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Cost-Efficient Resource Scheduling under QoS Constraints for Geo-Distributed Data Centers
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
(University of Missouri--Kansas City, USA.)
Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science.
(University of Missouri--Kansas City, USA.)
(English)Manuscript (preprint) (Other academic)
Abstract [en]

Geo-distributed Data Centers (DCs) are more and more common in order to provide scalability for the ever increasing compute demands of modern applications. When multiple distributed DCs can serve user requests, it is important to determine, which DC and server to select to fulfil the compute request, given that enough resources are available in terms of CPU and bandwidth. The problem is complicated as every DC has different operational costs associated, such as energy costs, carbon emission cost and bandwidth costs. In this paper, we develop a novel mathematical optimization model that guides the decision maker which DC to select, which server to use to host the compute demands and which DC gateway and network path to use to route the network traffic in order to satisfy the compute, bandwidth and latency demands. Our model includes the queuing delay depending on the traffic load in the model. Our extensive numerical evaluation based on real-world DC locations, demand patterns and resource provision costs shows how operational cost increases with traffic load, and we analyse the impact of different latency bounds on the performance of different virtual networks.

National Category
Communication Systems
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:kau:diva-48481OAI: oai:DiVA.org:kau-48481DiVA: diva2:1092851
Projects
READY
Funder
Knowledge Foundation
Available from: 2017-05-04 Created: 2017-05-04 Last updated: 2017-05-10
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: 2017-05-19Bibliographically approved

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