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OpenStackEmu - A Cloud Testbed Combining Network Emulation with OpenStack and SDN
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: 0000-0001-7734-1653
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
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: 0000-0001-9866-8209
Karlstads universitet, Fakulteten för hälsa, natur- och teknikvetenskap (from 2013), Institutionen för matematik och datavetenskap (from 2013).ORCID-id: 0000-0002-9446-8143
2017 (engelsk)Inngår i: Consumer Communications & Networking Conference (CCNC), 2017 14th IEEE Annual, IEEE, 2017, s. 566-568Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

OpenStack has been widely acknowledged to be one of the most important open source cloud platforms. In order to perform experimentally driven research in the area of cloud and cloud networking, there is however a big gap, because most researchers do not have access to a large cloud deployment and cannot change networking or compute infrastructure in order to test their algorithms and protocols on a large-scale. We developed OpenStackEmu, which is to the best of our knowledge the first attempt that combines OpenStack infrastructure with a Software Defined Networking (SDN) based controller such as OpenDaylight and a large-scale network emulator CORE (Common Open Research Emulator). The OpenStack compute and control nodes are connected to the CORE emulation server using TUN/TAP interfaces that inject the control (e.g. for VM migration) and data (VM-to-VM traffic) packets into a customizable network topology that is emulated using configurable Open vSwitches using CORE emulator. Experimenters can define e.g. fat-tree or distributed data center topologies and study the behavior of real VMs and services in those VMs under different background loads and SDN routing policies. We integrated the data center traffic generator DCT2Gen that allows to generate realistic background traffic based on traces from real data centers. Experimenters can study the performance impact of different VM migration strategies or different routing and load balancing schemes on real VM and application performance using different emulated topologies. We believe that OpenStackEmu is an important tool for both the SDN and OpenStack community in order to evaluate the performance of novel algorithms and protocols in the area of cloud networking.

sted, utgiver, år, opplag, sider
IEEE, 2017. s. 566-568
Serie
IEEE Consumer Communications and Networking Conference, ISSN 2331-9852
HSV kategori
Forskningsprogram
Datavetenskap
Identifikatorer
URN: urn:nbn:se:kau:diva-48478DOI: 10.1109/CCNC.2017.7983169ISI: 000412117100118ISBN: 978-1-5090-6196-9 (tryckt)OAI: oai:DiVA.org:kau-48478DiVA, id: diva2:1092829
Konferanse
The 14th Annual IEEE Consumer Communications & Networking Conference (CCNC), 8-11 Jan. 2017, Las Vegas, USA
Prosjekter
HITSTilgjengelig fra: 2017-05-04 Laget: 2017-05-04 Sist oppdatert: 2019-12-12bibliografisk kontrollert
Inngår i avhandling
1. Cost- and Performance-Aware Resource Management in Cloud Infrastructures
Åpne denne publikasjonen i ny fane eller vindu >>Cost- and Performance-Aware Resource Management in Cloud Infrastructures
2017 (engelsk)Doktoravhandling, med artikler (Annet vitenskapelig)
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).

sted, utgiver, år, opplag, sider
Karlstad: Karlstads universitet, 2017. s. 252
Serie
Karlstad University Studies, ISSN 1403-8099 ; 2017:21
Emneord
Cloud Computing, OpenStack, Robust Optimization, Latency, Tabu Search, Resource Management, Resource Contention, QoS
HSV kategori
Forskningsprogram
Datavetenskap
Identifikatorer
urn:nbn:se:kau:diva-48482 (URN)978-91-7063-783-4 (ISBN)978-91-7063-784-1 (ISBN)
Disputas
2017-06-21, 21A342 (Eva Erikssonsalen), Universitetsgatan 2, 651 88 Karlstad, Karlstad, 10:30 (engelsk)
Opponent
Veileder
Prosjekter
HITS, 4707
Forskningsfinansiär
Knowledge Foundation
Merknad

