1011121314151613 of 28
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Performance Implications of Virtualization
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.ORCID iD: 0000-0002-2974-3700
2019 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Virtualization is a component of cloud computing. Virtualization transforms traditional inflexible, complex infrastructure of individual servers, storage, and network hardware into a flexible virtual resource pool and increases IT agility, flexibility, and scalability while creating significant cost savings. Additional benefits of virtualization include, greater work mobility, increased performance and availability of resources, and automated operations. Many virtualization solutions have been implemented. There are plenty of cloud providers using different virtualization solutions to provide virtual machines (VMs) and containers, respectively. Various virtualization solutions have different performance overheads due to their various implementations of virtualization and supported features. A cloud user should understand performance overheads of different virtualization solutions and the impact on the performance caused by different virtualization features, so that it can choose appropriate virtualization solution, for the services to avoid degrading their quality of services (QoSs). In this research, we investigate the impacts of different virtualization technologies such as, container-based, and hypervisor-based virtualization as well as various virtualization features such as, over-allocation of resources, live migration, scalability, and distributed resource scheduling on the performance of various applications for instance, Cassandra NoSQL database, and a large telecommunication application. According to our results, hypervisor-based virtualization has many advantages and is more mature compare to the recently introduced container-based virtualization. However, impacts of the hypervisorbased virtualization on the performance of the applications is much higher than the container-based virtualization as well as the non-virtualized solution. The findings of this research should be of benefit to the ones who provide planning, designing, and implementing of the IT infrastructure.

Place, publisher, year, edition, pages
Karlskrona: Blekinge Tekniska Högskola, 2019. , p. 211
Series
Blekinge Institute of Technology Doctoral Dissertation Series, ISSN 1653-2090 ; 1
National Category
Computer Systems
Identifiers
URN: urn:nbn:se:bth-17217ISBN: 978-91-7295-361-1 (print)OAI: oai:DiVA.org:bth-17217DiVA, id: diva2:1260339
Public defence
2019-01-16, 13:00 (English)
Opponent
Supervisors
Available from: 2018-11-05 Created: 2018-11-02 Last updated: 2018-11-05Bibliographically approved
List of papers
1. Performance evaluation of distributed storage systems for cloud computing
Open this publication in new window or tab >>Performance evaluation of distributed storage systems for cloud computing
2013 (English)In: International Journal of Computers and Their Applications, ISSN 1076-5204, Vol. 20, no 4, p. 195-207Article in journal (Refereed) Published
Abstract [en]

The possibility to migrate a virtual server from one physical computer in a cloud to another physical computer in the same cloud is important in order to obtain a balanced load. In order to facilitate live migration of virtual servers, one needs to provide large shared storage systems that are accessible for all the physical servers that are used in the cloud. Distributed storage systems offer reliable and cost-effective storage of large amounts of data and such storage systems will be used in future Cloud Computing. We have evaluated four large distributed storage systems. Two of these use Distributed Hash Tables (DHTs) in order to keep track of how data is distributed, and two systems use multicasting to access the stored data. We measure the read/write/delete performance, as well as the recovery time when a storage node goes down. The evaluations are done on the same hardware, consisting of 24 storage nodes and a total storage capacity of 768 TB of data. These evaluations show that the multicast approach outperforms the DHT approach

Place, publisher, year, edition, pages
International Society for Computers and Their Applications (ISCA), 2013
Keywords
Cloud computing, Compuverde, Distributed storage system, File system, Gluster, OpenStack (Swift)
National Category
Software Engineering
Identifiers
urn:nbn:se:bth-6736 (URN)oai:bth.se:forskinfoAC72378778A2AB1BC1257CBA0034DD37 (Local ID)oai:bth.se:forskinfoAC72378778A2AB1BC1257CBA0034DD37 (Archive number)oai:bth.se:forskinfoAC72378778A2AB1BC1257CBA0034DD37 (OAI)
Available from: 2014-04-14 Created: 2014-04-14 Last updated: 2018-11-06Bibliographically approved
2. Performance Comparison of KVM, VMware and XenServer using a Large Telecommunication Application
Open this publication in new window or tab >>Performance Comparison of KVM, VMware and XenServer using a Large Telecommunication Application
2014 (English)Conference paper, Published paper (Refereed) Published
Abstract [en]

One of the most important technologies in cloud computing is virtualization. This paper presents the results from a performance comparison of three well-known virtualization hypervisors: KVM, VMware and XenServer. In this study, we measure performance in terms of CPU utilization, disk utilization and response time of a large industrial real-time application. The application is running inside a virtual machine (VM) controlled by the KVM, VMware and XenServer hypervisors, respectively. Furthermore, we compare the three hypervisors based on downtime and total migration time during live migration. The results show that the Xen hypervisor results in higher CPU utilization and thus also lower maximum performance compared to VMware and KVM. However, VMware causes more write operations to disk than KVM and Xen, and Xen causes less downtime than KVM and VMware during live migration. This means that no single hypervisor has the best performance for all aspects considered here.

