately, many parameters are uncertain or very difficult to predict precisely in the real world. As a consequence, a once calculated solution may be highly infeasible in practice. In this paper, we use methods from robust optimization theory in order to quantify the impact of uncertainty in modern data centers. We study the impact of different parameter uncertainties on the energy efficiency and overbooking ratios such as e.g. VM resource demands, migration related overhead or the power consumption model of the servers used. We also show that setting aside additional resource to cope with uncertainty of workload influences the overbooking ratio of the servers and the energy consumption. We show that, by using our model, Cloud operators can calculate a more robust migration schedule leading to higher total energy consumption. A more risky operator may well choose a more opportunistic schedule leading to lower energy consumption but also higher risk of SLA violation.

Ort, förlag, år, upplaga, sidor
Springer, 2018. Vol. 21, nr 3, s. 1681-1709
Nyckelord [en]
Virtual machine consolidation, Energy efficiency, Optimization model, Robust optimization, Cloud computing, Green Datacenter
Nationell ämneskategori
Kommunikationssystem
Forskningsämne
Datavetenskap
Identifikatorer
URN: urn:nbn:se:kau:diva-48480DOI: 10.1007/s10586-018-2718-6ISI: 000457275200014Scopus ID: s2.0-85046037105OAI: oai:DiVA.org:kau-48480DiVA, id: diva2:1092846
Projekt
READY
Forskningsfinansiär
KK-stiftelsen
Anmärkning

Artikeln ingick i avhandlingen som manuskript

Tillgänglig från: 2017-05-04 Skapad: 2017-05-04 Senast uppdaterad: 2019-10-28Bibliografiskt granskad
Ingår i avhandling
1. Cost- and Performance-Aware Resource Management in Cloud Infrastructures
Öppna denna publikation i ny flik eller fönster >>Cost- and Performance-Aware Resource Management in Cloud Infrastructures
2017 (Engelska)Doktorsavhandling, sammanläggning (Övrigt vetenskapligt)
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).

Ort, förlag, år, upplaga, sidor
Karlstad: Karlstads universitet, 2017. s. 252
Serie
Karlstad University Studies, ISSN 1403-8099 ; 2017:21
Nyckelord
Cloud Computing, OpenStack, Robust Optimization, Latency, Tabu Search, Resource Management, Resource Contention, QoS
Nationell ämneskategori
Kommunikationssystem
Forskningsämne
Datavetenskap
Identifikatorer
urn:nbn:se:kau:diva-48482 (URN)978-91-7063-783-4 (ISBN)978-91-7063-784-1 (ISBN)
Disputation
2017-06-21, 21A342 (Eva Erikssonsalen), Universitetsgatan 2, 651 88 Karlstad, Karlstad, 10:30 (Engelska)
Opponent
Handledare
Projekt
HITS, 4707
Forskningsfinansiär
KK-stiftelsen
Anmärkning

Paper 8 "Robust optimization for energy-efficient virtual machine consolidation in modern datacenters" ingick i avhandlingen som manuskript, nu publicerad. 

Paper 5 "Cost- and Performance-Aware Resource Management in Cloud Infrastructures" ingick i avhandlingen som manuskript nu konferensbidrag

Tillgänglig från: 2017-05-19 Skapad: 2017-05-04 Senast uppdaterad: 2019-11-07Bibliografiskt granskad

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