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

Tilgjengelig fra: 2017-05-19 Laget: 2017-05-04 Sist oppdatert: 2019-11-07bibliografisk kontrollert
2. Service Migration in Virtualized Data Centers
Åpne denne publikasjonen i ny fane eller vindu >>Service Migration in Virtualized Data Centers
2020 (engelsk)Doktoravhandling, med artikler (Annet vitenskapelig)
Abstract [en]

Modern virtualized Data Centers (DCs) require efficient management techniques to guarantee high quality services while reducing their economical cost. The ability to live migrate virtual instances, e.g., Virtual Machines (VMs), both inside and among DCs is a key operation for the majority of DC management tasks that brings significant flexibility into the DC infrastructure. However, live migration introduces new challenges as it ought to be fast and seamless while at the same time imposing a minimum overhead on the network. In this thesis, we study the networking problems of live service migration in modern DCs when services are deployed in virtualized environments, e.g., VMs and containers. In particular, this thesis has the following main objectives: (1) improving the live VM migration in Software-Defined Network (SDN) enabled DCs by addressing networking challenges of live VM migration, and (2) investigating the trade-off between the reconfiguration cost and optimality of the Service Function Chains (SFCs) placement after the reconfiguration has been applied when SFCs are composed of stateful Virtual Network Functions (VNFs).

To achieve the first objective, in this thesis, we use distinctive characteristics of SDN architectures such as their centralized control over the network to accelerate the network convergence time and address suboptimal routing problem. Consequently, we enhance the quality of intra- and inter-DC live migrations. Furthermore, we develop an SDN-based framework to improve the inter-DC live VM migration by automating the deployment, improving the management, enhancing the performance, and increasing the scalability of interconnections among DCs.

To accomplish the second objective, we investigate the overhead of dynamic reconfiguration of stateful VNFs. Dynamic reconfiguration of VNFs is frequently required in various circumstances, and live migration of VNFs is an integral part of this operation. By mathematically formulating the reconfiguration costs of stateful VNFs and developing a multi-objective heuristic solution, we explore the trade-off between the reconfiguration cost required to improve a given placement and the degree of optimality achieved after the reconfiguration is performed. Results show that the cost of performing the reconfiguration operations required to realize an optimal VNF placement might hamper the gain that could be achieved.

Abstract [en]

Modern virtualized Data Centers (DCs) require efficient management techniques to guarantee high quality services while reducing their economical cost. The ability to live migrate virtual instances, e.g., Virtual Machines (VMs), both inside and among DCs, is a key operation for the majority of DC management tasks that brings significant flexibility into the DC infrastructure. However, live migration introduces new challenges as it ought to be fast and seamless while at the same time imposing a minimum overhead on the network.

This thesis investigates the networking challenges of short and long-haul live VM migration in Software Defined Networking (SDN) enabled DCs. We propose solutions to make the intra- and inter-DC live VM migration more seamless. Our proposed SDN-based framework for inter-DC migration improves the management, enhances the performance, and increases the scalability of interconnections among DCs.

Moreover, by considering the overhead of VM migration over the network, servers, and quality of service the VM provides, we explore the trade-off between the costs required to change the placement of VMs and the optimality degree of the placement in the DC. Results show that the cost of improving the placement might hamper the gain that could be achieved.

sted, utgiver, år, opplag, sider
Karlstad: Karlstads universitet, 2020. s. 49
Serie
Karlstad University Studies, ISSN 1403-8099 ; 2020:1
Emneord
Data Center, Ethernet VPN, EVPN, Live Service Migration, Reconfiguration, SDN, Virtual Network Function, VNF
HSV kategori
Forskningsprogram
Datavetenskap
Identifikatorer
urn:nbn:se:kau:diva-75921 (URN)978-91-7867-073-4 (ISBN)978-91-7867-083-3 (ISBN)
Disputas
2020-02-07, 21A342 (Eva Eriksson lecture hall), Universitetsgatan 2, 651 88, Karlstad, 10:15 (engelsk)
Opponent
Veileder
Forskningsfinansiär
Knowledge Foundation
Tilgjengelig fra: 2020-01-16 Laget: 2019-12-12 Sist oppdatert: 2020-01-16bibliografisk kontrollert

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