Place, publisher, year, edition, pages
Venice, Italy: IARIA XPS Press, 2014
Keywords
Cloud Computing, KVM, Live Migration, VMware vMotion, XenMotion
National Category
Computer Sciences
Identifiers
urn:nbn:se:bth-6482 (URN)oai:bth.se:forskinfoC6FA88A0BAE3E5B5C1257DAA005E74D0 (Local ID)978-1-61208-338-4 (ISBN)oai:bth.se:forskinfoC6FA88A0BAE3E5B5C1257DAA005E74D0 (Archive number)oai:bth.se:forskinfoC6FA88A0BAE3E5B5C1257DAA005E74D0 (OAI)
Conference
Cloud Computing
Available from: 2014-12-11 Created: 2014-12-10 Last updated: 2018-11-06Bibliographically approved
3. Performance Implications of Over-allocation of Virtual CPUs
Open this publication in new window or tab >>Performance Implications of Over-allocation of Virtual CPUs
2015 (English)In: 2015 International Symposium on Networks, Computers and Communications (ISNCC 2015), IEEE , 2015Conference paper, Published paper (Refereed)
Abstract [en]

A major advantage of cloud environments is that one can balance the load by migrating virtual machines (VMs) from one server to another. High performance and high resource utilization are also important in a cloud. We have observed that over-allocation of virtual CPUs to VMs (i.e. allocating more vCPUs to VMs than there CPU cores on the server) when there are many VMs running on one host can reduce performance. However, if we do not use any over-allocation of virtual CPUs we may suffer from poor resource utilization after VM migration. Thus, it is important to identify and quantify performance bottlenecks when running in virtualized environment. The results of this study will help virtualized environment service providers to decide how many virtual CPUs should be allocated to each VM.

Place, publisher, year, edition, pages
IEEE, 2015
Keywords
virtualization, over-allocation, VMware, virtual CPUs
National Category
Computer Systems
Identifiers
urn:nbn:se:bth-14572 (URN)000380545000019 ()978-1-4673-7467-5 (ISBN)
Conference
2015 International Symposium on Networks, Computers and Communications (ISNCC 2015), MAY 13-15, 2015, Yasmine Hammamet, TUNISIA
Available from: 2017-06-19 Created: 2017-06-19 Last updated: 2018-11-06Bibliographically approved
4. Comparing Automatic Load Balancing using VMware DRS with a Human Expert
Open this publication in new window or tab >>Comparing Automatic Load Balancing using VMware DRS with a Human Expert
2016 (English)In: 2016 IEEE INTERNATIONAL CONFERENCE ON CLOUD ENGINEERING WORKSHOP (IC2EW), IEEE, 2016, p. 239-246Conference paper, Published paper (Refereed)
Abstract [en]

In recent years, there has been a rapid growth of interest in dynamic management of resources in virtualized systems. Virtualization provides great flexibility in terms of resource sharing but at the same time it also brings new challenges for load balancing using automatic migrations of virtual machines. In this paper, we have evaluated VMware's Distributed Resource Scheduler (DRS) in a number of realistic scenarios using multiple instances of a large industrial telecommunication application. We have measured the performance on the hosts before and after the migration in terms of CPU utilization, and compared DRS migrations with human expert migrations. According to our results, DRS with the most aggressive threshold gave us the best results. It could balance the load in 40% of cases while in other cases it could not balance the load properly. DRS did completely unnecessary migrations back and forth in some cases.

Place, publisher, year, edition, pages
IEEE, 2016
Keywords
Cloud Computing, Distributed Resource Scheduler (DRS), Virtual Machine Migration, Virtualization, VMware
National Category
Computer Sciences
Identifiers
urn:nbn:se:bth-13923 (URN)10.1109/IC2EW.2016.14 (DOI)000392269400047 ()978-1-5090-3684-4 (ISBN)
Conference
IEEE International Conference on Cloud Engineering (IC2E), APR 04-08, 2016, TU Berlin, Berlin, GERMANY
Available from: 2017-02-22 Created: 2017-02-22 Last updated: 2018-11-06Bibliographically approved
5. Performance implications of resource over-allocation during the live migration
Open this publication in new window or tab >>Performance implications of resource over-allocation during the live migration
2016 (English)In: 8TH IEEE INTERNATIONAL CONFERENCE ON CLOUD COMPUTING TECHNOLOGY AND SCIENCE (CLOUDCOM 2016), IEEE Computer Society, 2016, p. 552-557Conference paper, Published paper (Refereed)
Abstract [en]

As the number of cloud users are increasing, it becomes essential for cloud service providers to allocate the right amount of resources to virtual machines, especially during live migration. In order to increase the resource utilization and reduce waste, the providers have started to think about the role of over-allocating the resources. However, the benefits of over-allocations are not without inherent risks. In this paper, we conducted an experiment using a large telecommunication application that runs inside virtual machines, here we have varied the number of vCPU resources allocated to these virtual machines in order to find the best choice which at the same time reduces the risk of underallocating resources after the migration and increases the performance during the live migration. During our measurements we have used VMware's vMotion to migrate virtual machines while they are running. The results of this study will help virtualized environment service providers to decide how much resources should be allocated for better performance during live migration as well as how much resource would be required for a given load.

Place, publisher, year, edition, pages
IEEE Computer Society, 2016
Series
International Conference on Cloud Computing Technology and Science, ISSN 2330-2194
Keywords
live migration, over-allocation, performance, virtualization, vmware, Cloud computing, Network security, Virtual reality, Cloud service providers, Live migrations, Resource utilizations, Telecommunication applications, Virtualized environment, Virtual machine
National Category
Computer Sciences
Identifiers
urn:nbn:se:bth-13962 (URN)10.1109/CloudCom.2016.0096 (DOI)000398536300080 ()2-s2.0-85012981838 (Scopus ID)978-1-5090-1445-3 (ISBN)
Conference
8th IEEE International Conference on Cloud Computing Technology and Science, CloudCom, Luxembourg
Available from: 2017-03-02 Created: 2017-03-02 Last updated: 2018-11-06Bibliographically approved
6. Performance Evaluation of Container and Virtual Machine Running Cassandra Workload
Open this publication in new window or tab >>Performance Evaluation of Container and Virtual Machine Running Cassandra Workload
2017 (English)In: PROCEEDINGS OF 2017 3RD INTERNATIONAL CONFERENCE OF CLOUD COMPUTING TECHNOLOGIES AND APPLICATIONS (CLOUDTECH) / [ed] Essaaidi, M Zbakh, M, 2017, p. 24-31Conference paper, Published paper (Refereed)
Abstract [en]

Today, scalable and high-available NoSQL distributed databases are largely used as Big Data platforms. Such distributed databases typically run on a virtualized infrastructure that could be implemented using Hypervisorb ased virtualiz ation or Container-based virtualiz ation. Hypervisor-based virtualization is a mature technology but imposes overhead on CPU, memory, networking, and disk Recently, by sharing the operating system resources and simplifying the deployment of applications, container-based virtualization is getting more popular. Container-based virtualization is lightweight in resource consumption while also providing isolation. However, disadvantages are security issues and 110 performance. As a result, today these two technologies are competing to provide virtual instances for running big data platforms. Hence, a key issue becomes the assessment of the performance of those virtualization technologies while running distributed databases. This paper presents an extensive performance comparison between VMware and Docker container, while running Apache Cassandra as workload. Apache Cassandra is a leading NoSQL distributed database when it comes to Big Data platforms. As baseline for comparisons we used the Cassandra's performance when running on a physical infrastructure. Our study shows that Docker had lower overhead compared to the VMware when running Cassandra. In fact, the Cassandra's performance on the Dockerized infrastructure was as good as on the Non-Virtualized.

Keywords
Cassandra, Cloud computing, Containers, Docker, NoSQL databases, Virtual machine, VMware, Big Data, Performance evaluation
National Category
Computer Sciences
Identifiers
urn:nbn:se:bth-16000 (URN)000426451400004 ()978-1-5386-1115-9 (ISBN)
Conference
3rd International Conference of Cloud Computing Technologies and Applications (CloudTech), Rabat
Available from: 2018-03-23 Created: 2018-03-23 Last updated: 2018-11-06Bibliographically approved
7. Performance Comparison between Horizontal Scaling of Hypervisor and Container Based Virtualization using Cassandra NoSQL Database
Open this publication in new window or tab >>Performance Comparison between Horizontal Scaling of Hypervisor and Container Based Virtualization using Cassandra NoSQL Database
2018 (English)In: Proceeding of the 3rd International Conference on Virtualization Application and Technology, 2018, , p. 6Conference paper, Published paper (Refereed)
Abstract [en]

Cloud computing promises customers the ondemand ability to scale in face of workload variations. There are different ways to accomplish scaling, one is vertical scaling and the other is horizontal scaling. The vertical scaling refers to buying more power (CPU, RAM), buying a more expensive and robust server, which is less challenging to implement but exponentially expensive. While, the horizontal scaling refers to adding more servers with less processor and RAM, which is usually cheaper overall and can scale very well. The majority of cloud providers prefer the horizontal scaling approach, and for them would be very important to know about the advantages and disadvantages of both technologies from the perspective of the application performance at scale. In this paper, we compare performance differences caused by scaling of the different virtualization technologies in terms of CPU utilization, latency, and the number of transactions per second. The workload is Apache Cassandra, which is a leading NoSQL distributed database for Big Data platforms. Our results show that running multiple instances of the Cassandra database concurrently, affected the performance of read and write operations differently; for both VMware and Docker, the maximum number of read operations was reduced when we ran several instances concurrently, whereas the maximum number of write operations increased when we ran instances concurrently.

Publisher
p. 6
Keywords
Cassandra; Cloud computing; Docker container; Horizontal scaling; NoSQL database; Performance comparison; Virtualization; VMware virtual machine
National Category
Computer Systems
Identifiers
urn:nbn:se:bth-17212 (URN)
Conference
3rd International Conference on Virtualization Application and Technology (ICVAT 2018, Nov.16-18, Sanya, China
Available from: 2018-11-02 Created: 2018-11-02 Last updated: 2018-11-06Bibliographically approved
8. Scheduling Tasks with Hard Deadlines in CloudBased Virtualized Software Systems
Open this publication in new window or tab >>Scheduling Tasks with Hard Deadlines in CloudBased Virtualized Software Systems
(English)Manuscript (preprint) (Other academic)
Abstract [en]

There is scheduling on two levels in real-time applications executing in a virtualized environment: traditional real-time scheduling of the tasks in the realtime application, and scheduling of different Virtual Machines (VMs) on the hypervisor level. In this paper, we describe a technique for calculating a period and an execution time for a VM containing a real-time application with hard deadlines. This result makes it possible to apply existing real-time scheduling theory when scheduling VMs on the hypervisor level, thus making it possible to guarantee that the real-time tasks in a VM meet their deadlines. If overhead for switching from one VM to another is ignored, it turns out that (infinitely) short VM periods minimize the utilization that each VM needs to guarantee that all real-time tasks in that VM will meet their deadlines. Having infinitely short VM periods is clearly not realistic, and in order to provide more useful results we have considered a fixed overhead at the beginning of each execution of a VM. Considering this overhead, a set of real-time tasks, the speed of each processor core, and a certain processor utilization of the VM containing the real-time tasks, we present a simulation study and some performance bounds that make it possible to determine if it is possible to schedule the real-time tasks in the VM, and in that case for which periods of the VM that this is possible.

National Category
Computer Systems
Identifiers
urn:nbn:se:bth-17215 (URN)
Available from: 2018-11-02 Created: 2018-11-02 Last updated: 2018-11-06Bibliographically approved

Open Access in DiVA

fulltext(6263 kB)46 downloads
File information
File name FULLTEXT01.pdfFile size 6263 kBChecksum SHA-512
22c27e5229cc26c0e696f737b9bb5f785c8015c83a177ff66cf12c4e71afec1c66a2044e691ca5bdb96b1913a8345ae895af213d45666cfea944a68d62b9fc8d
Type fulltextMimetype application/pdf

Search in DiVA

By author/editor
Shirinbab, Sogand
By organisation
Department of Computer Science and Engineering
Computer Systems

Search outside of DiVA

GoogleGoogle Scholar
Total: 46 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

isbn
urn-nbn

Altmetric score

isbn
urn-nbn
Total: 164 hits
1011121314151613 of 28
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf